3D Curves Reconstruction Based on Deformable Models

We present a new method, based on curve evolution, for the reconstruction of a 3D curve from two different projections. It is based on the minimization of an energy functional. Following the work on geodesic active contours by Caselles et al. (in Int. Conf. on Pattern Recognition, 1996, Vol. 43, pp. 693–737), we then transform the problem of minimizing the functional into a problem of geodesic computation in a Riemann space. The Euler-Lagrange equation of this new functional is derived and its associated PDE is solved using the level set formulation, giving the existence and uniqueness results. We apply the model to the reconstruction of a vessel from a biplane angiography.     

On the Use of Automata-based Techniques in Symbolic Model Checking: Invited Address

Checking infinite-state systems is frequently done by encoding infinite sets of states as regular languages. Computing such a regular representation of, say, the reachable set of states of a system requires acceleration techniques that can finitely compute the effect of an unbounded number of transitions. Among the acceleration techniques that have been proposed, one finds both specific and generic techniques. Specific techniques exploit the particular type of system being analyzed, e.g. a system manipulating queues or integers, whereas generic techniques only assume that the transition relation is represented by a finite-state transducer, which has to be iterated. In this paper, we survey two generic techniques that have been presented in [B. Boigelot and A. Legay and P. Wolper, Iterating Transducers in the Large, Proc. 15th Int. Conf. on Computer Aided Verification, Boulder, USA, Lecture Notes in Computer Science, volume 2725, year 2003, pages 223–235] and [B. Boigelot and A. Legay and P. Wolper, Omega-Regular Model Checking, Proc. 10th Int. Conf. on Tools and and Algorithms for the Construction and Analysis of Systems, Barcelona, Spain, Lecture Notes in Computer Science, volume 2988, year 2004, pages 561–575]. Those techniques build on earlier work, but exploits a number of new conceptual and algorithmic ideas, often induced with the help of experiments, that give it a broad scope, as well as good performance.     

A knowledge server for reasoning about temporal constraints between classes and instances of events

Reasoning with temporal constraints is a ubiquitous issue in many computer science tasks, for which many dedicated approaches have been and are being built. In particular, in many areas, including planning, workflow, guidelines, and protocol management, one needs to represent and reason with temporal constraints between classes of events (e.g., between the types of actions needed to achieve a goal) and temporal constraints between instances of events (e.g., between the specific actions being executed). The temporal constraints between the classes of events must be inherited by the instances, and the consistency of both types of constraints must be checked. In this article, we design a general‐purpose domain‐independent knowledge server dealing with these issues. In particular, we propose a formalism to represent temporal constraints, and we point out two orthogonal parameters that affect the definition of reasoning algorithms operating on them. We then show four algorithms to deal with inheritance and to perform temporal consistency checking (depending on the parameters) and we study their properties. Finally, we report the results we obtained by applying our system to the treatment of temporal constraints in clinical guidelines. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 919–947, 2004.     

All-Pairs Shortest Paths with Real Weights in <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis><Superscript>3</Superscript>/log <Emphasis Type="Italic">n</Emphasis>) Time

We describe an O(n ³/log n)-time algorithm for the all-pairs-shortest-paths problem for a real-weighted directed graph with n vertices. This slightly improves a series of previous, slightly subcubic algorithms by Fredman (SIAM J. Comput. 5:49–60, 1976), Takaoka (Inform. Process. Lett. 43:195–199, 1992), Dobosiewicz (Int. J. Comput. Math. 32:49–60, 1990), Han (Inform. Process. Lett. 91:245–250, 2004), Takaoka (Proc. 10th Int. Conf. Comput. Comb., Lect. Notes Comput. Sci., vol. 3106, pp. 278–289, Springer, 2004), and Zwick (Proc. 15th Int. Sympos. Algorithms and Computation, Lect. Notes Comput. Sci., vol. 3341, pp. 921–932, Springer, 2004). The new algorithm is surprisingly simple and different from previous ones. A preliminary version of this paper appeared in Proc. 9th Workshop Algorithms Data Struct. (WADS), Lect. Notes Comput. Sci., vol. 3608, pp. 318–324, Springer, 2005.     

The projection of systolic programs

A scheme is presented which transforms systolic programs with a two-dimensional structure to one dimension. The elementary steps of the transformation are justified by theorems in the theory of communicating sequential processes and the scheme is demonstrated with an example in occam: matrix composition/decomposition.A previous version appeared inProc. Int. Conf. on Mathematics of Program Construction, J. L. A. van de Snepscheut (ed.), Lecture Notes in Computer Science 375, pp. 307–324, Springer-Verlag, 1989.On leave from the Department of Computer Sciences, The University of Texas at Austin, Taylor Hall 2.124, Austin, Texas 78712-1188, USA.     

What can Computer Graphics expect from 3D Computer Vision?

Computer Vision is a discipline whose ultimate goal is to interpret optical images of real scenes. It is well understood that such a problem is cursed by ambiguity of interpretation and uncertainty of evidence. Despite imperfectness of results due to the scenes never following our prior models exactly, Computer Vision has achieved a significant progress in the past two decades. This talk will outline the quest of 3D Computer Vision by describing a processing pipeline that receives a heap of unorganized images from unknown cameras and produces a consistent 3D geometric model together with camera calibrations. We will see how new algorithms allow the standard conception of the pipeline as a series of independent processing steps gradually transform to a single complex, yet efficient vision task. We will identify some points where linking Computer Vision and Computer Graphics would bring significant progress.     

A feedback control scheme for multiple independent dynamic non-point agents

The Decentralized Navigation Functions' (DNF) based methodology, established in our previous work (D.V. Dimarogonas, M.M. Zavlanos, S.G. Loizou and K.J. Kyriakopoulos, “Decentralized motion control of multiple holonomic agents under input constraints”, in 42nd IEEE Conf. Deci. Cont., 2003, pp. 3390–3395 and S.G. Loizou, D.V. Dimarogonas and K.J. Kyriakopoulos, “Decentralized feedback stabilization of multiple nonholonomic agents”, in 2004 IEEE Int. Conf. on Robotics and Autom., 2004, pp. 3012–3017) on multi-agent systems with kinematic models of motion, is extended to the case where dynamic models of motion for both holonomic and nonholonomic agents must be treated. The proposed control scheme guarantees collision avoidance and global convergence of the multi-agent team to the desired goal configuration. The volume of each agent is taken into account and non-point models are considered. The satisfaction of the imposed system specifications under the proposed control scheme are verified and depicted through non-trivial computer simulations.     

Unifying statistical texture classification frameworks

The objective of this paper is to examine statistical approaches to the classification of textured materials from a single image obtained under unknown viewpoint and illumination. The approaches investigated here are based on the joint probability distribution of filter responses.

We review previous work based on this formulation and make two observations. First, we show that there is a correspondence between the two common representations of filter outputs—textons and binned histograms. Second, we show that two classification methodologies, nearest neighbour matching and Bayesian classification, are equivalent for particular choices of the distance measure. We describe the pros and cons of these alternative representations and distance measures, and illustrate the discussion by classifying all the materials in the Columbia-Utrecht (CUReT) texture database.

These equivalences allow us to perform direct comparisons between the texton frequency matching framework, best exemplified by the classifiers of Leung and Malik [Int. J. Comput. Vis. 43 (2001) 29], Cula and Dana [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2001) 1041], and Varma and Zisserman [Proceedings of the Seventh European Conference on Computer Vision 3 (2002) 255], and the Bayesian framework most closely represented by the work of Konishi and Yuille [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2000) 125].     

Adaptive tree similarity learning for image retrieval

Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval in recent years. However, the existing approaches either require prior knowledge of the data or converge slowly and are thus not coneffective. Motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval based on an adaptive tree similarity model to solve these difficulties. The proposed tree model is a hierarchical nonlinear Boolean representation of a user query concept. Each path of the tree is a clustering pattern of the feedback samples, which is small enough and local in the feature space that it can be approximated by a linear model nicely. Because of the linearity, the parameters of the similartiy model are better learned by the optimal adaptive filter, which does not require any prior knowledge of the data and supports incremental learning with a fast convergence rate. The proposed approach is simple to implement and achieves better performance than most approaches. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.An early version of part of the system was reported in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2001.     

Inplace run-length 2d compressed search

The recent explosion in the amount of stored data has necessitated the storage and transmission of data in compressed form. The need to quickly access this data has given rise to a new paradigm in searching, that of compressed matching (Proc. Data Compression Conf., Snow Bird, UT, 1992, pp. 279–288; Proc. 8th Annu. Symp. on Combinatorial Pattern Matching (CPM 97), Lecture Notes in Computer Science, Vol. 1264, Springer, Berlin, 1997, pp. 40–51; Proc. 7th Annu. Symp. on Combinatorial Pattern Matching (CPM 96), Lecture Notes in Computer Science, Vol. 1075, Springer, Berlin, 1996, pp. 39–49). The goal of the compressed pattern matching problem is to find a pattern in a text without decompressing the text.

The criterion of extra space is very relevant to compressed searching. An algorithm is called inplace if the amount of extra space used is proportional to the input size of the pattern. In this paper we present a 2d compressed matching algorithm that is inplace. Let compressed(T) and compressed(P) denote the compressed text and pattern, respectively. The algorithm presented in this paper runs in time O(|compressed(T)|+|P|log σ) where σ is min(|P|,|Σ|), and Σ is the alphabet, for all patterns that have no trivial rows (rows consisting of a single repeating symbol). The amount of space used is O(|compressed(P)|). The compression used is the 2d run-length compression, used in FAX transmission.     

Attention And Positional Encoding Are (Almost) All You Need For Shape Matching

The fast development of novel approaches derived from the Transformers architecture has led to outstanding performance in different scenarios, from Natural Language Processing to Computer Vision. Recently, they achieved impressive results even in the challenging task of non-rigid shape matching. However, little is known about the capability of the Transformer-encoder architecture for the shape matching task, and its performances still remained largely unexplored. In this paper, we step back and investigate the contribution made by the Transformer-encoder architecture compared to its more recent alternatives, focusing on why and how it works on this specific task. Thanks to the versatility of our implementation, we can harness the bi-directional structure of the correspondence problem, making it more interpretable. Furthermore, we prove that positional encodings are essential for processing unordered point clouds. Through a comprehensive set of experiments, we find that attention and positional encoding are (almost) all you need for shape matching. The simple Transformer-encoder architecture, coupled with relative position encoding in the attention mechanism, is able to obtain strong improvements, reaching the current state-of-the-art.     

On the controllability of the parameterized state matrix in “a continuation approach to eigenvalue assignment” by Harris,DeCarlo and Richter

This paper will investigate the controllability properties for systems parameterized as in Harns et al. (1983, Automatica, 19, 551–555). It will be shown that for systems of low dimensions this parameterization must be done carefully to guarantee that the system is controllable over the parameterization. The controllability property is key to the algorithms developed in Lefebvre et al. (1985, Int. J. Control, 41, 1273–1292), Richter and DeCarlo (1984, IEEE Trans. Aut. Control, AC-29), Harris et al. (1983, Automatica, 19, 551–555), Richter et al. (1981, Proc. 1981 Joint Aut. Control Conf.), Harris and DeCarlo (1982, Purdue School of EE, TR-EE 82-11) and Sebok and DeCarlo (1984, Proc. Allerton Conf. Communication, Control and Computing).     

Using Biologically Inspired Features for Face Processing

In this paper, we show that a new set of visual features, derived from a feed-forward model of the primate visual object recognition pathway proposed by Riesenhuber and Poggio (R&P Model) (Nature Neurosci. 2(11):1019–1025, 1999) is capable of matching the performance of some of the best current representations for face identification and facial expression recognition. Previous work has shown that the Riesenhuber and Poggio Model features can achieve a high level of performance on object recognition tasks (Serre, T., et al. in IEEE Comput. Vis. Pattern Recognit. 2:994–1000, 2005). Here we modify the R&P model in order to create a new set of features useful for face identification and expression recognition. Results from tests on the FERET, ORL and AR datasets show that these features are capable of matching and sometimes outperforming other top visual features such as local binary patterns (Ahonen, T., et al. in 8th European Conference on Computer Vision, pp. 469–481, 2004) and histogram of gradient features (Dalal, N., Triggs, B. in International Conference on Computer Vision & Pattern Recognition, pp. 886–893, 2005). Having a model based on shared lower level features, and face and object recognition specific higher level features, is consistent with findings from electrophysiology and functional magnetic resonance imaging experiments. Thus, our model begins to address the complete recognition problem in a biologically plausible way.     

Abstracting Vehicle Shape and Kinematic Constraints from Obstacle Avoidance Methods

Most obstacle avoidance techniques do not take into account vehicle shape and kinematic constraints. They assume a punctual and omnidirectional vehicle and thus they are doomed to rely on approximations when used on real vehicles. Our main contribution is a framework to consider shape and kinematics together in an exact manner in the obstacle avoidance process, by abstracting these constraints from the avoidance method usage. Our approach can be applied to many non-holonomic vehicles with arbitrary shape. For these vehicles, the configuration space is three-dimensional, while the control space is two-dimensional. The main idea is to construct (centred on the robot at any time) the two-dimensional manifold of the configuration space that is defined by elementary circular paths. This manifold contains all the configurations that can be attained at each step of the obstacle avoidance and is thus general for all methods. Another important contribution of the paper is the exact calculus of the obstacle representation in this manifold for any robot shape (i.e. the configuration regions in collision). Finally, we propose a change of coordinates of this manifold so that the elementary paths become straight lines. Therefore, the three-dimensional obstacle avoidance problem with kinematic constraints is transformed into the simple obstacle avoidance problem for a point moving in a two-dimensional space without any kinematic restriction (the usual approximation in obstacle avoidance). Thus, existing avoidance techniques become applicable. The relevance of this proposal is to improve the domain of applicability of a wide range of obstacle avoidance methods. We validated the technique by integrating two avoidance methods in our framework and performing tests in the real robot. Javier Minguez received the physics science degree in 1996 from the Universidad Complutense de Madrid, Madrid, Spain, and the Ph.D. degree in computer science and systems engineering in 2002 from the University of Zaragoza, Zaragoza, Spain. During his student period, in 1999 he was a research visitor in the Robotics and Artificial Intelligence Group, LAASCNRS, Toulouse, France. In 2000, he visited the Robot and ComputerVision Laboratory (ISR-IST), Technical University of Lisbon, Lisbon, Portugal. In 2001, he was with the Robotics Laboratory, Stanford University, Stanford, USA. He is currently a fulltime Researcher in the Robot, Vision, and Real Time Group, in the University of Zaragoza. His research interests are obstacle avoidance, motion estimation and sensor-based motion systems for mobile robots. Luis Montano was born on September 6, 1958 in Huesca, Spain. He received the industrial engineering degree in 1981 and the PhD degree in 1987 from the University of Zaragoza, Spain. He is an Associate Professor of Systems Engineering and Automatic Control at the University of Zaragoza (Spain). He has been Head of the Computer Science and Systems Engineering Department of the University of Zaragoza. Currently he is the coordinator of the Production Technologies Research in the Aragon Institute of Engineering Research and of the Robotics, Perception and Real Time group of the University of Zaragoza. He is principal researcher in robotic projects and his major research interests are mobile robot navigation and cooperative robots. José Santos-Victor received the PhD degree in Electrical and Computer Engineering in 1995 from Instituto Superior Técnico (IST - Lisbon, Portugal), in the area of Computer Vision and Robotics. He is an Associate Professor at the Department of Electrical and Computer Engineering of IST and a researcher of the Institute of Systems and Robotics (ISR), at the Computer and Robot Vision Lab - VisLab. (http://vislab.isr.ist.utl.pt) He is the scientific responsible for the participation of IST in various European and National research projects in the areas of Computer Vision and Robotics. His research interests are in the areas of Computer and Robot Vision, particularly in the relationship between visual perception and the control of action, biologically inspired vision and robotics, cognitive vision and visual controlled (land, air and underwater) mobile robots. Prof. Santos-Victor is an IEEE member and an Associated Editor of the IEEE Transactions on Robotics.     

An Integrated Framework for Semantic Annotation and Adaptation

Tools for the interpretation of significant events from video and video clip adaptation can effectively support automatic extraction and distribution of relevant content from video streams. In fact, adaptation can adjust meaningful content, previously detected and extracted, to the user/client capabilities and requirements. The integration of these two functions is increasingly important, due to the growing demand of multimedia data from remote clients with limited resources (PDAs, HCCs, Smart phones). In this paper we propose an unified framework for event-based and object-based semantic extraction from video and semantic on-line adaptation. Two cases of application, highlight detection and recognition from soccer videos and people behavior detection in domotic^* applications, are analyzed and discussed.Domotics is a neologism coming from the Latin word domus (home) and informatics.Marco Bertini has a research grant and carries out his research activity at the Department of Systems and Informatics at the University of Florence, Italy. He received a M.S. in electronic engineering from the University of Florence in 1999, and Ph.D. in 2004. His main research interest is content-based indexing and retrieval of videos. He is author of more than 25 papers in international conference proceedings and journals, and is a reviewer for international journals on multimedia and pattern recognition.Rita Cucchiara (Laurea Ingegneria Elettronica, 1989; Ph.D. in Computer Engineering, University of Bologna, Italy 1993). She is currently Full Professor in Computer Engineering at the University of Modena and Reggio Emilia (Italy). She was formerly Assistant Professor (‘93–‘98) at the University of Ferrara, Italy and Associate Professor (‘98–‘04) at the University of Modena and Reggio Emilia, Italy. She is currently in the Faculty staff of Computer Engenering where has in charges the courses of Computer Architectures and Computer Vision.Her current interests include pattern recognition, video analysis and computer vision for video surveillance, domotics, medical imaging, and computer architecture for managing image and multimedia data.Rita Cucchiara is author and co-author of more than 100 papers in international journals, and conference proceedings. She currently serves as reviewer for many international journals in computer vision and computer architecture (e.g. IEEE Trans. on PAMI, IEEE Trans. on Circuit and Systems, Trans. on SMC, Trans. on Vehicular Technology, Trans. on Medical Imaging, Image and Vision Computing, Journal of System architecture, IEEE Concurrency). She participated at scientific committees of the outstanding international conferences in computer vision and multimedia (CVPR, ICME, ICPR, ...) and symposia and organized special tracks in computer architecture for vision and image processing for traffic control. She is in the editorial board of Multimedia Tools and Applications journal. She is member of GIRPR (Italian chapter of Int. Assoc. of Pattern Recognition), AixIA (Ital. Assoc. Of Artificial Intelligence), ACM and IEEE Computer Society.Alberto Del Bimbo is Full Professor of Computer Engineering at the Università di Firenze, Italy. Since 1998 he is the Director of the Master in Multimedia of the Università di Firenze. At the present time, he is Deputy Rector of the Università di Firenze, in charge of Research and Innovation Transfer. His scientific interests are Pattern Recognition, Image Databases, Multimedia and Human Computer Interaction. Prof. Del Bimbo is the author of over 170 publications in the most distinguished international journals and conference proceedings. He is the author of the “Visual Information Retrieval” monography on content-based retrieval from image and video databases edited by Morgan Kaufman. He is Member of IEEE (Institute of Electrical and Electronic Engineers) and Fellow of IAPR (International Association for Pattern Recognition). He is presently Associate Editor of Pattern Recognition, Journal of Visual Languages and Computing, Multimedia Tools and Applications Journal, Pattern Analysis and Applications, IEEE Transactions on Multimedia, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He was the Guest Editor of several special issues on Image databases in highly respected journals.Andrea Prati (Laurea in Computer Engineering, 1998; PhD in Computer Engineering, University of Modena and Reggio Emilia, 2002). He is currently an assistant professor at the University of Modena and Reggio Emilia (Italy), Faculty of Engineering, Dipartimento di Scienze e Metodi dell’Ingegneria, Reggio Emilia. During last year of his PhD studies, he has spent six months as visiting scholar at the Computer Vision and Robotics Research (CVRR) lab at University of California, San Diego (UCSD), USA, working on a research project for traffic monitoring and management through computer vision. His research interests are mainly on motion detection and analysis, shadow removal techniques, video transcoding and analysis, computer architecture for multimedia and high performance video servers, video-surveillance and domotics. He is author of more than 60 papers in international and national conference proceedings and leading journals and he serves as reviewer for many international journals in computer vision and computer architecture. He is a member of IEEE, ACM and IAPR.     

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     

Global Non-rigid Alignment of Surface Sequences

This paper presents a general approach based on the shape similarity tree for non-sequential alignment across databases of multiple unstructured mesh sequences from non-rigid surface capture. The optimal shape similarity tree for non-rigid alignment is defined as the minimum spanning tree in shape similarity space. Non-sequential alignment based on the shape similarity tree minimises the total non-rigid deformation required to register all frames in a database into a consistent mesh structure with surfaces in correspondence. This allows alignment across multiple sequences of different motions, reduces drift in sequential alignment and is robust to rapid non-rigid motion. Evaluation is performed on three benchmark databases of 3D mesh sequences with a variety of complex human and cloth motion. Comparison with sequential alignment demonstrates reduced errors due to drift and improved robustness to large non-rigid deformation, together with global alignment across multiple sequences which is not possible with previous sequential approaches.     

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.     

Identification of function distinguishable languages

We show how appropriately chosen functions f which we call distinguishing can be used to make deterministic finite automata backward deterministic. This idea can be exploited to design regular language classes called f-distinguishable which are identifiable in the limit from positive samples. Special cases of this approach are the k-reversible and terminal distinguishable languages, as discussed in Angluin (J. Assoc. Comput. Mach. 29 (3) (1982) 741), Fernau (Technical Report WSI-99-23, Universität Tübingen (Germany), Wilhelm-Schickard-Institut für Informatik, 1999, Short version published in the proceedings of AMAI 2000, see \tt http://rutcor.rutgers.edu/˜amai/aimath00/AcceptedCont.htm, Proc. 15th Internat. Conf. on Pattern Recognition (ICPR 2000), Vol. 2, IEEE Press, New York, 2000, pp. 125–128), Radhakrishnan (Ph.D. Thesis, Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, India, 1987), Radhakrishnan and Nagaraja (IEEE Trans. Systems, Man Cybernet. 17 (6) (1987) 982). Moreover, we show that all regular languages may be approximated in the setting introduced by Kobayashi and Yokomori (in: K. P. Jantke, T. Shinohara, Th. Zeugmann (Eds.), Proc. Sixth Internat. Conf. Algorithmic Learning Theory (ALT’95), Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence, Vol. 997, Springer, Berlin, 1995, pp. 298–312), (Theoret. Comput. Sci. 174 (1997) 251–257) by any class of f-distinguishable languages. Observe that the class of all function-distinguishable languages is equal to the class of regular languages.     

Probability approximation schemes for stochastic programs with distributionally robust second-order dominance constraints

Since the pioneering work by Dentcheva and Ruszczyński [Optimization with stochastic dominance constraints, SIAM J. Optim. 14 (2003), pp. 548–566], stochastic programs with second-order dominance constraints (SPSODC) have received extensive discussions over the past decade from theory of optimality to numerical schemes and practical applications. In this paper, we investigate discrete approximation of SPSODC when (a) the true probability is known but continuously distributed and (b) the true probability distribution is unknown but it lies within an ambiguity set of distributions. Differing from the well-known Monte Carlo discretization method, we propose a deterministic discrete approximation scheme due to Pflug and Pichler [Approximations for Probability Distributions and Stochastic Optimization Problems, International Series in Operations Research & Management Science, Vol. 163, Springer, New York, 2011, pp. 343–387] and demonstrate that the discrete probability measure and the ambiguity set of discrete probability measures approximate their continuous counterparts under the Kantorovich metric. Stability analysis of the optimal value and optimal solutions of the resulting discrete optimization problems is presented and some comparative numerical test results are reported.