OpenRaiser/Intern-Atlas huggingface dataset:
Contour Grouping with Strong Prior Models
Authors: James H. Elder, Amnon Krupnik
Venue: CVPR
Year: 2001

Conventional approaches to perceptual grouping assume little specific knowledge about the object(s) of interest. However, there are many applications in which such knowledge is available and useful. We address the problem of finding the bounding contour of an object in an image when some prior knowledge about the object is available. We introduce a framework for combining prior probabilistic knowledge of the appearance of the object with probabilistic models for contour grouping. While prior probabilistic approaches have employed shortest-path algorithms to compute contours, this approach is limited in that many global properties cannot easily be incorporated in the computation. We propose as an alternative an approximate, constructive search technique, which finds a good (not necessarily optimal) solution, and which can accommodate important global cues and constraints. We apply this approach to the problem of computing exact lake boundaries from satellite imagery, given approximate prior models from an existing digital database. Our algorithm improves the accuracy of the prior GIS lake models by an average of 41%.
Non-Rigid Object Tracking using Performance Evaluation Measures as Feedback
Authors: Çigdem Eroglu Erdem, Bülent Sankur, A. Murat Tekalp
Venue: CVPR
Year: 2001

We present a scalable object tracking framework which is capable of tracking the contour of rigid and non-rigid objects in the presence of occlusion. The method adaptively divides the object contour into sub-contours, and employs several low-level features such as color edge, color segmentation, motion models, motion segmentation, and shape continuity information in a feedback loop to track each sub-contour. We also introduce some novel performance evaluation measures to evaluate the goodness of the segmentation and tracking. The results of these performance measures are utilized in a feedback loop to adjust the weights assigned to each of these low-level features for each sub-contour at each frame. The framework is scalable because it can be adapted to roughly track simple objects in real-time as well as pixel-accurate tracking of more complex objects in offline mode. The proposed method does not depend on any single motion or shape model, and does not need training. Experimental results demonstrate that the algorithm is able to track the object boundaries accurately under significant occlusion and background clutter.
Non parametric motion recognition using temporal multiscale Gibbs models
Authors: Ronan Fablet, Patrick Bouthemy
Venue: CVPR
Year: 2001

We present an original approach for non parametric motion analysis in image sequences. It relies on the statistical modeling of distributions of local motion-related measurements computed over image sequences. Contrary to previously proposed methods, the use of temporal multiscale Gibbs models allows us to handle in a unified statistical framework both spatial and temporal aspects of motion content. The important feature of our probabilistic scheme is to make the exact computation of conditional likelihood functions feasible and simple. It enables us to straightforwardly achieve model estimation according to the ML criterion and to benefit from a statistical point of view for classification issues. We have conducted motion recognition experiments over a large set of real image sequences comprising various motion types such as temporal texture samples, human motion examples and rigid motion situations.
Real-time affine region tracking and coplanar grouping
Authors: Vittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
Venue: CVPR
Year: 2001

We present a novel approach for tracking locally planar regions in an image sequence and their grouping into larger planar surfaces. The tracker recovers the affine transformation of the region and therefore yields reliable point correspondences between frames. Both edges and texture information are exploited in an integrated way, while not requiring the complete region's contour. The tracker withstands zoom, out-of-plane rotations, discontinuous motion and changes in illumination conditions while achieving real-time performance for a region. Multiple tracked regions are grouped into disjoint coplanarity classes. We first define a coplanarity score between each pair of regions, based on motion and texture cues. The scores are then analyzed by a clique-partitioning algorithm yielding the coplanarity classes that best fit the data. The method works in the presence of perspective distortions, discontinuous planar surfaces and considerable amounts of measurement noise.
Convex and Non-convex Illuminant Constraints for Dichromatic Colour Constancy
Authors: Graham D. Finlayson, Gerald Schaefer
Venue: CVPR
Year: 2001

The dichromatic reflectance model introduced by S. Shafer (1985) predicts that the colour signals of most materials fall on a plane spanned by a vector due to the material and a vector that represents the scene illuminant. Since the illuminant is in the span of all dichromatic planes, colour constancy can be achieved by finding the intersection of two or more planes. Unfortunately, this approach has proven to be hard to get to work in practice. First, segmentation needs to be carried out and second, the actual intersection computation is quite unstable: small changes in the orientation of a dichromatic plane can significantly alter the location of the intersection point. We propose to ameliorate the instability problem by regularising the intersection. Specifically, we introduce a constraint on the colour of the illuminant. We show how the intersection problem in the context of convex and non-convex illuminant constraints, based on the distribution of common light sources, can be solved. This algorithm coupled with the simplest of segmentations results in good estimation results for a large set of real images. Estimation performance is significantly better than for the unconstrained algorithm.
Efficient Spatiotemporal Grouping Using the Nystro"m Method
Authors: Charless C. Fowlkes, Serge J. Belongie, Jitendra Malik
Venue: CVPR
Year: 2001

Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation, but due to the computational demands, applications of such methods to spatiotemporal data have been slow to appear For even a short video sequence, the set of all pairwise voxel similarities is a huge quantity of data: one second of a 256/spl times/384 sequence captured at 30 Hz entails on the order of 10/sup 13/ pairwise similarities. The contribution of this paper is a method that substantially reduces the computational requirements of grouping algorithms based on spectral partitioning, making it feasible to apply them to very large spatiotemporal grouping problems. Our approach is based on a technique for the numerical solution of eigenfunction problems known as the Nystrom method This method allows extrapolation of the complete grouping solution using only a small number of "typical" samples. In doing so, we successfully exploit the fact that there are far fewer coherent groups in an image sequence than pixels.
Virtual Sample Generation For Template-Based Shape Matching
Authors: Dariu Gavrila, Jan Giebel
Venue: CVPR
Year: 2001

This paper presents a method for improving the performance of matching systems that correlate using shape templates. The basic idea involves extending an existing set of training shapes with generated "virtual" shapes, in order to improve representational capability, yet no a-priori feature correspondence is necessary among the original shapes in the training set. Instead, an integrated clustering and registration approach partitions the original shape samples into clusters of similar and registered shapes; in each cluster a separate feature space is embedded. This allows the derivation of standard compact parameterizations for each cluster. This paper demonstrates that sampling these low-order spaces can produce an extended training set which facilitates a superior matching performance, as measured by a ROC curve. In the experiments, we consider a realistic application involving thousands of pedestrian shapes and perform correlation matching based on distance transforms.
Segmentation for Robust Tracking in the Presence of Severe Occlusion
Authors: Camillo Gentile, Octavia I. Camps, Mario Sznaier
Venue: CVPR
Year: 2001

Tracking an object in a sequence of images can fail due to partial occlusion or clutter Robustness can be increased by tracking a set of "parts", provided that a suitable set can be identified. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function highly correlated with the tracking error.
Color Constant Ratio Gradients for Image Segmentation and Similarity of Texture Objects
Authors: Theo Gevers, Arnold W. M. Smeulders
Venue: CVPR
Year: 2001

We aim for content-based image retrieval of texture objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, region-based texture segmentation is applied on the target images prior to the actual image retrieval process. The retrieval scheme is empirically verified on color images taken from texture objects under different lighting, conditions.
Morphological Color Quantization
Authors: Stuart E. Gibson, Richard W. Harvey
Venue: CVPR
Year: 2001

Color histograms are a central feature in many image retrieval systems. Indeed they are part of the MPEG-7 standard. But histograms suffer from the "curse of dimensionality " in which the number of bins increases exponentially with the number of dimensions. There is therefore an imperative for methods for simplifying histograms. This paper presents a new method for simplifying histograms based on a cascade of increasing-scale graph morphology filters. The system we choose preserves scale space causality and so preserves the modes of the histogram. The method is quick to compute so is therefore a practically useful feature. We present results using the MPEG-7 Common Color Dataset that show that these new compressed features have a retrieval performance that is equivalent to full histograms.
A New 3-D Pattern Recognition Technique With Application to Computer Aided Colonoscopy
Authors: Salih Burak Göktürk, Carlo Tomasi
Venue: CVPR
Year: 2001

To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3D image data need to be constructed and subsequently used by a classification method. We present a computer aided diagnosis system for early diagnosis of colon cancer. The system extracts features via a new 3D pattern processing method and processes them using a support vector machine classifier. Our 3D pattern processing method, called Random Orthogonal Shape Section (ROSS) mimics the radiologist's way of viewing these images and combines information from many random triples of mutually orthogonal sections going through the volume. Another contribution of the paper is a new feedback framework between the classification algorithm and the definition of the features. This framework, called Distinctive Component Analysis combines support vector samples with linear discriminant analysis to map the features of clustered support vectors to a lower dimensional space where the two classes of objects of interest are optimally separated to obtain better features. We show that the combination of these better features with support vector machine classification provides a good recognition rate.
Affine Arithmetic Based Estimation of Cue Distributions in Deformable Model Tracking
Authors: Siome Goldenstein, Christian Vogler, Dimitris N. Metaxas
Venue: CVPR
Year: 2001

In this paper we describe a statistical method for the integration of an unlimited number of cues within a deformable model framework. We treat each cue as a random variable, each of which is the sum of a large number of local contributions with unknown probability distribution functions. Under the assumption that these distributions are independent, the overall distributions of the generalized cue forces can be approximated with multidimensional Gaussians, as per the central limit theorem. Estimating the covariance matrix of these Gaussian distributions, however, is difficult, because the probability distributions of the local contributions are unknown. We use affine arithmetic as a novel approach toward overcoming these difficulties. It lets us track and integrate the support of bounded distributions without having to know their actual probability distributions, and without having to make assumptions about their properties. We present a method for converting the resulting affine forms into the estimated Gaussian distributions of the generalized cue forces. This method scales well with the number of cues. We apply a Kalman filter as a maximum likelihood estimator to merge all Gaussian estimates of the cues into a single best fit Gaussian. Its mean is the deterministic result of the algorithm, and its covariance matrix provides a measure of the confidence in the result. We demonstrate in experiments how to apply this framework to improve the results of a face tracking system.
The Systematic Design and Analysis Cycle of a Vision System: A Case Study in Video Surveillance
Authors: Michael Greiffenhagen, Visvanathan Ramesh, Heinrich Niemann
Venue: CVPR
Year: 2001

As computer vision systems are increasingly developed and tested in the real-world, there is a significant need to formalize the process of system design and analysis so that engineers can rapidly design, test, and deploy vision systems for real-world applications. Our objective in this paper is to analyze the system design, analysis, and refinement cycle through a case study involving the systematic engineering of a dual-camera video surveillance system for people detection and zooming. We illustrate how an existing system designed and analyzed by following rigorous systematic engineering principles can be extended to relax the system operating conditions with minimal re-design and analysis efforts. The key conclusion is that by choosing appropriate modules and suitable statistical representations, we are able to re-use existing system design and performance analysis results.
Learning Probabilistic Distribution Model for Multi-View Face Detection
Authors: Lie Gu, Stan Z. Li, HongJiang Zhang
Venue: CVPR
Year: 2001

Modeling subspaces of a distribution of interest in high dimensional spaces is a challenging problem in pattern analysis. In this paper, we present a novel framework for pose invariant face detection through. multi-view face distribution modeling. The approach is aimed to learn a set of low-dimensional subspaces from an originally nonlinear distribution by using the mixtures of probabilistic PCA. From the experiments, we found the learned PPCA models are of low dimensionality and exhibit high local linearity, and consequently offer an efficient representation for visual recognition. The model is then used to extract features and select "representative" negative training samples. Multi-view face detection is performed in the derived feature space by classifying each face into one of the view classes or into the nonface class, by using a multi-class SVM array classifier. The classification results from each view are fused together and yields the final classification results. The experimental results demonstrate the performance superiority of our proposed framework while performing multi-view face detection.
A Weighted Non-Negative Matrix Factorization for Local Representations
Authors: David Guillamet, Marco Bressan 0001, Jordi Vitrià
Venue: CVPR
Year: 2001

The paper presents an improvement of the classical Non-negative Matrix Factorization (NMF) approach for dealing with local representations of image objects. NHF, when applied to global data representations such as faces presents a high ability to represent local features of the original data in an unsupervised way. However, when applied to local representations, NMF generates redundant basis. This work implements an improvement on the original NMF approach by incorporating prior knowledge in the form of a weight matrix extracted from the training data. A detailed mathematical description of the inclusion of this weight matrix is provided, and results demonstrating its advantages are included. Furthermore, the original NMF approach lacks a hierarchy of the elements of the estimated basis. A technique to determine an ordered set of discriminant basis is also presented. Finally, the effectiveness of the weighted approach with respect to the classical approach is experimentally compared. This is done by implementing a clustering algorithm that automatically extracts object parts from the NMF representation of an image database corresponding to newspapers.
Learning-Based Building Outline Detection from Multiple Aerial Images
Authors: Yanlin Guo, Harpreet S. Sawhney, Rakesh Kumar 0001, Steven C. Hsu
Venue: CVPR
Year: 2001

This paper presents a method for detecting building outlines using multiple aerial images. Since data-driven techniques may not be able to account for variability of building geometry and appearances, a key insight explored in this paper is a combination of model-based data driven front end with data driven learning in the back end for increased detection accuracy. The three main components of the detection algorithm are: (i) initialization. Image intensity and depth information are integrally used to efficiently detect buildings, and a robust rectilinear path finding algorithm is adopted to obtain good initial outlines. The initialization process involves the following steps: detecting location of buildings, determining the dominant orientations and knot points in the building outline and using these to fit the initial outline; (ii) learning. A compact set of building features are defined and learned from the well-delineated buildings, and a tree-based classifier is applied to the whole region to detect any missing buildings and obtain their rough outlines; and (iii) verification and refinement. Learned features are used to remove falsely detected buildings, and all outlines are refined by the deformation of rectilinear templates. The experiments, with improved detection rate and precise outlines, demonstrate the applicability of our algorithm.
Spatial Information in Multiresolution Histograms
Authors: Efstathios Hadjidemetriou, Michael D. Grossberg, Shree K. Nayar
Venue: CVPR
Year: 2001

Intensity histograms have been used extensively for recognition and for retrieval of images and video from visual databases. Intensity histograms of images at different individual resolutions have also been used for indexing. They suffer, however, from the inability to encode spatial image information. Spatial information can be incorporated into histograms simply by taking histograms of an image at multiple resolutions together to form a multiresolution histogram. Multiresolution histograms can also be computed, stored, and matched efficiently. The authors analyze and quantify the relation and sensitivity of the multiresolution histogram to spatial image information as well as to properties of shapes and textures in an image. They verify the analytical results experimentally and demonstrate the ability of multiresolution histograms to discriminate between images, as well as their robustness to noise.
A Topology Preserving Deformable Model Using Level Sets
Authors: Xiao Han 0007, Chenyang Xu 0001, Jerry L. Prince
Venue: CVPR
Year: 2001

Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models, the ability to automatically handle topology changes, turns out to be a liability in applications where the objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology. This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.
The Contracting Curve Density Algorithm and its Application to Model-based Image Segmentation
Authors: Robert Hanek
Venue: CVPR
Year: 2001

The article addresses the problem of model-based image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters, an initial probability distribution of the model edge in the image is obtained. From the vicinity of the surmised edge, local statistics are learned for both sides of the edge. These local statistics provide locally adapted criteria to distinguish the two sides of the edge, even in the presence of spatially changing properties such as texture, shading, or color. Based on the local statistics, the model parameters are iteratively refined using a MAP estimation. Experiments with RGB images show that the method is capable of achieving high subpixel accuracy and robustness even in the presence of texture, shading, clutter, and partial occlusion.
Detection and Tracking of Shopping Groups in Stores
Authors: Ismail Haritaoglu, Myron Flickner
Venue: CVPR
Year: 2001

We describe a monocular real-time computer vision system that identifies shopping groups by detecting and tracking multiple people as they wait in a checkout line or service counter. Our system segments each frame into foreground regions which contains multiple people. Foreground regions are further segmented into individuals using a temporal segmentation of foreground and motion cues. Once a person is detected, an appearance model based on color and edge density in conjunction with a mean-shift tracker is used to recover the person's trajectory. People are grouped together as a shopping group by analyzing interbody distances. The system also monitors the cashier's activities to determine when shopping transactions start and end. Experimental results demonstrate the robustness and real-time performance of the algorithm.
Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images
Authors: Bernd Heisele, Thomas Serre, Sayan Mukherjee 0001, Tomaso A. Poggio
Venue: CVPR
Year: 2001

We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.