OpenRaiser/Intern-Atlas huggingface dataset:
Closely Coupled Object Detection and Segmentation
Authors: Liang Zhao, Larry S. Davis
Venue: ICCV
Year: 2005

We propose a closely coupled object detection and segmentation algorithm for enhancing both processes in a cooperative and iterative manner. Figure-ground segmentation reduces the effect of background clutter on template matching; the matched template provides shape constraints on segmentation. More precisely, we estimate the probability of each pixel belonging to the foreground by a weighted sum of the estimates based on shape and color alone. The weight on the shape-based estimate is related to the probability that a familiar object is present and is updated dynamically so that we enforce shape constraints only where the object is present. Experiments on detecting people in images of cluttered scenes demonstrate that the proposed algorithm improves both segmentation and detection. More accurate object boundaries are extracted; higher object detection rates and lower false alarm rates are achieved than performing the two processes separately or sequentially.
Robust Point Matching for Two-Dimensional Nonrigid Shapes
Authors: Yefeng Zheng 0001, David S. Doermann
Venue: ICCV
Year: 2005

Recently, nonrigid shape matching has received more and more attention. For nonrigid shapes, most neighboring points cannot move independently under deformation due to physical constraints. Furthermore, the rough structure of a shape should be preserved under deformation otherwise even people cannot match shapes reliably. Therefore, though the absolute distance between two points may change significantly, the neighborhood of a point is well preserved in general. Based on this observation, we formulate point matching as a graph matching problem. Each point is a node in the graph, and two nodes are connected by an edge if their Euclidean distance is less than a threshold. The optimal match between two graphs is the one that maximizes the number of matched edges. The shape context distance is used to initialize the graph matching, followed by relaxation labeling for refinement. Nonrigid deformation is overcome by bringing one shape closer to the other in each iteration using deformation parameters estimated from the current point correspondence. Experiments demonstrate the effectiveness of our approach: it outperforms the shape context and TPS-RPM algorithms under nonrigid deformation and noise on a public data set.
Okapi-Chamfer Matching for Articulated Object Recognition
Authors: Hanning Zhou, Thomas S. Huang
Venue: ICCV
Year: 2005

Recent years have witnessed the rise of many effective text information retrieval systems. By treating local visual features as terms, training images as documents and input images as queries, we formulate the problem of object recognition into that of text retrieval. Our formulation opens up the opportunity to integrate some powerful text retrieval tools with computer vision techniques. In this paper, we propose to improve the efficiency of articulated object recognition by an Okapi-Chamfer matching algorithm. The algorithm is based on the inverted index technique. The inverted index is a widely used way to effectively organize a collection of text documents. With the inverted index, only documents that contain query terms are accessed and used for matching. To enable inverted indexing in an image database, we build a lexicon of local visual features by clustering the features extracted from the training images. Given a query image, we extract visual features and quantize them based on the lexicon, and then look up the inverted index to identify the subset of training images with non-zero matching score. To evaluate the matching scores in the subset, we combined the modified Okapi weighting formula with the Chamfer distance. The performance of the Okapi-Chamfer matching algorithm is evaluated on a hand posture recognition system. We test the system with both synthesized and real world images. Quantitative results demonstrate the accuracy and efficiency of our system.
Learning a Sparse, Corner-Based Representation for Time-varying Background Modeling
Authors: Qiang Zhu 0006, Shai Avidan, Kwang-Ting Cheng
Venue: ICCV
Year: 2005

Time-varying phenomenon, such as ripples on water, trees waving in the wind and illumination changes, produces false motions, which significantly compromises the performance of an outdoor-surveillance system. In this paper, we propose a corner-based background model to effectively detect moving-objects in challenging dynamic scenes. Specifically, the method follows a three-step process. First, we detect feature points using a Harris corner detector and represent them as SIFT-like descriptors. Second, we dynamically learn a background model and classify each extracted feature as either a background or a foreground feature. Last, a "Lucas-Kanade" feature tracker is integrated into this framework to differentiate motion-consistent foreground objects from background objects with random or repetitive motion. The key insight of our work is that a collection of SIFT-like features can effectively represent the environment and account for variations caused by natural effects with dynamic movements. Features that do not correspond to the background must therefore correspond to foreground moving objects. Our method is computational efficient and works in real-time. Experiments on challenging video clips demonstrate that the proposed method achieves a higher accuracy in detecting the foreground objects than the existing methods.
Consistent Segmentation for Optical Flow Estimation
Authors: C. Lawrence Zitnick, Nebojsa Jojic, Sing Bing Kang
Venue: ICCV
Year: 2005

In this paper, we propose a method for jointly computing optical flow and segmenting video while accounting for mixed pixels (matting). Our method is based on statistical modeling of an image pair using constraints on appearance and motion. Segments are viewed as overlapping regions with fractional (/spl alpha/) contributions. Bidirectional motion is estimated based on spatial coherence and similarity of segment colors. Our model is extended to video by chaining the pairwise models to produce a joint probability distribution to be maximized. To make the problem more tractable, we factorize the posterior distribution and iteratively minimize its parts. We demonstrate our method on frame interpolation.
Toward a Theory of Shape from Specular Flow
Authors: Yair Adato, Yuriy Vasilyev, Ohad Ben-Shahar, Todd E. Zickler
Venue: ICCV
Year: 2007

The image of a curved, specular (mirror-like) surface is a distorted reflection of the environment. The goal of our work is to develop a framework for recovering general shape from such distortions when the environment is neither calibrated nor known. To achieve this goal we consider far-field illumination, where the object-environment distance is relatively large, and we examine the dense specular flow that is induced on the image plane through relative object-environment motion. We show that under these very practical conditions the observed specular flow can be related to surface shape through a pair of coupled nonlinear partial differential equations. Importantly, this relationship depends only on the environment's relative motion and not its content. We examine the qualitative properties of these equations, present analytic methods for recovery of the shape in several special cases, and empirically validate our results using captured data. We also discuss the relevance to both computer vision and human perception.
Spatio-Temporal Shape from Silhouette using Four-Dimensional Delaunay Meshing
Authors: Ehsan Aganj, Jean-Philippe Pons, Florent Ségonne, Renaud Keriven
Venue: ICCV
Year: 2007

We propose a novel method for computing a four-dimensional (4D) representation of the spatio-temporal visual hull of a dynamic scene, based on an extension of a recent provably correct Delaunay meshing algorithm. By considering time as an additional dimension, our approach exploits seamlessly the time coherence between different frames to produce a compact and high-quality 4D mesh representation of the visual hull. The 3D visual hull at a given time instant is easily obtained by intersecting this 4D mesh with a temporal plane, thus enabling interpolation of objects' shape between consecutive frames. In addition, our approach offers easy and extensive control over the size and quality of the output mesh as well as over its associated re- projection error. Our numerical experiments demonstrate the effectiveness and flexibility of our approach for generating compact, high-quality, time-coherent visual hull representations from real silhouette image data.
Extracting Texels in 2.1D Natural Textures
Authors: Narendra Ahuja, Sinisa Todorovic
Venue: ICCV
Year: 2007

This paper proposes the problem of unsupervised extraction of texture elements, called texels, which repeatedly occur in the image of a frontally viewed, homogeneous, 2.1D, planar texture, and presents a solution. 2.1D texture here means that the physical texels are thin objects lying along a surface that may partially occlude one another. The image texture is represented by the segmentation tree whose structure captures the recursive embedding of regions obtained from a multiscale image segmentation. In the segmentation tree, the texels appear as subtrees with similar structure, with nodes having similar photometric and geometric properties. A new learning algorithm is proposed for fusing these similar subtrees into a tree-union, which registers all visible texel parts, and thus represents a statistical, generative model of the complete (unoccluded) texel. The learning algorithm involves concurrent estimation of texel tree structure, as well as the probability distributions of its node properties. Texel detection and segmentation are achieved simultaneously by matching the segmentation tree of a new image with the texel model. Experiments conducted on a newly compiled dataset containing 2.1D natural textures demonstrate the validity of our approach.
Learning the Taxonomy and Models of Categories Present in Arbitrary Images
Authors: Narendra Ahuja, Sinisa Todorovic
Venue: ICCV
Year: 2007

This paper proposes, and presents a solution to, the problem of simultaneous learning of multiple visual categories present in an arbitrary image set and their inter-category relationships. These relationships, also called their taxonomy, allow categories to be defined recursively, as spatial configurations of (simpler) subcategories each of which may be shared by many categories. Each image is represented by a segmentation tree, whose structure captures recursive embedding of image regions in a multiscale segmentation, and whose nodes contain the associated region properties. The presence of any occurring categories is reflected in the occurrence of associated, similar subtrees within the image trees. Similar subtrees across the entire image set are clustered. Each cluster corresponds to a discovered category, represented by the cluster properties. A (subcategory) cluster of small matching subtrees may occur within multiple clusters (categories) of larger matching subtrees, in different spatial relationships with subtrees from other small clusters. Such recursive embedding, grouping and intersection of clusters is captured in a directed acyclic graph (DAG) which represents the discovered taxonomy. Detection, recognition and segmentation of any of the learned categories present in a new image are simultaneously conducted by matching the segmentation tree of the new image with the learned DAG. This matching also yields a semantic explanation of the recognized category, in terms of the presence of its subcategories. Experiments with a newly compiled dataset of four-legged animals demonstrate good cross-category resolvability.
3D Object Representation Using Transform and Scale Invariant 3D Features
Authors: Erdem Akagündüz, Ilkay Ulusoy
Venue: ICCV
Year: 2007

An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output. This surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure.
Toward Reconstructing Surfaces With Arbitrary Isotropic Reflectance : A Stratified Photometric Stereo Approach
Authors: Neil Gordon Alldrin, David J. Kriegman
Venue: ICCV
Year: 2007

We consider the problem of reconstructing the shape of a surface with an arbitrary, spatially varying isotropic bidirectional reflectance distribution function (BRDF), and introduce a novel, stratified photometric stereo method. By using a particular configuration of lights, it is possible to use symmetry in the image measurements resulting from BRDF isotropy to estimate at each point a plane containing the surface normal. For differentiable surfaces, this allows us to recover the isocontours of the depth map, but not the actual depth associated with each contour. The isocontour structure provides topological information about the surface (critical points, Reeb graph, etc.). By using additional cues in the image data or imposing additional constraints on the surface (e.g., shadows, specular highlights, Helmholtz reciprocity, uniform BRDF), the unknown height of each isocontour can be estimated and the metric structure is resolved. We validate this technique on real and synthetic data by successfully recovering the isocontours of the depth map from images.
Specular Highlight Detection Based on the Fresnel Reflection Coefficient
Authors: Elli Angelopoulou
Venue: ICCV
Year: 2007

Reliable specularity detection can affect the accuracy of further image analysis. The majority of specularity detection algorithms are based on the chromaticity of the regions of specular highlights. They assume that the color of specular highlights of dielectrics is approximately the color of the incident light. We will show how this assumption is inaccurate especially in multispectral images. We propose a new, physics-based specularity detection method, which depends on the Fresnel term of the specular highlight, instead of assumptions on chromaticity space. We compute at each pixel an approximation to the Fresnel term at various wavelengths. We then use mean-shift analysis to segment the image based on the Fresnel information. Our experiments with multispectral, as well as traditional RGB images, show improved specularity detection and higher robustness in chromaticity space noise.
Robust Modelling and Tracking of NonRigid Objects Using Active-GNG
Authors: Anastassia Angelopoulou, Alexandra Psarrou, Gaurav Gupta 0001, José García Rodríguez 0001
Venue: ICCV
Year: 2007

This paper presents a robust approach to nonrigid modelling and tracking. The contour of the object is described by an active growing neural gas (A-GNG) network which allows the model to re-deform locally. The approach is novel in that the nodes of the network are described by their geometrical position, the underlying local feature structure of the image, and the distance vector between the modal image and any successive images. A second contribution is the correspondence of the nodes which is measured through the calculation of the topographic product, a topology preserving objective function which quantifies the neighbourhood preservation before and after the mapping. As a result, we can achieve the automatic modelling and tracking of objects without using any annotated training sets. Experimental results have shown the superiority of our proposed method over the original growing neural gas (GNG) network.
An L Approach to Structure and Motion Problems in 1D-Vision
Authors: Kalle Åström, Olof Enqvist, Carl Olsson, Fredrik Kahl, Richard I. Hartley
Venue: ICCV
Year: 2007

The structure and motion problem of multiple one-dimensional projections of a two-dimensional environment is studied. One-dimensional cameras have proven useful in several different applications, most prominently for autonomous guided vehicles, but also in ordinary vision for analysing planar motion and the projection of lines. Previous results on one-dimensional vision are limited to classifying and solving minimal cases, bundle adjustment for finding local minima to the structure and motion problem and linear algorithms based on algebraic cost functions. In this paper, we present a method for finding the global minimum to the structure and motion problem using the max norm of reprojection errors. We show how the optimal solution can be computed efficiently using simple linear programming techniques. The algorithms have been tested on a variety of different scenarios, both real and synthetic, with good performance. In addition, we show how to solve the multiview triangulation problem, the camera pose problem and how to dualize the algorithm in the Carlsson duality sense, all within the same framework.
ClassMap: Efficient Multiclass Recognition via Embeddings
Authors: Vassilis Athitsos, Alexandra Stefan, Quan Yuan, Stan Sclaroff
Venue: ICCV
Year: 2007

In many computer vision applications, such as face recognition and hand pose estimation, we need systems that can recognize a very large number of classes. Large margin classification methods, such as AdaBoost and SVMs, often provide competitive accuracy rates, but at the cost of evaluating a large number of binary classifiers. We propose an embedding-based method for efficient multiclass recognition. In our method, patterns and classes are mapped to vectors in such a way that patterns and their associated classes tend to get mapped close to each other. This way, given a test pattern, a small set of candidate classes can be identified efficiently using simple vector comparisons. In experiments with 3D hand pose recognition (2430 classes) and face recognition (535 classes), our method is between 3 and 28 times faster compared to evaluating all binary classifiers, with negligible or no loss in classification accuracy.
Task Specific Local Region Matching
Authors: Boris Babenko, Piotr Dollár, Serge J. Belongie
Venue: ICCV
Year: 2007

Many problems in computer vision require the knowledge of potential point correspondences between two images. The usual approach for automatically determining correspondences begins by comparing small neighborhoods of high saliency in both images. Since speed is of the essence, most current approaches for local region matching involve the computation of a feature vector that is invariant to various geometric and photometric transformations, followed by fast distance computations using standard vector norms. These algorithms include many parameters, and choosing an algorithm and setting its parameters for a given problem is more an art than a science. Furthermore, although invariance of the resulting feature space is in general desirable, there is necessarily a tradeoff between invariance and descriptiveness for any given task. In this paper we pose local region matching as a classification problem, and use powerful machine learning techniques to train a classifier that selects features from a much larger pool. Our algorithm can be trained on specific domains or tasks, and performs better than the state of the art in such cases. Since our method is an application of boosting, we refer to it as boosted region matching (BOOM).
A Database and Evaluation Methodology for Optical Flow
Authors: Simon Baker, Daniel Scharstein, J. P. Lewis 0001, Stefan Roth 0001, Michael J. Black, Richard Szeliski
Venue: ICCV
Year: 2007

The quantitative evaluation of optical flow algorithms by Barron et al. led to significant advances in the performance of optical flow methods. The challenges for optical flow today go beyond the datasets and evaluation methods proposed in that paper and center on problems associated with nonrigid motion, real sensor noise, complex natural scenes, and motion discontinuities. Our goal is to establish a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture; realistic synthetic sequences; high frame-rate video used to study interpolation error; and modified stereo sequences of static scenes. In addition to the average angular error used in Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and flow accuracy at motion boundaries and in textureless regions. We evaluate the performance of several well-known methods on this data to establish the current state of the art. Our database is freely available on the web together with scripts for scoring and publication of the results at http://vision.middlebury.edu/flow/.
Catadioptric Image-based Rendering for Mobile Robot Localization
Authors: Hynek Bakstein, Ales Leonardis
Venue: ICCV
Year: 2007

We present an approach to view-based mobile robot localization using a X-slits image based rendering (IBR) method for creating novel views from a set of input images. The input images are acquired by a non-central catadioptric sensor mounted on a robot moving on a straight line. We propose to use the IBR for column ordering only, where occlusions in the horizontal direction are modeled and the sensor can be non-central. For the column matching between a query view at an unknown position and virtual views created by IBR, we use correlation of columns.
Object Matching in the Presence of Non-Rigid Deformations Close to Similarities
Authors: Evguenia Balmachnova, Bram Platel, Luc Florack, Bart M. ter Haar Romeny
Venue: ICCV
Year: 2007

In this paper we address the problem of object retrieval based on scale-space interest points, namely top-points. The original retrieval algorithm can only cope with scale-Euclidean transformations. We extend the algorithm to the case of non-rigid transformations like affine and perspective transformations and investigate its robustness. The proposed algorithm is proven to be highly robust under various degrading factors, such as noise, occlusion, rendering artifacts, etc. and can deal with multiple occurrences of the object.
A Scene Representation Based on Multi-Modal 2D and 3D Features
Authors: Emre Baseski, Nicolas Pugeault, Sinan Kalkan, Dirk Kraft, Florentin Wörgötter, Norbert Krüger
Venue: ICCV
Year: 2007

Visually extracted 2D and 3D information have their own advantages and disadvantages that complement each other. Therefore, it is important to be able to switch between the different dimensions according to the requirements of the problem and use them together to combine the reliability of 2D information with the richness of 3D information. In this article, we use 2D and 3D information in a feature-based vision system and demonstrate their complementary properties on different applications (namely: depth prediction, scene interpretation, grasping from vision and object learning).