Guest Editorial: Computational Vision at Brown

From the outside it may not be apparent that Brown University has a large, interdisciplinary, and vibrant computer vision community. Despite being a small school (5,674 undergraduate and 1,343 graduate students), Brown has a tightly knit community of vision researchers in various departments. This can be partly seen in this special issue which has contributions from researchers in the Division of Applied Mathematics, the Department of Computer Science, the Division of Engineering, and the Department of Cognitive and Linguistic Sciences. What we find amazing and wonderful about Brown is the high degree of interaction among researchers from these different disciplines. We hope the papers in this special issue give some glimpse into the computational vision research at Brown. Any collection of this type is only a snapshot at an instant in time and cannot hope to capture the full diversity of work going on here. While there is a broad range of vision research at Brown, our focus here is specifically on conveying a sense of the breadth and depth of computational vision research at Brown. Towards that end, we briefly summarize some of the current research efforts that are not represented by articles in this issue and provide some references for the interested reader.

[1]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[2]  M I Miller,et al.  Mathematical textbook of deformable neuroanatomies. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Sean P. MacEvoy,et al.  Lightness constancy in primary visual cortex , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[4]  D. Mumford,et al.  Stochastic models for generic images , 2001 .

[5]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  Joseph L. Mundy,et al.  Object Recognition: The Search for Representation , 1994, Object Representation in Computer Vision.

[7]  M. Tarr,et al.  FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise , 2000, Nature Neuroscience.

[8]  William H Warren,et al.  Path Integration from Optic Flow and Body Senses in a Homing Task , 2002, Perception.

[9]  U. Grenander,et al.  Computational anatomy: an emerging discipline , 1998 .

[10]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .

[11]  Leslie Pack Kaelbling,et al.  Ecological Robotics , 1998, Adapt. Behav..

[12]  Michael J. Black,et al.  Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion , 1997, International Journal of Computer Vision.

[13]  Elena K. Festa,et al.  Recruitment mechanisms in speed and fine-direction discrimination tasks , 1997, Vision Research.

[14]  Benjamin B. Kimia,et al.  On the role of medial geometry in human vision , 2003, Journal of Physiology-Paris.

[15]  S. Edelman,et al.  Learning Low Dimensional Representations of Visual Objects With Extensive Use of Prior Knowledge , 1997 .

[16]  David H. Laidlaw,et al.  Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms , 1997, IEEE Transactions on Medical Imaging.

[17]  Elie Bienenstocktsll.  A shape-recognition model using dynamical links , 1994 .

[18]  David J. Fleet,et al.  Robustly Estimating Changes in Image Appearance , 2000, Comput. Vis. Image Underst..

[19]  Isabel Gauthier,et al.  BOLD Activity during Mental Rotation and Viewpoint-Dependent Object Recognition , 2002, Neuron.

[20]  Joachim M. Buhmann,et al.  Histogram clustering for unsupervised segmentation and image retrieval , 1999, Pattern Recognit. Lett..

[21]  Anuj Srivastava,et al.  Probability Models for Clutter in Natural Images , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Gabriel Taubin,et al.  Space‐Optimized Texture Maps , 2002 .

[23]  F. Domini,et al.  Recovery of 3-D structure from motion is neither euclidean nor affine , 1998 .

[24]  J. Crisco,et al.  Fitting Manifold Surfaces To 3D Point Clouds , 2002 .

[25]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[26]  S. Geman,et al.  Hidden Markov Random Fields , 1995 .

[27]  Isabel Gauthier,et al.  Three-dimensional object recognition is viewpoint dependent , 1998, Nature Neuroscience.

[28]  Nathan Intrator,et al.  Receptive Field Formation in Natural Scene Environments: Comparison of Single-Cell Learning Rules , 1997, Neural Computation.

[29]  David B. Cooper,et al.  Assembling virtual pots from 3D measurements of their fragments , 2001, VAST '01.

[30]  Muge M. Bakircioglu,et al.  Curve matching on brain surfaces using frenet distances , 1998, Human brain mapping.

[31]  Nathan Intrator,et al.  (coarse Coding of Shape Fragments) (retinotopy) Representation of Structure , 2000 .

[32]  Fulvio Domini,et al.  Temporal integration in structure from motion. , 2002, Journal of experimental psychology. Human perception and performance.

[33]  David L. Sheinberg,et al.  The role of temporal cortical areas in perceptual organization. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[34]  A. Duchon,et al.  A Visual Equalization Strategy for Locomotor Control: Of Honeybees, Robots, and Humans , 2002, Psychological science.

[35]  LESLIE WELCH,et al.  PII: S0042-6989(97)00084-9 , 2002 .

[36]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[37]  L. Welch,et al.  The Effect of Inducer Polarity and Contrast on the Perception of Illusory Figures , 1997, Perception.

[38]  David Mumford,et al.  Pattern Theory: the Mathematics of Perception , 2002, math/0212400.

[39]  M. Tarr,et al.  Learning to see faces and objects , 2003, Trends in Cognitive Sciences.

[40]  Donald Geman,et al.  Boundary Detection by Constrained Optimization , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Donald Geman,et al.  A nonlinear filter for film restoration and other problems in image processing , 1992, CVGIP Graph. Model. Image Process..

[42]  Joseph J Crisco,et al.  Fitting manifold surfaces to three-dimensional point clouds. , 2002, Journal of biomechanical engineering.

[43]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[44]  Emanuele Trucco,et al.  Robust motion and correspondence of noisy 3-D point sets with missing data , 1999, Pattern Recognit. Lett..

[45]  Elie Bienenstock,et al.  A neural network for invariant pattern recognition. , 1987 .

[46]  David H. Laidlaw,et al.  Toward a framework for assembling broken pottery vessels , 2002, AAAI/IAAI.

[47]  Gabriel Taubin,et al.  Progressive forest split compression , 1998, SIGGRAPH.

[48]  C Caudek,et al.  Distortions of depth-order relations and parallelism in structure from motion , 1998, Perception & psychophysics.

[49]  M. Paradiso,et al.  Neural Correlates of Perceived Brightness in the Retina, Lateral Geniculate Nucleus, and Striate Cortex , 1999, The Journal of Neuroscience.

[50]  Gabriel Taubin,et al.  BLIC: Bi-Level Isosurface Compression , 2002, IEEE Visualization, 2002. VIS 2002..

[51]  J. Mundy.  Object recognition based on geometry: progress over three decades , 1998, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[52]  Gabriel Taubin,et al.  Geometric compression through topological surgery , 1998, TOGS.

[53]  C Caudek,et al.  Misperceptions of angular velocities influence the perception of rigidity in the kinetic depth effect. , 1997, Journal of experimental psychology. Human perception and performance.

[54]  Elie Bienenstock,et al.  Compositionality, MDL Priors, and Object Recognition , 1996, NIPS.

[55]  David J. Fleet,et al.  Probabilistic Detection and Tracking of Motion Boundaries , 2000, International Journal of Computer Vision.

[56]  Stuart Geman,et al.  Statistical methods for tomographic image reconstruction , 1987 .

[57]  Benjamin B. Kimia,et al.  On the evolution of curves via a function of curvature , 1992 .

[58]  Michael I. Miller,et al.  Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[59]  David B. Cooper,et al.  A Linear Dual-Space Approach to 3D Surface Reconstruction from Occluding Contours using Algebraic Surfaces , 2001, ICCV.

[60]  U. Grenander,et al.  Curve Matching on Brain Surfaces Using Induced Frenet Distance Metrics , 2000 .

[61]  David B. Cooper,et al.  The 3L Algorithm for Fitting Implicit Polynomial Curves and Surfaces to Data , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[62]  C Caudek,et al.  Perceiving surface slant from deformation of optic flow. , 1999, Journal of experimental psychology. Human perception and performance.

[63]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[64]  Joachim M. Buhmann,et al.  Unsupervised Texture Segmentation in a Deterministic Annealing Framework , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[65]  Basilis Gidas,et al.  A Renormalization Group Approach to Image Processing Problems , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[66]  Gabriel Taubin,et al.  Building a Digital Model of Michelangelo's Florentine Pietà , 2002, IEEE Computer Graphics and Applications.

[67]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[68]  David L. Sheinberg,et al.  Noticing Familiar Objects in Real World Scenes: The Role of Temporal Cortical Neurons in Natural Vision , 2001, The Journal of Neuroscience.

[69]  David B. Cooper,et al.  The Complex Representation of Algebraic Curves and Its Simple Exploitation for Pose Estimation and Invariant Recognition , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[70]  David H. Laidlaw,et al.  Experiments in Immersive Virtual Reality for Scientific Visualization , 2002, Comput. Graph..

[71]  Li Li,et al.  Perception of heading during rotation: sufficiency of dense motion parallax and reference objects , 2000, Vision Research.

[72]  S. Zucker,et al.  On the psychophysics of the shape triangle , 2001, Vision Research.

[73]  David J. Fleet,et al.  Probabilistic detection and tracking of motion discontinuities , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[74]  William H. Warren,et al.  Optic flow is used to control human walking , 2001, Nature Neuroscience.

[75]  Nikos K. Logothetis,et al.  Perceptual Learning and the Development of Complex Visual Representation in Temporal Cortical Neurons , 2002 .

[76]  Basilis Gidas,et al.  Tracking of moving objects in cluttered environments via Monte Carlo filter , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[77]  Michael A Paradiso,et al.  Perceptual and neuronal correspondence in primary visual cortex , 2002, Current Opinion in Neurobiology.

[78]  David A. Forsyth,et al.  Extracting projective structure from single perspective views of 3D point sets , 1993, 1993 (4th) International Conference on Computer Vision.

[79]  Benjamin B. Kimia,et al.  Shapes, shocks, and deformations I: The components of two-dimensional shape and the reaction-diffusion space , 1995, International Journal of Computer Vision.

[80]  Philip N. Klein,et al.  Recognition of shapes by editing their shock graphs , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[81]  David B. Cooper,et al.  Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning , 2002, ICPR.

[82]  Kim Steenstrup Pedersen,et al.  The Nonlinear Statistics of High-Contrast Patches in Natural Images , 2003, International Journal of Computer Vision.

[83]  Azriel Rosenfeld,et al.  Image Analysis and Computer Vision: 1995 , 1996, Comput. Vis. Image Underst..

[84]  L. Chalupa,et al.  The visual neurosciences , 2004 .

[85]  David B. Cooper,et al.  Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[86]  M. Tarr,et al.  Virtual reality in behavioral neuroscience and beyond , 2002, Nature Neuroscience.

[87]  Gye-Young Kim,et al.  Model-based tracking of moving object , 1997, Pattern Recognit..