Facial expression recognition based on Haar-like feature detection

In this paper we propose a novel approach for facial feature detection in color image sequences using Haar-like classifiers. The feature extraction is initialized without manual input and has the capability to fulfill the real time requirement. For facial expression recognition, we use geometrical measurement and simple texture analysis in detecting facial regions based on the prior detected facial feature points. For expression classification we used a three layer feed forward artificial neural network. The efficiency of the suggested approach is demonstrated under real world conditions. The text was submitted by the authors in English. Axel Panning was born in Magdeburg, Germany, in 1980. He received his Masters Degree (Dipl.-Ing.) in Computer Science at the University of Magdeburg, Germany, in 2006. He is currently working on a PhD thesis focusing on image processing, tracking, and pattern recognition. Ayoub K. Al-Hamadi was born in Yemen in 1970. He received his Masters Degree (Dipl.-Ing.) in Electrical Engineering and Information Technology in 1997 and his PhD in Technical Computer Science at the Ottovon-Guericke-University of Magdeburg, Germany, in 2001. Since 2002 he has been Assistant Professor and Junior-Research-Group-Leader at the Institute for Electronics, Signal Processing, and Communications at the Otto-von-Guericke-University Magdeburg. His research work concentrates on the field of image processing, tracking analysis, pattern recognition, and artificial neural networks. Dr. Al-Hamadi is the author of more than 60 articles. Robert Niese was born in Halberstadt, Germany, in 1977. He received his Masters Degree (Dipl.-Ing.) with distinction in computer science at the Otto-von-Guericke-University Magdeburg, Germany, in 2004. He gathered broad experience in several international internship investigations on medical image and data analysis, including MRI, CT, and EEG. He is currently working at Magdeburg University on his PhD thesis, which focuses on 3D, image processing, tracking, and pattern recognition. Robert Niese is the author of more than 15 publications. Bernd Michaelis was born in Magdeburg, Germany, in 1947. He received a Masters Degree in Electronic Engineering from the Technische Hochschule Magdeburg in 1971 and his first PhD in 1974. Between 1974 and 1980 he worked at the Technische Hochschule Magdeburg and was granted a second doctoral degree in 1980. In 1993 he became Professor of Technical Computer Science at the Otto-von-Guericke University Magdeburg. His research work concentrates on the field of image processing, artificial neural networks, pattern recognition, processor architectures, and microcomputers. Professor Michaelis is the author of more than 200 papers.     

Multiobject tracking in video using a trisection paradigm

This paper proposes a technique for analyzing the following three problems: (a) segmentation of moving objects, (b) feature extraction, and (c) the solution of the correspondence problem in multiobject tracking in video sequences. In (c), we use a paradigm to solve the correspondence problem and to determine a motion trajectory based on a trisectional structure. The paradigm distinguishes between real-world objects, extracts image features such as motion blobs and color patches, and abstracts objects such as meta objects that denote real-world physical objects. The efficiency of the proposed method for determining the motion trajectories of moving objects will be demonstrated in this paper on the basis of the analysis of real image sequences that are subjected to severe disturbances (e.g., increasing congestion, shadow casting, and lighting transitions). The text was submitted by the authors in English. Ayoub K. Al-Hamadi received his Masters Degree (Dipl.-Ing.) in Electrical Engineering and Information Technology in 1997 and his PhD in Technical Computer Science at the Otto von Guericke University of Magdeburg, Germany, in 2001. Since 2002, he has been Assistant Professor at the Institute for Electronics, Signal Processing, and Communications Technology at the University of Magdeburg. His research work concentrates on the field of image processing, tracking analysis, and pattern recognition. Dr. Al-Hamadi is the author of more than 22 articles. Robert Niese received his Masters Degree (Dipl.-Ing.) in Computer Science at the University of Magdeburg, Germany, in 2004. He is currently working on a PhD thesis focusing on image processing, tracking, and pattern recognition. Bernd Michaelis was born in Magdeburg, Germany, in 1947. He received a Masters Degree in Electronic Engineering from the Technische Hochschule Magdeburg in 1971 and his first PhD in 1974. Between 1974 and 1980 he worked at the Technische Hochschule Magdeburg and was granted a second doctoral degree in 1980. In 1993, he became Professor of Technical Computer Science at the Otto von Guericke University of Magdeburg. His research work concentrates on the field of image processing, artificial neural networks, pattern recognition, processor architectures, and microcomputers. Professor Michaelis is the author of more than 150 papers.     

Consideration of uncertainty in computer vision: Necessity and chance

Observations and decisions in computer vision are inherently uncertain. The rigorous treatment of uncertainty has therefore received a lot of attention, since it not only improves the results compared to ad hoc methods but also makes the results more explainable. In this paper, the usefulness of stochastic approaches will be demonstrated by example with selected problems. These are given in the context of optimal estimation, self-diagnostics, and performance evaluation and cover all steps of the reasoning chain. The removal or interpretation of unexplainable thresholds and tuning parameters will be discussed for typical tasks in feature extraction, object reconstruction, and object classification. The text was submitted by the author in English. Jochen Meidow studied surveying and mapping at the University of Bonn, Germany, and graduated with a diploma in 1996. As research associate at the Institute for Theoretical Geodesy, University of Bonn, he received his PhD degree (Dr.-Ing.) in 2001 for a thesis about aerial image analysis. Between 2001 and 2004 he was a postdoctoral fellow at the Institute for Photogrammetry, University of Bonn, and since 2004 he is with the Research Institute for Optronics and Pattern Recognition (FGAN-FOM) in Ettlingen, Germany. He is a member of the DAGM (German Pattern Recognition Society). His research interests are adjustment theory, statistics, and spatial reasoning.     

Stereophotogrammetric 3D real-time machine vision

Signal processing algorithms often have to be modified significantly for implementation in hardware. Continuous real-time image processing at high speed is a particularly challenging task. In this paper a hardware-software codesign is applied to a stereophotogrammetric system. To calculate the depth map, an optimized algorithm is implemented as a hierarchical-parallel hardware solution. By subdividing distances to objects and selecting them sequentially, we can apply 3D scanning and ranging over large distances. We designed processor-based object clustering and tracking functions. We can detect objects utilizing density distributions of disparities in the depth map (disparity histogram). Motion parameters of detected objects are stabilized by Kalman filters. The text was submitted by the authors in English. Michael Tornow was born in Magdeburg, Germany, in 1977. He received his diploma engineer degree (Dipl.-Ing.) in electrical engineering at the University of Magdeburg, Germany, in 2002. He is currently working on a PhD thesis focusing on hardware adapted image processing and vision based driver assistance. Robert W. Kuhn received his diploma engineer degree (Dipl.-Ing.) in geodesy at the Technical University of Berlin, Germany, in 2000. His current work on a PhD thesis focuses on calibration and image processing. Jens Kaszubiak was born in Blankenburg, Germany, in 1977. He received his diploma engineer degree (Dipl.-Ing.) in electrical engineering at the University of Magdeburg, Germany, in 2002. His current research work focuses on vision-based driver assistance and hardware-software codesign. Bernd Michaelis was born in Magdeburg, Germany, in 1947. He received a Masters Degree in Electronic Engineering from the Technische Hochschule, Magdeburg, in 1971 and his first PhD in 1974. Between 1974 and 1980 he worked at the Technische Hochschule, Magdeburg, and was granted a second doctoral degree in 1980. In 1993 he became Professor of Technical Computer Science at the Otto-von-Guericke University, Magdeburg. His research work concentrates on the field of image processing, artificial neural networks, pattern recognition, processor architectures, and microcomputers. Professor Michaelis is the author of more than 150 papers. Gerald Krell was born in Magdeburg, Germany, in 1964. He earned his diploma in electrical engineering in 1990 and his doctorate in 1995 at Otto-von-Guericke University of Magdeburg. Since then he has been a research assistant. His primary research interest is focused on digital image processing and compression, electronic hardware development, and artificial neural networks.     

ScaleSpaceViz: α-Scale spaces in practice

Kernels of the so-called α-scale space have the undesirable property of having no closed-form representation in the spatial domain, despite their simple closed-form expression in the Fourier domain. This obstructs spatial convolution or recursive implementation. For this reason an approximation of the 2D α-kernel in the spatial domain is presented using the well-known Gaussian kernel and the Poisson kernel. Experiments show good results, with maximum relative errors of less than 2.4%. The approximation has been successfully implemented in a program for visualizing α-scale spaces. Some examples of practical applications with scale space feature points using the proposed approximation are given. The text was submitted by the authors in English. Frans Kanters received his MSc degree in Electrical Engineering in 2002 from the Eindhoven University of Technology in the Netherlands. Currently he is working on his PhD at the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. His PhD work is part of the “Deep Structure, Singularities, and Computer Vision (DSSCV)” project sponsored by the European Union. His research interests include scale space theory, image reconstruction, image processing algorithms, and hardware implementations thereof. Luc Florack received his MSc degree in theoretical physics in 1989 and his PhD degree cum laude in 1993 with a thesis on image structure, both from Utrecht University, the Netherlands. During the period from 1994 to 1995, he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and IN-ESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 to June 2001, he was an assistant research professor at Utrecht University in the Department of Mathematics and Computer Science. Since June 1, 2001, he has been working as an assistant professor and, then, as an associate professor at Eindhoven University of Technology, Department of Biomedical Engineering. His interest includes all multiscale structural aspects of signals, images, and movies and their applications to imaging and vision. Remco Duits received his MSc degree (cum laude) in Mathematics in 2001 from the Eindhoven University of Technology, the Netherlands. Today he is a PhD student at the Department of Biomedical Engineering at the Eindhoven University of Technology on the subject of multiscale perceptual organization. His interest subtends functional analysis, group theory, partial differential equations, multiscale representations and their applications to biomedical imaging and vision, perceptual grouping. Currently, he is finishing his thesis “Perceptual Organization in Image Analysis (A Mathematical Approach Based on Scale, Orientation and Curvature).” During his PhD work, several of his submissions at conferences were chosen as selected or best papers—in particular, at the PRIA 2004 conference on pattern recognition and image analysis in St. Petersburg, where he received a best paper award (second place) for his work on invertible orientation scores. Bram Platel received his Masters Degree cum laude in biomedical engineering from the Eindhoven University of Technology in 2002. His research interests include image matching, scale space theory, catastrophe theory, and image-describing graph constructions. Currently he is working on his PhD in the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. Bart M. ter Haar Romany is full professor in Biomedical Image Analysis at the Department of Biomedical Engineering at Eindhoven University of Technology. He has been in this position since 2001. He received a MSc in Applied Physics from Delft University of Technology in 1978, and a PhD on neuromuscular nonlinearities from Utrecht University in 1983. After being the principal physicist of the Utrecht University Hospital Radiology Department, in 1989 he joined the department of Medical Imaging at Utrecht University as an associate professor. His interests are mathematical aspects of visual perception, in particular linear and non-linear scale-space theory, computer vision applications, and all aspects of medical imaging. He is author of numerous papers and book chapters on these issues; he edited a book on non-linear diffusion theory and is author of an interactive tutorial book on scale-space theory in computer vision. He has initiated a number of international collaborations on these subjects. He is an active teacher in international courses, a senior member of IEEE, and IEEE Chapter Tutorial Speaker. He is chairman of the Dutch Biophysical Society.     

Anisotropic Curvature Motion for Structure Enhancing Smoothing of 3D MR Angiography Data

We propose a novel concept of shape prior for the processing of tubular structures in 3D images. It is based on the notion of an anisotropic area energy and the corresponding geometric gradient flow. The anisotropic area functional incorporates a locally adapted template as a shape prior for tubular vessel structures consisting of elongated, ellipsoidal shape models. The gradient flow for this functional leads to an anisotropic curvature motion model, where the evolution is driven locally in direction of the considered template. The problem is formulated in a level set framework, and a stable and robust method for the identification of the local prior is presented. The resulting algorithm is able to smooth the vessels, pushing solution toward elongated cylinders with round cross sections, while bridging gaps in the underlying raw data. The implementation includes a finite-element scheme for numerical accuracy and a narrow band strategy for computational efficiency. Oliver Nemitz received his Diploma in mathematics from the university of Duisburg, Germany in 2003. Then he started to work on his Ph.D. thesis in Duisburg. Since 2005 he is continuing the work on his Ph.D. project at the Institute for Numerical Simulation at Bonn University. His Ph.D. subject is fast algorithms for image manipulation in 3d, using PDE’s, variational methods, and level set methods. Martin Rumpf received his Ph.D. in mathematics from Bonn University in 1992. He held a postdoctoral research position at Freiburg University. Between 1996 and 2001, he was an associate professor at Bonn University and from 2001 until 2004 full professor at Duisburg University. Since 2004 he is now full professor for numerical mathematics and scientific computing at Bonn University. His research interests are in numerical methods for nonlinear partial differential equations, geometric evolution problems, calculus of variations, adaptive finite element methods, image and surface processing. Tolga Tasdizen received his B.S. degree in Electrical Engineering from Bogazici University, Istanbul in 1995. He received the M.S. and Ph.D. degrees in Engineering from Brown University in 1997 and 2001. From 2001 to 2004 he was a postdoctoral research associate with the Scientific Computing and Imaging Institute at the University of Utah. Since 2004 he has been with the School of Computing at the University of Utah as a research assistant professor. He also holds an adjunct assistant professor position with the Department of Neurology and the Center for Alzheimer’s Care, Imaging and Research, and a research scientist position with the Scientific Computing and Imaging Institute at the University of Utah. Ross Whitaker received his B.S. degree in Electrical Engineering and Computer Science from Princeton University in 1986, earning Summa Cum Laude. From 1986 to 1988 he worked for the Boston Consulting Group, entering the University of North Carolina at Chapel Hill in 1989. At UNC he received the Alumni Scholarship Award, and completed his Ph.D. in Computer Science in 1994. From 1994–1996 he worked at the European Computer-Industry Research Centre in Munich Germany as a research scientist in the User Interaction and Visualization Group. From 1996–2000 he was an Assistant Professor in the Department of Electrical Engineering at the University of Tennessee. He is now an Associate Professor at the University of Utah in the College of Computing and the Scientific Computing and Imaging Institute.     

Contour tracking on ultrasound sequences of vascular images

Simple vascular measurements on sequences of echographic images can be used to quantify important indexes of cardiovascular risk. The measurement of the intima-media thickness and the characterization of the endothelial function are but two examples. Real-time image processing systems would be helpful to automatically track, locate, and discriminate vascular structures through image sequences. Many algorithms have been developed to accomplish this task and they are generally based on the application of a mathematical operator at the points of a starting contour and on an iterative procedure that brings the starting contour to the final contour. In this paper, the performances of a mathematical operator that exploits both temporal and spatial information are compared to those of an operator that only exploits spatial information. The paper shows that, in general, when tracking contours on image sequences and when two or more gray-level discontinuities are present and close to each other, as in the case of arteries, both operators should be used in sequence. The text was submitted by the authors in English. Marcello Demi was born in Cecina, Italy, in 1956. He graduated in Electronic Engineering from the University of Florence, Italy in 1985. He is currently head of the Computer Vision Group at the CNR Institute of Clinical Physiology in Pisa and he teaches a course on Medical Image Processing at the faculty of Applied Physics, University of Pisa. His research interests are image processing systems and filtering schemes inspired by the early stages of biological vision systems. He has 80 scientific publications and his objective is the development of common projects with people who work in the area of biological vision for the purpose of both understanding biological vision and developing image processing systems. Elisabetta Bianchini was born in Lucca, Italy, in 1975. She received the degree in Electronic Engineering from the University of Pisa, Italy, in 2004. Since 2004 she is junior research at CNR, the Italian National Research Council, at the DSP lab in IFC (Institute of Clinical Physiology). Her field of interest is image processing and in particular development of methods for the assessment of indices of cardiovascular risk from ultrasound images. She is author or co-author of 14 scientific publications in international journals and conference proceedings. Francesco Faita was born in 1973 in La Spezia (Italy). In 2001 he graduated from Università degli Studi di Pisa obtaining the degree of Electronic Engineer. Since 2001, he has been working as a research fellow at the Institute of Clinical Physiology of the Italian National Research Council. His main research interests lie in Computer Vision, in particular in the field of ultrasound image motion estimation. A major focus of his research in the last years has been development of clinically applicable automated techniques for cardiovascular analysis and prediction of disease progression. He is author or co-author of 58 scientific publications in international journals and conference proceedings. Viencenzo Gemignani was born in 1969, in Viareggio (Italy). In 1995, he graduated in Electronic Engineering from the University of Pisa. Since 1996, he has been working at the Institute of Clinical Physiology of the Italian National Research Council. His main research interests are in diagnostic ultrasound, realtime image analysis and non-invasive patient monitoring systems. He teaches a course on DSP processors at the Faculty of Engineering, University of Pisa. He is author or coauthor of 40 scientific publications in international journals and conference proceedings and is co-inventor of 4 patents in the field of ultrasonic image processing.     

Pose Estimation in Conformal Geometric Algebra Part I: The Stratification of Mathematical Spaces

2D-3D pose estimation means to estimate the relative position and orientation of a 3D object with respect to a reference camera system. This work has its main focus on the theoretical foundations of the 2D-3D pose estimation problem: We discuss the involved mathematical spaces and their interaction within higher order entities. To cope with the pose problem (how to compare 2D projective image features with 3D Euclidean object features), the principle we propose is to reconstruct image features (e.g. points or lines) to one dimensional higher entities (e.g. 3D projection rays or 3D reconstructed planes) and express constraints in the 3D space. It turns out that the stratification hierarchy [11] introduced by Faugeras is involved in the scenario. But since the stratification hierarchy is based on pure point concepts a new algebraic embedding is required when dealing with higher order entities. The conformal geometric algebra (CGA) [24] is well suited to solve this problem, since it subsumes the involved mathematical spaces. Operators are defined to switch entities between the algebras of the conformal space and its Euclidean and projective subspaces. This leads to another interpretation of the stratification hierarchy, which is not restricted to be based solely on point concepts. This work summarizes the theoretical foundations needed to deal with the pose problem. Therefore it contains mainly basics of Euclidean, projective and conformal geometry. Since especially conformal geometry is not well known in computer science, we recapitulate the mathematical concepts in some detail. We believe that this geometric model is useful also for many other computer vision tasks and has been ignored so far. Applications of these foundations are presented in Part II [36].Bodo Rosenhahn gained his diploma degree in Computer Science in 1999. Since then he has been pursuing his Ph.D. at the Cognitive Systems Group, Institute of Computer Science, Christian-Albrechts University Kiel, Germany. He is working on geometric applications of Clifford algebras in computer vision.Prof. Dr. Gerald Sommer received a diploma degree in physics from the Friedrich-Schiller-Universität Jena, Germany, in 1969, a Ph.D. degree in physics from the same university in 1975, and a habilitation degree in engineering from the Technical University Ilmenau, Germany, in 1988. Since 1993 he is leading the research group Cognitive Systems at the Christian-Albrechts-Universität Kiel, Germany. Currently he is also the scientific coordinator of the VISATEC project.     

Fast training for object recognition with structure-from-motion

In this paper we present a system for statistical object classification and localization that applies a simplified image acquisition process for the learning phase. Instead of using complex setups to take training images in known poses, which is very time-consuming and not possible for some objects, we use a handheld camera. The pose parameters of objects in all training frames that are necessary for creating the object models are determined using a structure-from-motion algorithm. The local feature vectors we use are derived from wavelet multiresolution analysis. We model the object area as a function of 3D transformations and introduce a background model. Experiments made on a real data set taken with a handheld camera with more than 2500 images show that it is possible to obtain good classification and localization rates using this fast image acquisition method. The text was submitted by the authors in English. Marcin Grzegorzek, born in 1977, obtained his Master’s Degree in Engineering from the Silesian University of Technology Gliwice (Poland) in 2002. Since December 2002 he has been a PhD candidate and member of the research staff of the Chair for Pattern Recognition at the University of Erlangen-Nuremberg, Germany. His fields are 3D object recognition, statistical modeling, and computer vision. He is an author or coauthor of seven publications. Michael Reinhold, born in 1969, obtained his degree in Electrical Engineering from RWTH Aachen University, Germany, in 1998. Later, he received a Doctor of Engineering from the University of Erlangen-Nuremberg, Germany, in 2003. His research interests are statistical modeling, object recognition, and computer vision. He is currently a development engineer at Rohde & Schwarz in Munich, Germany, where he works in the Center of Competence for Digital Signal Processing. He is an author or coauthor of 11 publications. Ingo Scholz, born in 1975, graduated in computer science at the University of Erlangen-Nuremberg, Germany, in 2000 with a degree in Engineering. Since 2001 he has been working as a research staff member at the Institute for Pattern Recognition of the University of Erlangen-Nuremberg. His main research focuses on the reconstruction of light field models, camera calibration techniques, and structure from motion. He is an author or coauthor of ten publications and member of the German Gesellschaft für Informatik (GI). Heinrich Niemann obtained his Electrical Engineering degree and Doctor of Engineering degree from Hannover Technical University, Germany. He worked with the Fraunhofer Institut für Informationsverarbeitung in Technik und Biologie, Karlsruhe, and with the Fachhochschule Giessen in the Department of Electrical Engineering. Since 1975 he has been a professor of computer science at the University of Erlangen-Nuremberg, where he was dean of the engineering faculty of the university from 1979 to 1981. From 1988 to 2000, he was head of the Knowledge Processing research group at the Bavarian Research Institute for Knowledge-Based Systems (FORWISS). Since 1998, he has been a spokesman for a “special research area” with the name of “Model-Based Analysis and Visualization of Complex Scenes and Sensor Data” funded by the German Research Foundation. His fields of research are speech and image understanding and the application of artificial intelligence techniques in these areas. He is on the editorial board of Signal Processing, Pattern Recognition Letters, Pattern Recognition and Image Analysis, and the Journal of Computing and Information Technology. He is an author or coauthor of seven books and about 400 journal and conference contributions, as well as editor or coeditor of 24 proceedings volumes and special issues. He is a member of DAGM, ISCA, EURASIP, GI, IEEE, and VDE and an IAPR fellow.     

Object extraction from an image of wear particles on a complex background

The object extraction of a debris image is an important basic task in identifying wear particles in ferrographic analysis. However, there is some difficulty in object extraction because of noise jamming in the original debris image. In the present study, two methods of image enhancement—weighted mean filtering and adaptive median filtering—were applied in order to improve the image quality. Then, the adaptive thresholding selection method was used, which is based on an improved debris image. Finally, the effective segmentation of the debris image and the automatic extraction of debris objects were realized. At the same time, targetting the characteristics of low proportion of an object in the total image, a novel method of adaptive thresholding selection was put forward, which is based on the Ostu thresholding method. The segmentation results along with the debris image prove that the current method can give more precise and accurate segmentation of objects than the classical methods. The results also showed that methods in the present paper were concise and effective, which provides an important basis for the further study of debris recognition, fault diagnosis, and condition monitoring of machines. The text was submitted by the authors in English. Xianguo Hu (born 1963), PhD, is a professor at the School of Mechanical and Automotive Engineering at the Hefei University of Technology, China. He received his BS and MS in Powder Metallurgy Material and Mechanics (Tribology) from the Hefei University of Technology in 1985 and 1988, respectively. His PhD degree was awarded at Szent Istvan University, Hungary, in 2002. As a visiting scientist, he conducted research at the Technical University of Budapest, Hungary, and the Technical University of Berlin, Germany, from 1994 to 1997. His research areas include wear debris analysis, optimal tribological design, friction and wear mechanisms, etc. He is the author or coauthor of more than 100 published technical papers. Peng Huang (born 1981) is an MS student at the School of Mechanical and Automotive Engineering of Hefei University of Technology, China. His main focus is on wear debris analysis. Shousen Zheng (born 1963) is an associate professor at the School of Engineering, SunYat-Sen University, China. He received his BS, MS, and PhD in Mechanical Engineering from Hefei University of Technology in 1985, 1988, and 2001, respectively. From 1988 to 2004, he was employed at the Department of Mechanical Engineering at the Hefei University of Technology. In 2005, he moved to the current university. His research interests include computer language, auto CAD/CAM, wear debris analysis, etc. He is the author or coauthor of more than 40 published technical papers.     

The Theory and Use of the Quaternion Wavelet Transform

This paper presents the theory and practicalities of the quaternion wavelet transform (QWT). The major contribution of this work is that it generalizes the real and complex wavelet transforms and derives a quaternionic wavelet pyramid for multi-resolution analysis using the quaternionic phase concept. As a illustration we present an application of the discrete QWT for optical flow estimation. For the estimation of motion through different resolution levels we use a similarity distance evaluated by means of the quaternionic phase concept and a confidence mask. We show that this linear approach is amenable to be extended to a kind of quadratic interpolation. Eduardo Jose Bayro-Corrochano gained his Ph.D. in Cognitive Computer Science in 1993 from the University of Wales at Cardiff. From 1995 to 1999 he has been Researcher and Lecturer at the Institute for Computer Science, Christian Albrechts University, Kiel, Germany, working on applications of geometric Clifford algebra to cognitive systems. His current research interest focuses on geometric methods for artificial perception and action systems. It includes geometric neural networks, visually guidevsd robotics, color image processing, Lie bivector algebras for early vision and robot maneuvering. He is editor and author of the following books: Geometric Computing for Perception Action Systems, E. Bayro-Corrochano, Springer Verlag, 2001; Geometric Algebra with Applications in Science and Engineering, E. Bayro-Corrochano and G. Sobczyk (Eds.), Birkahauser 2001; Handbook of Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics, E. Bayro-Corrochano, Springer Verlag, 2005.     

Invertible orientation scores as an application of generalized wavelet theory

Inspired by the visual system of many mammals, we consider the construction of—and reconstruction from—an orientation score of an image, via a wavelet transform corresponding to the left-regular representation of the Euclidean motion group in (ℝ²) and oriented wavelet φ ∈ (ℝ²). Because this representation is reducible, the general wavelet reconstruction theorem does not apply. By means of reproducing kernel theory, we formulate a new and more general wavelet theory, which is applied to our specific case. As a result we can quantify the well-posedness of the reconstruction given the wavelet φ and deal with the question of which oriented wavelet φ is practically desirable in the sense that it both allows a stable reconstruction and a proper detection of local elongated structures. This enables image enhancement by means of left-invariant operators on orientation scores. The text was submitted by the authors in English. Remco Duits received his M.Sc. degree (cum laude) in Mathematics in 2001 from Eindhoven University of Technology, The Netherlands. He received his PhD degree (cum laude) at the Department of Biomedical Engineering at Eindhoven University of Technology on the subject of multiscale perceptual organization. His interests include functional analysis, group theory, partial differential equations, multiscale representations and their applications to biomedical imaging and vision, and perceptual grouping. His PhD thesis is titled Perceptual Organization in Image Analysis (A Mathematical Approach Based on Scale, Orientation and Curvature). Several of his submissions at conferences have been selected/best papers, in particular, at the PRIA 2004 conference on pattern recognition and image analysis in St. Petersburg, he received a best paper award (second prize) for his work on invertible orientation scores. Currently, he is working at Eindhoven University of Technology as an assistant professor at both the Department of Applied Mathematics and Computer Science and the Department of Biomedical Engineering. Maurice Duits received his MSc degree (cum laude) in Mathematics in 2004 from Eindhoven University of Technology, The Netherlands on the subject of reproducing kernels in frame and wavelet transforms. Now he is a PhD student at the Department of Mathematics at Katholieke Universiteit Leuven on the subject of random matrices. His interests include Riemann-Hilbert problems, random matrices, orthogonal polynomials and Toeplitz matrices. Markus van Almsick earned a master degree in physics at the Technical University of Munich in 1990. From 1988 until 1992, he worked for the University of Illinois at Urbana-Champaign as a research and teaching associate. He taught undergraduate chemistry as well as graduate courses in advanced quantum mechanics, for which he developed Mathematica course material. His research interest has been quantum logic and quantization procedures of space-time. Since 1990, he has been a freelance applications consultant for Wolfram Research, Inc., USA, and Wolfram Research Europe Ltd., United Kingdom, promoting Mathematica at universities and research institutions in the U.S., Europe, and Israel, as well as developing Mathematica packages and application material. In 1996, Mr. van Almsick joined the Max Planck Insitut fur Biophysik in Frankfurt am Main, Germany, where he addressed problems in nonequilibrium thermodynamics until the theoretical department closed in 1997. Then, until 2001 he worked in collaboration with QT Software GmbH, Munich, as a full-time Mathematica consultant on a wide variety of assignments, e.g., designing the geometry of slides for playgrounds, modeling human interaction via graph theory (“social networks”), lossless image compression, vibration control in electric engines, and the isomer enumeration of libraries containing chemical diamutamers. Since 2001, he has been a part-time employee of the Technische Universiteit Eindhoven, where he develops Math VisionTools, a biomedical image analysis toolkit based on Mathematica. Bart M. ter Haar Romany is full professor in Biomedical Image Analysis at the Department of Biomedical Engineering at Eindhoven University of Technology. He has been in this position since 2001. He received a MSc in Applied Physics from Delft University of Technology in 1978, and a PhD on neuromuscular nonlinearities from Utrecht University in 1983. After being the principal physicist of the Utrecht University Hospital Radiology Department, in 1989 he joined the department of Medical Imaging at Utrecht University as an associate professor. His interests are mathematical aspects of visual perception, in particular linear and non-linear scale-space theory, computer vision applications, and all aspects of medical imaging. He is author of numerous papers and book chapters on these issues; he edited a book on non-linear diffusion theory and is author of an interactive tutorial book on scale-space theory in computer vision. He has initiated a number of international collaborations on these subjects. He is an active teacher in international courses, a senior member of IEEE, and IEEE Chapter Tutorial Speaker. He is chairman of the Dutch Biophysical Society. An erratum to this article is available at .     

Enhanced motion parameters estimation for an active vision system

In this paper, we present an enhanced approach for estimating 3D motion parameters from 2D motion vector fields. The proposed method achieves valuable reduction in computational time and shows high robustness against noise in the input data. The output of the algorithm is part in a multiobject segmentation approach implemented in an active vision system. Hence, the improvement in the motion parameters estimation process leads to speed-up in the overall segmentation process. The text was submitted by the authors in English. Mohamed Shafik obtained his B.Sc. in mechanical engineering at the University of Banha. In 2004 he earned an Information Technology Diploma in Mechatronics from the Information Technology Institute (ITI). In 2006 he obtained his M.Eng. in applied mechatronics at the University of Paderborn. Since 2006, he is a PhD student and a scientific assistant in the GET Lab. His research interests focus on robotic vision, neural networks, and mechatronic systems. Baerbel Mertsching studied electrical engineering and obtained her PhD at the University of Paderborn. Between 1994 and 2003, she was professor of computer science at the University of Hamburg. In 2003 she returned to the University of Paderborn where she is now professor of electrical engineering and director of the GET Lab. Her research interests focus on cognitive systems engineering, especially active vision systems, and microelectronics for image and speech processing. She has been a member of a variety of scientific councils and editorial boards and is author of more than 120 scientific publications.     

Pose Estimation in Conformal Geometric Algebra Part II: Real-Time Pose Estimation Using Extended Feature Concepts

Part II uses the foundations of Part I [35] to define constraint equations for 2D-3D pose estimation of different corresponding entities. Most articles on pose estimation concentrate on specific types of correspondences, mostly between points, and only rarely use line correspondences. The first aim of this part is to extend pose estimation scenarios to correspondences of an extended set of geometric entities. In this context we are interested to relate the following (2D) image and (3D) model types: 2D point/3D point, 2D line/3D point, 2D line/3D line, 2D conic/3D circle, 2D conic/3D sphere. Furthermore, to handle articulated objects, we describe kinematic chains in this context in a similar manner. We ensure that all constraint equations end up in a distance measure in the Euclidean space, which is well posed in the context of noisy data. We also discuss the numerical estimation of the pose. We propose to use linearized twist transformations which result in well conditioned and fast solvable systems of equations. The key idea is not to search for the representation of the Lie group, describing the rigid body motion, but for the representation of their generating Lie algebra. This leads to real-time capable algorithms.Bodo Rosenhahn gained his diploma degree in Computer Science in 1999. Since then he has been pursuing his Ph.D. at the Cognitive Systems Group, Institute of Computer Science, Christian-Albrechts University Kiel, Germany. He is working on geometric applications of Clifford algebras in computer vision.Prof. Dr. Gerald Sommer received a diploma degree in physics from the Friedrich-Schiller-Universität Jena, Germany, in 1969, a Ph.D. degree in physics from the same university in 1975, and a habilitation degree in engineering from the Technical University Ilmenau, Germany, in 1988. Since 1993 he is leading the research group Cognitive Systems at the Christian-Albrechts-Universität Kiel, Germany. Currently he is also the scientific coordinator of the VISATEC project.     

Hiding a halftone secret image in two camouflaged halftone images

In this paper, we shall propose a method to hide a halftone secret image into two other camouflaged halftone images. In our method, we adjust the gray-level image pixel value to fit the pixel values of the secret image and two camouflaged images. Then, we use the halftone technique to transform the secret image into a secret halftone image. After that, we make two camouflaged halftone images at the same time out of the two camouflaged images and the secret halftone image. After overlaying the two camouflaged halftone images, the secret halftone image can be revealed by using our eyes. The experimental results included in this paper show that our method is very practicable. The text was submitted by the authors in English. Wei-Liang Tai received his BS degree in Computer Science in 2002 from Tamkang University, Tamsui, Taiwan, and his MS degree in Computer Science and Information Engineering in 2004 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding, digital watermarking, and image compression. Chi-Shiang Chan received his BS degree in Computer Science in 1999 from National Cheng Chi University, Taipei, Taiwan, and his MS degree in Computer Science and Information Engineering in 2001 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in Applied Mathematics in 1977 and his MS degree in Computer and Decision Sciences in 1979, both from National Tsing Hua University, Hsinchu, Taiwan. He received his PhD in Computer Engineering in 1982 from National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a fellow of the IEEE, a fellow of the IEE, and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression.     

Segmentation of convex cells with partially undefined edges

This paper presents an algorithm for segmentation of convex cells with partially undefined edges based on application of a marker-controlled watershed transform to a combination of a source grayscale image and the result of a “geodesic distance” morphological operation, applied to the result of binarization of a source image. The presented approach is used in computer image processing systems for analysis of several industrial materials. The text was submitted by the authors in English. Ilia V. Safonov received his MS degree in automatic and electronic engineering from Moscow Engineering Physics Institute/University (MEPhI), Russia in 1994 and his PhD degree in computer science from MEPhI in 1997. Since 1998 he is an associate professor of faculty of Cybernetics of MEPhI while conducting researches in image segmentation, features extraction and pattern recognition problems. Since 2004, Dr. I. Safonov has joint Image Enhancement Technology Group, Printing Technology Lab, Samsung Research Center, Moscow, Russia where he is engaged in photo, video, and document image enhancement projects. Konstantin A. Kryzhanovsky received the MS degree in cybernetics from Moscow Engineering Physics Institute/University (MEPhI), Russia in 2000. Since 2006 he is an assistant professor of faculty of Cybernetics of MEPhI. He is presently working towards his Ph.D. degree. His current research interests include image processing and pattern recognition. Gennady N. Mavrin received his MS degree in automatic and electronic engineering from Moscow Engineering Physics Institute/University (MEPhI), Russia in 1998. Since 2002 he is an assistant professor of faculty of Cybernetics of MEPhI. He is currently pursuing the PhD degree. His research interests include image segmentation and feature extraction problems.     

DocXS distributed operator construction and execution system

In the field of computer vision and pattern recognition, data processing and data analysis tasks are often implemented as a consecutive or parallel application of more-or-less complex operations. In the following we will present DocXS, a computing environment for the design and the distributed and parallel execution of such tasks. Algorithms can be programmed using an Eclipse-based user interface, and the resulting Matlab and Java operators can be visually connected to graphs representing complex data processing workflows. DocXS is platform independent due to its implementation in Java, is freely available for noncommercial research, and can be installed on standard office computers. One advantage of DocXS is that it automatically takes care about the task execution and does not require its users to care about code distribution or parallelization. Experiments with DocXS show that it scales very well with only a small overhead. The text was submitted by the authors in English. Steffen Wachenfeld received B.Sc. and M.Sc. (honors) degrees in Information Systems in 2003 and 2005 from the University of Muenster, Germany, and an M.Sc. (honors) degree in Computer Science in 2003 from the University of Muenster. He is currently a research fellow and PhD student in the Computer Science at the Dept. of Computer Science, University of Muenster. His research interests include low resolution text recognition, computer vision on mobile devices, and systems/system architectures for computer vision and image analysis. He is author or coauthor of more than ten scientific papers and a member of IAPR. Tobias Lohe, M.Sc. degree in Computer Science in 2007 from the University of Muenster, Germany, is currently a research associate and PhD student in Computer Science at the Institute for Robotics and Cognitive Systems, University of Luebeck, Germany. His research interests include medical imaging, signal processing, and robotics for minimally invasive surgery. Michael Fieseler is currently a student of Computer Science at the University of Muenster, Germany. He has participated in research in the field of computer vision and medical imaging. Currently he is working on his Master thesis on depth-based image rendering (DBIR). Xiaoyi Jiang studied Computer Science at Peking University, China, and received his PhD and Venia Docendi (Habilitation) degree in Computer Science from the University of Bern, Switzerland. In 2002 he became an associate professor at the Technical University of Berlin, Germany. Since October 2002 he has been a full professor at the University of Münster, Germany. He has coauthored and coedited two books published by Springer and has served as the co-guest-editor of two special issues in international journals. Currently, he is the Coeditor-in-Chief of the International Journal of Pattern Recognition and Artificial Intelligence. In addition he also serves on the editorial advisory board of the International Journal of Neural Systems and the editorial board of IEEE Transactions on Systems, Man, and Cybernetics—Part B, the International Journal of Image and Graphics, Electronic Letters on Computer Vision and Image Analysis, and Pattern Recognition. His research interests include medical image analysis, vision-based man-machine interface, 3D image analysis, structural pattern recognition, and mobile multimedia. He is a member of IEEE and a Fellow of IAPR.     

Fully automated segmentation of multiple sclerosis lesions in multispectral MRI

This paper addresses segmentation of multiple sclerosis lesions in multispectral 3-D brain MRI data. For this purpose, we propose a novel fully automated segmentation framework based on probabilistic boosting trees, which is a recently introduced strategy for supervised learning. By using the context of a voxel to be classified and its transformation to an overcomplete set of Haar-like features, it is possible to capture class specific characteristics despite the well-known drawbacks of MR imaging. By successively selecting and combining the most discriminative features during ensemble boosting within a tree structure, the overall procedure is able to learn a discriminative model for voxel classification in terms of posterior probabilities. The final segmentation is obtained after refining the preliminary result by stochastic relaxation and a standard level set approach. A quantitative evaluation within a leave-one-out validation shows the applicability of the proposed method. The text was submitted by the authors in English. Michael Wels was born in 1979 and graduated with a degree in computer science from the University of Wuerzburg in 2006. Currently, he is a member of the research staff at the University of Erlangen-Nuremberg’s Institute of Pattern Recognition working towards his Ph.D. His research interests are medical imaging in general and the application of machine learning techniques to medical image segmentation. Martin Huber studied at the University of Karlsruhe and received his Ph.D. degree in computer science in 1999. Since 1996, he has been with Siemens Corporate Technology and Siemens Medical Solutions. He currently is technical coordinator of the EU funded project Health-e-Child with research interests in medical imaging and semantic data integration. Joachim Hornegger graduated with a degree in computer science (1992) and received his Ph.D. in applied computer science (1996) at the University of Erlangen-Nuremberg (Germany). His Ph.D. thesis was on statistical learning, recognition, and pose estimation of 3-D objects. Joachim was a visiting scholar and lecturer at Stanford University (Stanford, CA, USA) during the 1997–1998 academic year, and, in 2007–2008, he was a visiting professor at Stanford’s Radiological Science Laboratory. In 1998, he joined Siemens Medical Solutions Inc., where he was working on 3D angiography. In parallel with his responsibilities in industry, he was a lecturer at the Universities of Erlangen (1998–1999), Eichstaett-Ingolstadt (2000), and Mannheim (2000–2003). In 2003, Joachim became Professor of Medical Imaging Processing at the University of Erlangen-Nuremberg, and, since 2005, he has been a chaired professor heading the Institute of Pattern Recognition. His main research topics are currently pattern recognition methods in medicine and sports.     

On minimizing errors in 3D reconstruction for stereo camera systems

Active reconstruction of 3D surfaces deals with the control of camer a viewpoints to minimize error and uncertainty in the reconstructed shape of an object. In this paper we develop a mathematical relationship between the setup and focal lengths of a stereo camera system and the corresponding error in 3D reconstruction of a given surface. We explicitly model the noise in the image plane, which can be interpreted as pixel noise or as uncertainty in the localization of corresponding point features. The results can be used to plan sensor positioning, e.g., using information theoretic concepts for optimal sensor data selection. The text was submitted by the authors in English. Stefan Wenhardt, born in 1978, graduated in mathematics at the University of Applied Sciences, Regensburg, Germany, in 2002, with a degree of Dipl.-Math. (FH). Since June 2002, he has been a research staff member at the Chair for Pattern Recognition at the Friedrich-Alexander-University of Erlangen-Nuremberg, Germany. The topics of his research are 3D reconstruction and active vision systems. He is author or coauthor of four publications. Joachim Denzler, born April 16, 1967, received a degree of Diplom-Informatiker, Dr.-Ing. and Habilitation from the University of Erlangen in 1992, 1997, and 2003, respectively. Currently, he holds a position of a full professor for computer science and is head of the computer vision group, Faculty of Mathematics and Informatics, University of Jena. His research interests comprise active computer vision, object recognition and tracking, 3D reconstruction, and plenoptic modeling, as well as computer vision for autonomous systems. He is author and coauthor of over 80 journal papers and technical articles. He is member of the IEEE computer society, DAGM, and GI. For his work on object tracking, plenoptic modeling, and active object recognition and state estimation, he was awarded with the DAGM best paper awards in 1996, 1999, and 2001, respectively. Heinrich Niemann obtained the degree of Dipl.-Ing. in Electrical Engineering and Dr.-Ing. from Technical University Hannover, Germany. He worked at the Fraunhofer Institut fur Informationsverarbeitung in Technik und Biologie, Karlsruhe, and at Fachhochschule Giessen in the department of Electrical Engineering. Since 1975 he has been Professor of Computer Science at the University of Erlangen-Nurnberg, where he was dean of the engineering faculty of the university from 1979–1981. From 1988–2000 he was head of the research group Knowledge Processing at the Bavarian Research Institute for Knowledge-based Systems (FORWISS). Since 1998 he has been the speaker of a special research area entitled Model-based Analysis and Visualization of Complex Scenes and Sensor Data, which is funded by the German Research Foundation (DFG). His fields of research are speech and image understanding and the application of artificial intelligence techniques in these fields. He is on the editorial boards of Signal Processing, Pattern Recognition Letters, Pattern Recognition and Image Analysis, and Journal of Computing and Information Technology. He is the author or coauthor of 7 books and about 400 journal and conference contributions, as well as editor or coeditor of 24 volumes of proceedings and special issues. He is a member of DAGM, ISCA, EURASIP, GI, and IEEE, and a Fellow of IAPR.     

Foam-rubber sample volume measuring

For industrial quality control of foam-rubber material, it is required to measure volume of the sample. A new approach is proposed to measure sample volume by images of sample faces. Faces images are got via flatbed scanner. The faces images are processed and the sample is approximated by hexahedron. Then the sample volume is calculated analytically. Also we proposed an iterative approach based on splitting geometrical model of the sample into several smaller hexahedrons. The test results have shown that results of volume measurements obtained by proposed approach coincide well with ones obtained by the standard method. However, repeatability and reproducibility of measurements is better for proposed algorithm, and it is faster. The article is published in the original. Ilia V. Safonov. Received his MS degree in automatic and electronic engineering from Moscow Engineering Physics Institute/University (MEPhI), Russia in 1994 and his PhD degree in computer science from MEPhI in 1997. Since 1998 he is an associate professor of faculty of Cybernetics of MEPhI while conducting researches in image segmentation, features extraction and pattern recognition problems. Since 2004, Dr. Safonov has joint Image Enhancement Technology Group, Printing Technology Lab, Samsung Research Center, Moscow, Russia where he is engaged in photo, video and document image enhancement projects. Sergei Yu. Yakovlev. Received the MS degree in cybernetics from Moscow Engineering Physics Institute/University (MEPhI), Russia in 2005. He is presently working towards his PhD degree. His current research interests include image processing, pattern recognition and 3D shape reconstruction.