nils-herrmann/clean_scirepeval huggingface dataset:
Energy Minimization Methods in Computer Vision and Pattern Recognition, 5th International Workshop, EMMCVPR 2005, St. Augustine, FL, USA, November 9-11, 2005, Proceedings

Probabilistic and Informational Approaches.- Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application.- A Computational Approach to Fisher Information Geometry with Applications to Image Analysis.- Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic, Non-parametric Clustering.- Concurrent Stereo Matching: An Image Noise-Driven Model.- Color Correction of Underwater Images for Aquatic Robot Inspection.- Bayesian Image Segmentation Using Gaussian Field Priors.- Handling Missing Data in the Computation of 3D Affine Transformations.- Maximum-Likelihood Estimation of Biological Growth Variables.- Deformable-Model Based Textured Object Segmentation.- Total Variation Minimization and a Class of Binary MRF  Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study.- Combinatorial Approaches.- Probabilistic Subgraph Matching Based on Convex  Relaxation of Hard Classification Targets for LSE  Linear Programming Matching and Appearance-Adaptive Object Tracking.- Extraction of Layers of Similar Motion Through Combinatorial Techniques.- Object Categorization by Compositional Graphical Models.- Learning Hierarchical Shape Models from Examples.- Discontinuity Preserving Phase Unwrapping Using Graph Cuts.- Retrieving Articulated 3-D Models Using Medial Surfaces and Their Graph Spectra.- Spatio-temporal Segmentation Using Dominant Sets.- Stable Bounded Canonical Sets and Image Matching.- Coined Quantum Walks Lift the Cospectrality of Graphs and Trees.- Variational Approaches.- Geodesic Image Matching: A Wavelet Based Energy Minimization Scheme.- Geodesic Shooting and Diffeomorphic Matching Via Textured Meshes.- An Adaptive Variational Model for Image Decomposition.- Segmentation Informed by Manifold Learning.- One-Shot Integral Invariant Shape Priors for Variational Segmentation.- Dynamic Shape and Appearance Modeling Via Moving and Deforming Layers.- Energy Minimization Based Segmentation and Denoising Using a Multilayer Level Set Approach.- Constrained Total Variation Minimization and Application in Computerized Tomography.- Some New Results on Non-rigid Correspondence and Classification of Curves.- Edge Strength Functions as Shape Priors in Image Segmentation.- Spatio-temporal Prior Shape Constraint for Level Set  A New Implicit Method for Surface Segmentation by Minimal Paths: Applications in 3D Medical Images.- Other Approaches and  Increasing Efficiency of SVM by Adaptively Penalizing Outliers.- Locally Linear Isometric Parameterization.- A Constrained Hybrid Optimization Algorithm for Morphable Appearance Models.- Kernel Methods for Nonlinear Discriminative Convex Programming for Sparse Image Codes.- Stereo for Slanted Surfaces: First Order Disparities and Normal Consistency.- Brain Image Analysis Using Spherical Splines.- High-Order Differential Geometry of Curves for Multiview Reconstruction and Matching.
The Perceived Motion of a Sterokinetic Stimulus - eScholarship

An ellipse rotating in the image plane can produce several different percepts. The two-dimensional (2D) percepts are either a rotating rigid ellipse or a constantly deforming non-rigid ellipse. The 3D percept is a rotating rigid circular disk that is tilted relative to the image plane. Stimuli that generate 3D percepts based on purely 2D rotational motion are known as stereokinetic stimuli. We examined the 3D percepts generated by the rotating ellipse stimulus. In theory, the motion of the 3D percept cannot be reliably inferred based on the 2D stimulus. When we quantitatively estimated observers' perceived motion, however, we found that the perceived motion was nearly identical across observes. These results suggest that all observers had similar 3D percepts. We assumed that given the 2D rotating ellipse stimulus the visual system generates a rigid 3D percept that is as slow and smooth as possible. The percepts predicted by these assumptions closely matched the experimental data. These findings suggest that perceptual ambiguity in stereokinetic stimuli is resolved using slow and smooth motion assumptions.
Occlusions and binocular stereo

Binocular stereo is the process of obtaining depth information from a pair of cameras. In the past, stereo algorithms have had problems at occlusions and have tended to fail there (though sometimes post-processing has been added to mitigate the worst effects). We show that, on the contrary, occlusions can help stereo computation by providing cues for depth discontinuities.We describe a theory for stereo based on the Bayesian approach, using adaptive windows and a prior weak smoothness constraint, which incorporates occlusion. Our model assumes that a disparity discontinuity, along the epipolar line, in one eyealways corresponds to an occluded region in the other eye thus, leading to anocclusion constraint. This constraint restricts the space of possible disparity values, thereby simplifying the computations. An estimation of the disparity at occluded features is also discussed in light of psychophysical experiments. Using dynamic programming we can find the optimal solution to our system and the experimental results are good and support the assumptions made by the model.
Title A Hierarchical Compositional System for Rapid Object Detection Permalink

We describe a hierarchical compositional system for detecting deformable objects in images. Objects are represented by graphical models. The algorithm uses a hierarchical tree where the root of the tree corresponds to the full object and lower-level elements of the tree correspond to simpler features. The algorithm proceeds by passing simple messages up and down the tree. The method works rapidly, in under a second, on 320 × 240 images. We demonstrate the approach on detecting cats, horses, and hands. The method works in the presence of background clutter and occlusions. Our approach is contrasted with more traditional methods such as dynamic programming and belief propagation. Categories: Visual Processing
A Time-Efficient Cascade for Real-Time Object Detection: With applications for the visually impaired

Real-time object detection is essential for many computer vision applications. Many rapid detection algorithms are based on using cascades of tests. But existing design criteria for cascades either ignore the time complexity of the tests or make over-simplified assumptions about them. This paper gives a criterion for designing a time-efficient cascade that explicitly takes into account the time complexity of tests (as evaluated by computer run time) including the time for pre-processing. We design a greedy algorithm to minimize this criterion (noting that the full problem is NP-complete). Finally, we illustrate our method on the task of text detection in city scenes. This gives a text detection algorithm that runs at 0.025 seconds per 320×240 image, which is equivalent to 40 frames per second. This is a speed up factor of 2.5 compared to our previous text detector. It gives a realtime system which can be used for applications to help the blind and visually impaired.
The Convergence of Contrastive Divergences - eScholarship

The Convergence of Contrastive Divergences Alan Yuille Department of Statistics University of California at Los Angeles Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We relate the algorithm to the stochastic approxi- mation literature. This enables us to specify conditions under which the algorithm is guaranteed to converge to the optimal solution (with proba- bility 1). This includes necessary and sufﬁcient conditions for the solu- tion to be unbiased. 1  Many learning problems can be reduced to statistical inference of parameters. But inference algorithms for this task tend to be very slow. Recently Hinton proposed a new algorithm called contrastive divergences (CD) . Computer simulations show that this algorithm tends to converge, and to converge rapidly, although not always to the correct solution . Theoretical analysis shows that CD can fail but does not give conditions which guarantee convergence [3,4]. This paper relates CD to the stochastic approximation literature  and hence derives elementary conditions which ensure convergence (with probability 1). We conjecture that far stronger results can be obtained by applying more advanced techniques such as those described by Younes [7]. We also give necessary and sufﬁcient conditions for the solution of CD to be unbiased. Section (2) describes CD and shows that it is closely related to a class of stochastic ap- proximation algorithms for which convergence results exist. In section (3) we state and give a proof of a simple convergence theorem for stochastic approximation algorithms. Section (4) applies the theorem to give sufﬁcient conditions for convergence of CD. 2 Contrastive Divergence and its Relations The task of statistical inference is to estimate the model parameters ω ∗ which minimize the Kullback-Leibler divergence D(P 0 (x)||P (x|ω)) between the empirical distribution func-
Detecting and reading text in natural scenes

This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to determine which image features are reliable indicators of text and have low entropy (i.e. feature response is similar for all text images). We obtain weak classifiers by using joint probabilities for feature responses on and off text. These weak classifiers are used as input to an AdaBoost machine learning algorithm to train a strong classifier. In practice, we trained a cascade with 4 strong classifiers containing 79 features. An adaptive binarization and extension algorithm is applied to those regions selected by the cascade classifier. Commercial OCR software is used to read the text or reject it as a non-text region. The overall algorithm has a success rate of over 90% (evaluated by complete detection and reading of the text) on the test set and the unread text is typically small and distant from the viewer.
AdaBoost Learning for Detecting and Reading Text in City Scenes

This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to determine which image features are reliable indicators of text and have low entropy (i.e. feature response is similar for all text images). We obtain weak classifiers by using joint probabilities for feature responses on and off text. These weak classifiers are used as input to an AdaBoost machine learning algorithm to train a strong classifier. In practice, we trained a cascade with 4 strong classifiers containg 79 features. An adaptive binarization and extension algorithm is applied to those regions selected by the cascade classifier. An commercial OCR software is used to read the text or reject it as a non-text region. The overall algorithm has a success rate of over 90% (evaluated by complete detection and reading of the text) on the test set and the unread text is typically small and distant from the viewer.
The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters

This paper analyzes generalization of the classic Rescorla-Wagner (R- W) learning algorithm and studies their relationship to Maximum Like- lihood estimation of causal parameters. We prove that the parameters of two popular causal models, ?P and P C , can be learnt by the same generalized linear Rescorla-Wagner (GLRW) algorithm provided gener- icity conditions apply. We characterize the fixed points of these GLRW algorithms and calculate the fluctuations about them, assuming that the input is a set of i.i.d. samples from a fixed (unknown) distribution. We describe how to determine convergence conditions and calculate conver- gence rates for the GLRW algorithms under these conditions.
The Bas-Relief Ambiguity

AbstractWhen an unknown object with Lambertian reflectance is viewed orthographically, there is an implicit ambiguity in determining its 3-d structure: we show that the object's visible surface f(x, y) is indistinguishable from a “generalized bas-relief” transformation of the object's geometry, 
$$ {\bar f} $$
 (x, y) = λf(x, y) + μx + νy, and a corresponding transformation on the object's albedo. For each image of the object illuminated by an arbitrary number of distant light sources, there exists an identical image of the transformed object illuminated by similarly transformed light sources. This result holds both for the illuminated regions of the object as well as those in cast and attached shadows. Furthermore, neither small motion of the object, nor of the viewer will resolve the ambiguity in determining the flattening (or scaling) λ of the object's surface. Implications of this ambiguity on structure recovery and shape representation are discussed.
Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?

Many problems in vision can be formulated as Bayesian inference. It is important to determine the accuracy of these inferences and how they depend on the problem domain. In this paper, we provide a theoretical framework based on Bayesian decision theory which involves evaluating performance based on an ensemble of problem instances. We pay special attention to the task of detecting a target in the presence of background clutter. This framework is then used to analyze the detectability of curves in images. We restrict ourselves to the case where the probability models are ergodic (both for the geometry of the curve and for the imaging). These restrictions enable us to use techniques from large deviation theory to simplify the analysis. We show that the detectability of curves depend on a parameter K which is a function of the probability distributions characterizing the problem. At critical values of K the target becomes impossible to detect on average. Our framework also enables us to determine whether a simpler approximate model is sufficient to detect the target curve and hence clarify how much information is required to perform specific tasks. These results generalize our previous work (Yuille, A.L. and Coughlan, J.M. 2000. Pattern Analysis and Machine Intelligence PAMI, 22(2):160–173) by placing it in a Bayesian decision theory framework, by extending the class of probability models which can be analyzed, and by analysing the case where approximate models are used for inference.
Motion Estimation by Swendsen-Wang Cuts - eScholarship

Our paper has two main contributions. Firstly, it presents a model for image sequences motivated by an im- age encoding perspective. It models accreted regions, where objects appear, as well as motion and motion boundaries. We formulate the problem as probabilistic inference using prior models of images and the motion field. Secondly, it introduces a new algorithm for motion estimation based on Swendsen-Wang Cuts, which performs inference on the image sequence model using bottom-up proposals to guide the search. The algorithm proceeds by first estimating the motion without the boundaries, and then by clustering in the velocity space to obtain initial estimates of the motion boundaries. The algorithm performs MAP estimation by evolving the motion boundaries by a stochastic boundary diffusion algorithm, while improving the motion estimates. Our approach is illustrated on real images of city scenes and on simulated data and can deal with large motions (even 10 pixels or more per frame). We show a brief com- parison of Swendsen-Wang Cuts with Graph Cuts and Belief Propagation on the related stereo matching problem.
Spontaneous symmetry-breaking energy functions and the emergence of orientation selective cortical cells

The mammalian visual cortex is comprised of “hypercolumns” of orientation selective cells. The developmental process by which cells are generated with receptive fields tuned to a variety of orientations has so far remained a mystery. We present a model for the production of orientation selective cells that requires no external stimuli and a minimum of input parameters. The process involves spontaneous symmetry-breaking in an energy function that governs the maturation of the cortical cells in a multi-layer network of Hebb-type feedforward neurons. An important feature is that the symmetry breaking occurs for each cell separately and is not due to global organizing effects. We present examples of receptive field profiles calculated with the symmetry-breaking procedure and note that the results seem robust and may be useful in the study of development in several types of cortical cells. The inclusion of long range lateral (intra-layer) correlations in the energy function could result in the development of cell groups with correlated preferred orientations that resemble the hypercolumns seen in the visual cortex.
Object perception as Bayesian inference.

We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features.
The Convergence of Contrastive Divergences

The Convergence of Contrastive Divergences Alan Yuille Department of Statistics University of California at Los Angeles Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We relate the algorithm to the stochastic approxi- mation literature. This enables us to specify conditions under which the algorithm is guaranteed to converge to the optimal solution (with proba- bility 1). This includes necessary and sufﬁcient conditions for the solu- tion to be unbiased. 1  Many learning problems can be reduced to statistical inference of parameters. But inference algorithms for this task tend to be very slow. Recently Hinton proposed a new algorithm called contrastive divergences (CD) . Computer simulations show that this algorithm tends to converge, and to converge rapidly, although not always to the correct solution . Theoretical analysis shows that CD can fail but does not give conditions which guarantee convergence [3,4]. This paper relates CD to the stochastic approximation literature  and hence derives elementary conditions which ensure convergence (with probability 1). We conjecture that far stronger results can be obtained by applying more advanced techniques such as those described by Younes [7]. We also give necessary and sufﬁcient conditions for the solution of CD to be unbiased. Section (2) describes CD and shows that it is closely related to a class of stochastic ap- proximation algorithms for which convergence results exist. In section (3) we state and give a proof of a simple convergence theorem for stochastic approximation algorithms. Section (4) applies the theorem to give sufﬁcient conditions for convergence of CD. 2 Contrastive Divergence and its Relations The task of statistical inference is to estimate the model parameters ω ∗ which minimize the Kullback-Leibler divergence D(P 0 (x)||P (x|ω)) between the empirical distribution func-
Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability

We describe a method of learning generative models of objects from a set of images of the object under different, and unknown, illumination. Such a model allows us to approximate the objects' appearance under a range of lighting conditions. This work is closely related to photometric stereo with unknown light sources and, in particular, to the use of Singular Value Decomposition (SVD) to estimate shape and albedo from multiple images up to a linear transformation (Hayakawa, 1994). Firstly we analyze and extend the SVD approach to this problem. We demonstrate that it applies to objects for which the dominant imaging effects are Lambertian reflectance with a distant light source and a background ambient term. To determine that this is a reasonable approximation we calculate the eigenvectors of the SVD on a set of real objects, under varying lighting conditions, and demonstrate that the first few eigenvectors account for most of the data in agreement with our predictions. We then analyze the linear ambiguities in the SVD approach and demonstrate that previous methods proposed to resolve them (Hayakawa, 1994) are only valid under certain conditions. We discuss alternative possibilities and, in particular, demonstrate that knowledge of the object class is sufficient to resolve this problem. Secondly, we describe the use of surface consistency for putting constraints on the possible solutions. We prove that this constraint reduces the ambiguities to a subspace called the generalized bas relief ambiguity (GBR) which is inherent in the Lambertian reflectance function (and which can be shown to exist even if attached and cast shadows are present (Belhumeur et al., 1997)). We demonstrate the use of surface consistency to solve for the shape and albedo up to a GBR and describe, and implement, a variety of additional assumptions to resolve the GBR. Thirdly, we demonstrate an iterative algorithm that can detect and remove some attached shadows from the objects thereby increasing the accuracy of the reconstructed shape and albedo.
Title AdaBoost Learning for Detecting and Reading Text in City Scenes Permalink

This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to determine which image features are reliable indicators of text and have low entropy (i.e. feature response is similar for all text images). We obtain weak classifiers by using joint probabilities for feature responses on and off text. These weak classifiers are used as input to an AdaBoost machine learning algorithm to train a strong classifier. In practice, we trained a cascade with 4 strong classifiers containg 79 features. An adaptive binarization and extension algorithm is applied to those regions selected by the cascade classifier. An commercial OCR software is used to read the text or reject it as a non-text region. The overall algorithm has a success rate of over 90% (evaluated by complete detection and reading of the text) on the test set and the unread text is typically small and distant from the viewer.