A Survey of Inverse Rendering Problems

Inverse rendering problems usually represent extremely complex and costly processes, but their importance in many research areas is well known. In particular, they are of extreme importance in lighting engineering, where potentially costly mistakes usually make it unfeasible to test design decisions on a model. In this survey we present the main ideas behind these kinds of problems, characterize them, and summarize work developed in the area, revealing problems that remain unsolved and possible areas of further research. ACM CSS: I.3.6 Computer Graphics Methodology and Techniques I.3.7 Computer Graphics—Three‐Dimensional Graphics and Realism I.4.1 Image Processing and Computer Vision Digitization and Image Capture I.4.7 Image Processing and Computer Vision Feature Measurement I.4.8 Image Processing and Computer Vision Scene Analysis     

The Shadow Function for Rough Surfaces

For many remote sensing applications it is beneficial to know how the amount of shadows on a surface depends on illumination. Many natural surfaces (planetary surfaces being an example) may be successfully described by a fractal model. While the fractal shadowing function can be easily calculated experimentally, to date no rigorous analytical model of self-shadowing on a fractal surface exists. In this paper we offer an integral form of the shadowing function for fractal surfaces with different fractal and roughness parameters. The analysis is based on working out the covariance matrix for an arbitrarily long sequence of heights in a fractal profile.Svetlana Barsky received her BSc degree in Mathematics and Applied Mathematics from Novosibirsk State University, Russia, in 1992, and her MSc and PhD degrees from the University of Surrey, UK, in 1999 and 2003 respectively. Since then she has been working as a research fellow at the Centre for Vision, Speech and Signal Processing of the School of Electronics and Physical Sciecnes of Surrey University.Maria Petrou studied Physics at the Aristotle University of Thessaloniki, Greece, Applied Mathematics in Cambridge and she did her PhD in the Institute of Astronomy in Cambridge, UK. She has been working on image processing and computer vision since 1986. She has been the Professor of Image Analysis since 1998 and leads a group of 20 researchers on this topic in Surrey University. She has published more than 250 scientific papers, on Astronomy, Remote Sensing, Computer Vision, Machine Learning, Colour analysis, Industrial Inspection, Medical Signal and Image Processing. She has co-authored a book “Image Processing: the fundamentals” published by John Wiley in 1999 and reprinted in 2000 and 2003, and numerous book chapters. She is a Fellow of the Royal Academy of Engineering, Fellow of IEE and Fellow of IAPR. She has served as the Chairman of the Technical Committee for Remote Sensing of IAPR, the Chairman of the British Machine Vision Association (BMVA), as an Associate Editor of IEEE Transactions on Image Processing, as the Newsletter Editor of IAPR and is currently the treasurer of IAPR and an Honorary Editor of IEE Electronics Letters. A full list of publications and other details can be found in     

A review of deformable surfaces: topology, geometry and deformation

Deformable models have raised much interest and found various applications in the fields of computer vision and medical imaging. They provide an extensible framework to reconstruct shapes. Deformable surfaces, in particular, are used to represent 3D objects. They have been used for pattern recognition [Computer Vision and Image Understanding 69(2) (1998) 201; IEEE Transactions on Pattern Analysis and Machine Intelligence 19(10) (1997) 1115], computer animation [ACM Computer Graphics (SIGGRAPH'87) 21(4) (1987) 205], geometric modelling [61][Computer Aided Design (CAD) 24(4) (1992) 178], simulation [Visual Computer 16(8) (2000) 437], boundary tracking [ACM Computer Graphics (SIGGRAPH'94) (1994) 185], image segmentation [Computer Integrated Surgery, Technology and Clinical Applications (1996) 59; IEEE Transactions on Medical Imaging 14 (1995) 442; Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine (CVRMed-MRCAS'97) 1205 (1997) 13; Medical Image Computing and Computer-Assisted Intervention (MICCAI'99) 1679 (1999) 176; Medical Image Analysis 1(1) (1996) 19], etc. In this paper we propose a survey on deformable surfaces. Many surface representations have been proposed to meet different 3D reconstruction problem requirements. We classify the main representations proposed in the literature and we study the influence of the representation on the model evolution behavior, revealing some similarities between different approaches.     

State of research in automatic as-built modelling

Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.     

Practical Anisotropic Geodesy

The computation of intrinsic, geodesic distances and geodesic paths on surfaces is a fundamental low‐level building block in countless Computer Graphics and Geometry Processing applications. This demand led to the development of numerous algorithms – some for the exact, others for the approximative computation, some focussing on speed, others providing strict guarantees. Most of these methods are designed for computing distances according to the standard Riemannian metric induced by the surface's embedding in Euclidean space. Generalization to other, especially anisotropic, metrics – which more recently gained interest in several application areas – is not rarely hampered by fundamental problems. We explore and discuss possibilities for the generalization and extension of well‐known methods to the anisotropic case, evaluate their relative performance in terms of accuracy and speed, and propose a novel algorithm, the Short‐Term Vector Dijkstra. This algorithm is strikingly simple to implement and proves to provide practical accuracy at a higher speed than generalized previous methods.     

An approach for enhancing fingerprint images using adaptive Gabor filter parameters

This work proposes a technique to enhance fingerprint images through the Gabor filter with adaptive parameters. Firstly, the average ridge and valley of each region as well as their direction are evaluated by a specific directional field algorithm. Secondly, since the filter orientation and the frequency parameters vary according to the fingerprint area, the fingerprint topological structure is enhanced by the Gabor filter with adaptive parameters. Finally, experimental tests show accurate final results for the matching step of an on-line recognition process. The text was submitted by the authors in English. Sanderson L. Gonzaga de Oliveira graduated from the Pontificia Universidade Catolica do Parana in 1996 and received his M.Sc. degree in 2004. Currently, he is a doctoral candidate in the Universidade Federal Fluminense, Brazil. His research interests include Image Processing and Computer Modeling. Author of 15 papers. A. Conci is a Dr.Sc. professor in the Department of Computer Science in Universidade Federal Fluminense. Her research interests include Biomechanics, Applications of Computer Vision, and Image Processing. F. M. Viola received his B.Sc. in Computer Science in 1999 and his M.Sc. at Universidade Federal Fluminense in 2006. His research interests include Biometrics and Image Processing.     

An accurate sampling-based method for approximating geometry

We present a sampling-based method for approximating the boundary of a geometry defined by various geometric operations. Based on a novel adaptive sampling condition, we first construct volumetric grids such that an error-minimizing point can be found in each cell to capture all the geometric objects inside the cell. We then construct a polygonal model from the grid. We guarantee the boundary approximation has the same topology as the exact surfaces, and the maximum approximation error from the exact surfaces is bounded by a user specified tolerance. Our method is robust and easy to implement. We have applied it in various applications such as remeshing of polygonal models, Boolean operations, and offsetting operations. We report experimental results on a variety of CAD models.     

The design,development and evaluation of hypermedia courseware for the World Wide Web

The World Wide Web provides hypertext and multimedia based information across the Internet. Many applications have been developed on http servers.One important and novel use of the servers has been the provision of courseware facilities. This includes on-line lecture notes, exercises and their solutions as well as interactive packages suited primarily for teaching and demonstration packages. A variety of disciplines have benefitted notably C programming, X Windows, Computer Vision, Image Processing, Computer Graphics, Artificial Intelligence and Parallel Computing.This paper will address the issues of (i) implementing a variety of computer science courses and (ii) using the packages in a class environment.It also considers how best to provide information in such a hypertext based system and how interactive image processing packages can be developed. A suite of multimedia based tools have been developed to facilitate such systems and these will be described in the paper. In particular we have developed a number of methods for running applications live over the WWW.     

Conformal Geometric Algebra for Robotic Vision

In this paper the authors introduce the conformal geometric algebra in the field of visually guided robotics. This mathematical system keeps our intuitions and insight of the geometry of the problem at hand and it helps us to reduce considerably the computational burden of the problems. As opposite to the standard projective geometry, in conformal geometric algebra we can deal simultaneously with incidence algebra operations (meet and join) and conformal transformations represented effectively using spinors. In this regard, this framework appears promising for dealing with kinematics, dynamics and projective geometry problems without the need to resort to different mathematical systems (as most current approaches do). This paper presents real tasks of perception and action, treated in a very elegant and efficient way: body–eye calibration, 3D reconstruction and robot navigation, the computation of 3D kinematics of a robot arm in terms of spheres, visually guided 3D object grasping making use of the directed distance and intersections of lines, planes and spheres both involving conformal transformations. We strongly believe that the framework of conformal geometric algebra can be, in general, of great advantage for applications using stereo vision, range data, laser, omnidirectional and odometry based systems. 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. He authored more than 90 strictly reviewed papers. Leo Hendrick Reyes-Lozano received his degree in Computer Engineering from the University of Guadalajara in 1999. He earned his MSc. and Ph.D. from the Center of Research and Advanced Studies (CINVESTAV) Guadalajara in 2001 and 2004, respectively. His research interests include Computer Vision, Geometric Algebra and Computer Graphics. Julio Zamora-Esquivel received his degree in Electronic Engineering at the Guzman City Institute of Tecnology in 2000. He earned his MSc. at the Center of Research and Advanced Studies (CINVESTAV) in Guadalajara in 2003. He is currently a Ph.D Candidate at CINVESTAV. His research interests include Computer Vision, Geometric Algebra and Robotics.     

Simultaneous and Sequential Reconstruction of Visual Primitives with Bundle Adjustment

In this paper, we compare the various methods for the simultaneous and sequential reconstruction of points, lines, planes, quadrics, plane conics and degenerate quadrics using Bundle Adjustment, both in projective and metric space. In contrast, most existing work on projective reconstruction focuses mainly on one type of primitive. We also compare the simultaneous refinement of all primitives through Bundle Adjustment with various sequential methods were only certain primitives are refined together. We found that even though the sequential methods may seem somewhat arbitrary on the choice of which primitives are refined together, a higher precision and speed is achieved in most cases. Leo Reyes graduated in Computer Engineering at the University of Guadalajara in 1999 and gained his Master’s and Doctoral degrees in Computer Science from the Center of Research and Advanced Studies Guadalajara (Centro de Investigación y Estudios Avanzados del IPN, CINVESTAV Unidad Guadalajara) in 2001 and 2004, respectively. He is currently working on a private company doing automatic inspection research. 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. At present he is a full professor at CINVESTAV, Unidad Guadalajara, Computer Science Group and he is responsible for the GEOVIS laboratory. His current research interest focuses on geometric methods for artificial perception and action systems. It includes geometric neural networks, visually guided robotics, color image processing, Lie bivector algebras for early vision and robot maneuvering. He developed the quaternion wavelet transform for quaternion multi-resolution analysis using the phase concept. 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. He has published over 100 refereed journal and conference papers.     

复杂曲面局部协调设计技术

综合利用CAGD中的曲线曲面理论和方法,通过构造合理的局部区域来裁剪N边汇交曲面,并利用汇交曲面的原始信息解决局部NURBS曲面的协调设计．该方法在新生成曲面与裁剪汇交曲面之间保证处处G^1连续,同时光滑地逼近于局部区域的特征走向．这种基于特征敏感的带复杂边界条件约束的协调曲面重构方法,作为曲面高级编辑工具已应用于计算机辅助反求工程CAD软件RE—SOFTV6．0中．实际应用表明,局部协调设计技术能够较好地解决复杂曲面造型和反求工程建模中模型的整体连续性和保形性．     

Comparison and improvement of tangent estimators on digital curves

Many contour-based applications rely on the estimation of the geometry of the shape, such as pattern recognition or classification methods. This paper proposes a comprehensive evaluation on the problem of tangent estimators on digital curves. The methods taken into account use different paradigms: approximation and digital geometry. In the former paradigm, methods based on polynomial fitting, smoothing and filtering are reviewed. In the latter case of digital geometry, we consider two methods that mainly rely on digital straight line recognition [J.-O. Lachaud, A. Vialard, F. de Vieilleville, Fast, accurate and convergent tangent estimation on digital contours, Image and Vision Computing 25(10) (2007) 1572-1587] and optimization [B. Kerautret, J.-O. Lachaud, Robust estimation of curvature along digital contours with global optimization, in: Proceedings of Discrete Geometry for Computer Imagery, Lyon, France, Lecture Notes in Computer Science, vol. 4992, Springer, Berlin, 2008]. The comparison takes into account objective criteria such as multi-grid convergence, average error, maximum error, isotropy and length estimation. Experiments underline that adaptive methods based on digital straight line recognition often propose a good trade-off between time and precision and that if precision is to be sought, non-adaptive methods can be easily transformed into adaptive methods to get more accurate estimations.     

A Numerical Scheme for the Curvature Equation Near the Singularities

In this paper we propose a modification in the usual numerical method for computing the solutions of the curvature equation in the plane . This modification takes place near the singularities of the image. We propose to use zero as the vertical speed at a saddle point and, at an extremum, the geometric mean of the eigenvalues of the Hessian matrix. This modification is theoretically justified and the preliminary experimental results show that it makes the algorithm more reliable.Marcos Craizer has a degree in mathematics from UFRJ (Rio de Janeiro), a M.Sc. from IMPA (Rio de Janeiro) and received his Ph.D. in mathematics also from IMPA, in 1989. His research interests in image processing includes image representation, curve evolution and PDE applications. Since 1988, he has been working at the math department of PUC-Rio, Brazil.Sinésio Pesco is an Assistant Professor of the Department of Mathematics at Pontifical Catholic University of Rio de Janeiro (PUCRio). He received his Ph.D. and MS degree in Applied Mathematics at PUC-Rio and a B.S. degree in mathematics from State University of Maringa Brasil. He has visiting positions at Lawrence Livermore National Laboratory, CSE/OGI School of Science and Engineering (Oregon Health & Science University) and Scientific Computation and Imaging Insititute (University of UTAH). His main research interests are in Computational Topology, Image Processing and Scientific Visualization. Since 1991, he has been working in the development of a CAD system for petroleum reservoir modeling.Ralph Teixeira has a degree in Computer Engineering from IME (in Rio), a M.Sc. from IMPA (also in Rio) and received his Ph.D. in Mathematics from Harvard University in 1998. His research interests in Computer Vision include shape representations by skeletons (medial axis and similar objects), curve evolutions and PDE applications. Since 2001, he has been working at Fundação Getulio Vargas in Rio de Janeiro, Brazil.     

Geo-located image categorization and location recognition

Image categorization is undoubtedly one of the most recent and challenging problems faced in Computer Vision. The scientific literature is plenty of methods more or less efficient and dedicated to a specific class of images; further, commercial systems are also going to be advertised in the market. Nowadays, additional data can also be attached to the images, enriching its semantic interpretation beyond the pure appearance. This is the case of geo-location data that contain information about the geographical place where an image has been acquired. This data allow, if not require, a different management of the images, for instance, to the purpose of easy retrieval from a repository, or of identifying the geographical place of an unknown picture, given a geo-referenced image repository. This paper constitutes a first step in this sense, presenting a method for geo-referenced image categorization, and for the recognition of the geographical location of an image without such information available. The solutions presented are based on robust pattern recognition techniques, such as the probabilistic Latent Semantic Analysis, the Mean Shift clustering and the Support Vector Machines. Experiments have been carried out on a couple of geographical image databases: results are actually very promising, opening new interesting challenges and applications in this research field. The article is published in the original. Marco Cristani received the Laurea degree in 2002 and the Ph.D. degree in 2006, both in Computer Science from the University of Verona, Verona, Italy. He was a visiting Ph.D. student at the Computer Vision Lab, Institute for Robotics and Intelligent Systems School of Engineering (IRIS), University of Southern California, Los Angeles, in 2004–2005. He is now an Assistant Professor with the Department of Computer Science, University of Verona, working with the Vision, Image Processing and Sounds (VIPS) Lab. His main research interests include statistical pattern recognition, generative modeling via graphical models, and non-parametric data fusion techniques, with applications on surveillance, segmentation and image and video retrieval. He is the author of several papers in the above subjects and a reviewer for several international conferences and journals. Alessandro Perina received the BD and MS degrees in Information Technologies and Intelligent and Multimedia Systems from the University of Verona, Verona, Italy, in 2004 and 2006, respectively. He is currently a Ph.D. candidate in the Computer Science Department at the University of Verona. His research interests include computer vision, machine learning and pattern recognition. He is a student member of the IEEE. Umberto Castellani is Ricercatore (i.e., Research Assistant) of Department of Computer Science at University of Verona. He received his Dottorato di Ricerca (Ph.D.) in Computer Science from the University of Verona in 2003 working on 3D data modelling and reconstruction. During his Ph.D., he had been Visiting Research Fellow at the Machine Vision Unit of the Edinburgh University, in 2001. In 2007 he has been an Invited Professor for two months at the LASMEA laboratory in Clermont-Ferrand, France. In 2008, he has been Visiting Researcher for two months at the PRIP laboratory of the Michigan State University (USA). His main research interests concern the processing of 3D data coming from different acquisition systems such as 3D models from 3D scanners, acoustic images for the vision in underwater environment, and MRI scans for biomedical applications. The addressed methodologies are focused on the intersections among Machine Learning, Computer Vision and Computer Graphics. Vittorio Murino received the Laurea degree in electronic engineering in 1989 and the Ph.D. degree in electronic engineering and computer science in 1993, both from the University of Genoa, Genoa, Italy. He is a Full Professor with the Department of Computer Science, University of Verona. From 1993 to 1995, he was a Postdoctoral Fellow in the Signal Processing and Understanding Group, Department of Biophysical and electronic Engineering, University of Genoa, where he supervised of research activities on image processing for object recognition and pattern classification in underwater environments. From 1995 to 1998, he was an Assistant Professor of the Department of Mathematics and Computer Science, University of Udine, Udine, Italy. Since 1998, he has been with the University of Verona, where he founded and is responsible for the Vision, Image processing, and Sound (VIPS) Laboratory. He is scientifically responsible for several national and European projects and is an Evaluator for the European Commission of research project proposals related to different scientific programmes and frameworks. His main research interests include computer vision and pattern recognition, probabilistic techniques for image and video processing, and methods for integrating graphics and vision. He is author or co-author of more than 150 papers published in refereed journals and international conferences. Dr. Murino is a referee for several international journals, a member of the technical committees for several conferences (ECCV, ICPR, ICIP), and a member of the editorial board of Pattern Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Pattern Analysis and Applications and Electronic Letters on Computer Vision and Image Analysis (ELCVIA). He was the promotor and Guest Editor off our special issues of Pattern Recognition and is a Fellow of the IAPR.