OpenRaiser/Intern-Atlas huggingface dataset:
A Region-Based MRF Model for Unsupervised Segmentation of Moving Objects in Image Sequences
Authors: Yaakov Tsaig, Amir Averbuch
Venue: CVPR
Year: 2001

This paper addresses the problem of segmentation of moving objects in image sequences, which is of key importance in content-based applications. We transform the problem into a graph labeling problem over a region adjacency graph (RAG), by introducing a Markov random field (MRF) model based on spatio-temporal information. The initial partition is obtained by fast, color-based watershed segmentation. The motion of each region is estimated and validated in a hierarchical framework. A dynamic memory, based on object tracking, is incorporated into the segmentation process to maintain temporal coherence. The performance of the algorithm is evaluated on several real-world image sequences.
Bayesian Color Constancy for Outdoor Object Recognition
Authors: Yanghai Tsin, Robert T. Collins, Visvanathan Ramesh, Takeo Kanade
Venue: CVPR
Year: 2001

Outdoor scene classification is challenging due to irregular geometry, uncontrolled illumination, and noisy reflectance distributions. This paper discusses a Bayesian approach to classifying a color image of an outdoor scene. A likelihood model factors in the physics of the image formation process, sensor noise distribution, and prior distributions over geometry, material types, and illuminant spectrum parameters. These prior distributions are learned through a training process that uses color observations of planar scene patches over time. An iterative linear algorithm estimates the maximum likelihood reflectance, spectrum, geometry, and object class labels for a new image. Experiments on images taken by outdoor surveillance cameras classify known material types and shadow regions correctly, and flag as outliers material types that were not seen previously.
Efficient Grouping under Perspective Skew
Authors: Andreas Turina, Tinne Tuytelaars, Luc Van Gool
Venue: CVPR
Year: 2001

We present an efficient grouping strategy for the detection of regular repetitions of planar (but not necessarily coplanar) patterns. At the heart of our system lie the fixed structures that typify the geometric transformations of the regularities. The approach unifies a number of grouping types that have traditionally been dealt with separately. It avoids the use of combinatorics in the search for pattern repetitions, through the combined use of invariants for hashing-based pattern matching on the one hand, and Hough transforms for the detection of the fixed structures on the other hand. In this paper we concentrate on planar homologies and elations in particular Results on real-world scenes demonstrate the performance of the approach.
Semi-Dense Stereo Correspondence with Dense Features
Authors: Olga Veksler
Venue: CVPR
Year: 2001

We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing a semi-dense disparity map. Our dense feature is defined with respect to both images of a stereo pair, and it is computed during the stereo matching process, not a preprocessing step. In essence, a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error (which is basically the difference in intensities between corresponding boundary pixels). Our algorithm produces accurate semi-dense disparity maps, leaving featureless regions in the scene unmatched It is robust, requires little parameter tuning, can handle brightness differences between images, and is fast (linear complexity).
Optimal Adaptive Learning for Image Retrieval
Authors: Tao Wang, Yong Rui, Shi-Min Hu 0001
Venue: CVPR
Year: 2001

Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval. However, the existing approaches either require prior knowledge of the data or entail high computation costs, making them less practical. To overcome these difficulties and motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval. Specifically, we cast the image retrieval problem in the optimal filtering framework, which does not require prior knowledge of the data, supports incremental learning, is simple to implement and achieves better performance than state-of-the-art approaches. To evaluate the effectiveness and robustness of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images. We report promising results on a wide variety of queries.
Matching of Double-Sided Document Images to Remove Interference
Authors: Qian Wang, Chew Lim Tan
Venue: CVPR
Year: 2001

The National Archives of Singapore keeps a large volume of historical handwritten documents. One, common problem with the archives is that over the years, ink sipped through the pages of these documents such that characters on the reverse side become visible and interfere with the characters on the front side. This paper addresses this problem and develops a novel algorithm to extract clear textual images from the, interference. We achieve this by mapping images from both sides of a page such that interfering strokes seen on the front side are matched with the strokes originating from the reverse side so as to achieve a cancellation effect. The resultant image a further subjected to an improved Canny edge detection to eliminate remaining background interference. Experimental results have confirmed the validity of our proposed method.
Generalized Dynamic Programming Approaches for Object Detection: Detecting Spine Boundaries and Vertebra Endplates
Authors: Guo-Qing Wei, Jian Zhong Qian, Helmuth Schramm
Venue: CVPR
Year: 2001

Object detection employing high-level knowledge is a challenging problem in image analysis. The authors propose a dynamic programming approach to address some related issues in this aspect. In particular, we propose to fuse the detection of two curves to form a dual dynamic programming procedure so that spatial relationships between the two curves can be enforced. In another pursuit of applying object level knowledge, we propose to introduce local backward tracing to the forward propagation step in dynamic programming, so that global constraints, containing even unknown parameters, can be imposed in a progressive manner. These approaches are explained in the context of spine landmark detection. Experimental results are presented to show the efficiency of the proposed methods. The methods are extendable to other application domains.
Two-body Segmentation from Two Perspective Views
Authors: Lior Wolf, Amnon Shashua
Venue: CVPR
Year: 2001

We consider a scene containing two independently and generally moving objects, viewed by two general perspective views. Using matching points arising from both objects simultaneously we derive a geometrical constraint, applicable to points from both objects, we call the segmentation matrix. We then use this constraint in order to recover the fundamental matrices associated with, each object, or simply to segment the scene into the two objects. Moreover, when the two bodies move in pure translation relative to each other we can both segment the scene and recover the affine calibration (homography at infinity) of the camera geometry. Unlike algorithms suggested in the past we need only two images, we work with general projective cameras (rather than affine or orthographic) and with general body motion, and no prior information beyond point matches is required.
Multibody Grouping via Orthogonal Subspace Decomposition
Authors: Ying Wu 0001, Zhengyou Zhang, Thomas S. Huang, John Y. Lin
Venue: CVPR
Year: 2001

Multibody structure from motion could be solved by the factorization approach. However, the noise measurements would make the segmentation difficult when analyzing the shape interaction matrix. This paper presents an orthogonal subspace decomposition and grouping technique to approach such a problem. We decompose the object shape spaces into signal subspaces and noise subspaces. We show that the signal subspaces of the object shape spaces are orthogonal to each other. Instead of using the shape interaction matrix contaminated by noise, we introduce the shape signal subspace distance matrix for shape space grouping. Outliers could be easily identified by this approach. The robustness of the proposed approach lies in the fact that the shape space decomposition alleviates the influence of noise, and has been verified with extensive experiments.
Computing Depth Maps From Descent Imagery
Authors: Yalin Xiong, Clark F. Olson, Larry H. Matthies
Venue: CVPR
Year: 2001

In the exploration of the other planets of our solar system, images taken during a lander's descent to the surface of a planet provide a critical link between orbital images and surface images. The descent images not only allow us to locate the landing site in a global coordinate frame, but also provide progressively higher-resolution maps for mission planning. The paper addresses the generation of depth maps from the descent images. Our approach has two steps: motion refinement and depth recovery. During motion refinement, we use an initial motion estimate in order to avoid the intrinsic ambiguity in descending motions. The objective of the motion refinement step is to adjust the motion parameters such that the epipolar constraints are valid between adjacent frames. The depth recovery step correlates adjacent frames to match pixels for triangulation. Due to the descending motion, the conventional rectification process is replaced by a set of anti-aliasing image warpings corresponding to a set of virtual parallel planes. We demonstrate experimental results on synthetic and real descent images.
A PDE Approach for Measuring Tissue Thickness
Authors: Anthony J. Yezzi, Jerry L. Prince
Venue: CVPR
Year: 2001

We outline an Eulerian framework for computing the thickness of tissues between two simply connected boundaries. Thickness is defined as the length of trajectories which follow a smooth vector field constructed in the region between the boundaries. A pair of partial differential equations (PDEs) are then solved and combined to yield length without requiring the explicit construction of the trajectories. An efficient, stable, and computationally fast solution to these PDEs is found by careful selection of finite differences according to an upwinding condition. The behavior and performance of the method is demonstrated on two simulations and two magnetic resonance imaging data sets in two and three dimensions. These experiments reveal very good performance and show strong potential for application in tissue thickness visualization and quantification.
Spin Discriminant Analysis (SDA) - Using a One-Dimensional Classifier for High Dimensional Classification Problems
Authors: Huaxin You, Hong Hua, Narendra Ahuja, Edward Y. Chang
Venue: CVPR
Year: 2001

In this paper we discuss how to use a one-dimensional classifier for solving high dimensional classification problems. We propose Spin Discriminant Analysis (SDA), which enables us to construct a family of new classifiers. We prove that SDA is equivalent to ridged Linear Discriminant Analysis (LDA) when two classes are Gaussians with common covariance matrices. Moreover, we prove that classification based on Parzen's window, is a special case of SDA. In addition to theoretical investigations, we conduct extensive empirical studies, implementing SDA using Support Vector Machines (SVMs) as its one-dimensional classifiers. This SVM-based SDA implementation is named SpinSVM. Our experiments show that SpinSVM outperforms traditional high dimensional classifiers like SVMs, Classification Using Spline (CUS), classification-based Parzen's window, and LDA on most standard and synthetic datasets we tested.
3D-Orientation Signatures with Conic Kernel Filtering for Multiple Motion Analysis
Authors: Weichuan Yu, Gerald Sommer, Kostas Daniilidis
Venue: CVPR
Year: 2001

In this paper we propose a new 3D kernel for the recovery of 3D-orientation signatures. The kernel is a Gaussian function defined in local spherical coordinates and its Cartesian support has the shape of a truncated cone with its axis in the radial direction and very small angular support. A set of such kernels is obtained by uniformly sampling the 2D space of polar and azimuth angles. The projection of a local neighborhood on such a kernel set produces a local 3D-orientation signature. In the case of spatiotemporal analysis, such a kernel set can be applied either on the derivative space of a local neighborhood or on the local Fourier transform. The well known planes arising from single or multiple motion produce maxima in the orientation signature. Due to the kernel's local support spatiotemporal signatures possess higher orientation resolution than 3D steerable filters and motion maxima can be detected and localized more accurately. We describe and show in experiments the superiority of the proposed kernels compared to Hough transformation or EM-based multiple motion detection.
Multiview Texture Models
Authors: Alexey Zalesny, Luc Van Gool
Venue: CVPR
Year: 2001

Mapping textured images on smoothly approximated surfaces is often used to conceal the loss of their real, fine-grained relief. A limitation of mapping a fixed texture in such cases is that it will only be correct for one viewing and one illumination direction. The presence of geometric surface details causes changes that simple foreshortening and global color scaling cannot model well. Hence, one would like to synthesize different textures for different viewing conditions. A texture model is presented that takes account of viewpoint dependent changes in texture appearance. It is highly compact and avoids copy-and-paste like repetitions. The model is learned from example images taken from different viewpoints. It supports texture synthesis for previously unseen conditions.
On 3D Scene Flow and Structure Estimation
Authors: Ye Zhang, Chandra Kambhamettu
Venue: CVPR
Year: 2001

In this paper, novel algorithms computing dense 3D scene flow from multiview image sequences are described. A new hierarchical rule-based stereo matching algorithm is presented to estimate the initial disparity map. Different available constraints under a multiview camera setup are investigated and then utilized in the proposed motion estimation algorithms. We show two different formulations for 3D scene flow computation. One formulation assumes that initial disparity map is accurate while the other does not make this assumption. Image segmentation information is used to maintain the motion and depth discontinuities. Iterative implementations are used to successfully compute 3D scene flow and structure at every point in the reference image. Novel hard constraints are introduced in this paper to make the algorithms more accurate and robust. Promising experimental results are seen by applying our algorithms to real imagery.
Segmentation and Tracking of Multiple Humans in Complex Situations
Authors: Tao Zhao 0001, Ramakant Nevatia, Fengjun Lv
Venue: CVPR
Year: 2001

Segmenting and tracking multiple humans is a challenging problem in complex situations in which extended occlusion, shadow and/or reflection exists. We tackle this problem with a 3D model-based approach. Our method includes two stages, segmentation (detection) and tracking. Human hypotheses are generated by shape analysis of the foreground blobs using a human shape model. The segmented human hypotheses are tracked with a Kalman filter with explicit handling of occlusion. Hypotheses are verified while being tracked for the first second or so. The verification is done by walking recognition using an articulated human walking model. We propose a new method to recognize walking using a motion template and temporal integration. Experiments show that our approach works robustly in very challenging sequences.
Small Sample Learning during Multimedia Retrieval using BiasMap
Authors: Xiang Sean Zhou, Thomas S. Huang
Venue: CVPR
Year: 2001

All positive examples are alike; each negative example is negative in its own way. During interactive multimedia information retrieval, the number of training samples fed-back by the user is usually small; furthermore, they are not representative for the true distributions-especially the negative examples. Adding to the difficulties is the nonlinearity in real-world distributions. Existing solutions fail to address these problems in a principled way. This paper proposes biased discriminant analysis and transforms specifically designed to address the asymmetry between the positive and negative examples, and to trade off generalization for robustness under a small training sample. The kernel version, namely "BiasMap ", is derived to facilitate nonlinear biased discrimination. Extensive experiments are carried out for performance evaluation as compared to the state-of-the-art methods.
Automated Cartridge Identification for Firearm Authentication
Authors: Jie Zhou 0001, Le-ping Xin, Dashan Gao 0001, Changshui Zhang, David Zhang 0001
Venue: CVPR
Year: 2001

In this paper, an algorithm of automated cartridge identification for firearm authentication is proposed. The ejector impression is used to calibrate the cartridge image. Features of the firing pin impression and the breach face impression are extracted using an active snake model and local orientation analysis, respectively. Different features are then integrated to make a final decision using a support vector machine. Experimental results illustrate the effectiveness of our algorithm.
Parametric Representations for Nonlinear Modeling of Visual Data
Authors: Ying Zhu 0006, Dorin Comaniciu, Visvanathan Ramesh, Stuart C. Schwartz
Venue: CVPR
Year: 2001

Accurate characterization of data distribution is of significant importance for vision problems. In many situations, multivariate visual data often spread into a nonlinear manifold in the high-dimensional space, which makes traditional linear modeling techniques ineffective. This paper proposes a generic nonlinear modeling scheme based on parametric data representations. We build a compact representation for the visual data using a set of parameterized basis (wavelet) functions, where the parameters are randomized to characterize the nonlinear structure of the data distribution. Meanwhile, a new progressive density approximation scheme is proposed to obtain an accurate estimate of the probability density, which imposes discrimination power on the model. Both synthetic and real image data are used to demonstrate the strength of our modeling scheme.
Tracking of Object with SVM Regression
Authors: Weiyu Zhu, Song Wang, Ruei-Sung Lin, Stephen E. Levinson
Venue: CVPR
Year: 2001

This paper presents a novel feature-matching based approach for rigid object tracking. The proposed method models the tracking problem as discovering the affine transforms of object images between frames according to the extracted feature correspondences. False feature matches (outliers) are automatically detected and removed with a new SVM regression technique, where outliers are iteratively identified as support vectors with the gradually decreased insensitive margin /spl epsi/. This method, in addition to object tracking, can also be used for general feature-based epipolar constraint estimation, in which it can quickly detect outliers even if they make up, in theory, over 50% of the whole data. We have applied the proposed method to track real objects under cluttering backgrounds with very encouraging results.
Robust Super-Resolution
Authors: Assaf Zomet, Alex Rav-Acha, Shmuel Peleg
Venue: CVPR
Year: 2001

A robust approach for super-resolution is, presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion errors, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolution algorithm. This process can increase resolution even in regions with outliers, where other super resolution methods actually degrade the image.
Envolvable Visual Commercial Detector
Authors: Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin, J. David Schaffer, Jan Nesvadba
Venue: CVPR
Year: 2003

Commercial detection plays an important role in various video segmentation and indexing applications. It provides high-level program segmentation so that other algorithms can be applied on the true program material in the broadcast. It is a challenge to have robust commercial detection methodology for various platforms, content formats, and broadcast styles that are used all over the world. Wide deployment of such an algorithm not only requires the development of new algorithms but also updating and tuning of parameters for existing algorithms. We present visual commercial detectors that rely on features including, luminance, letterbox, and keyframe distance. These detectors were developed after a careful study of the various features that can be extracted during MPEG-encoding process in real time. Due to the intermittent nature of the features, and platform restrictions, the commercial detection relies on a set of thresholds to keep the implementation as simple as possible. We evolved these thresholds using genetic algorithms (GAs) to optimize the performance. We show how a scalar genetic algorithm can locate sets of parameters in a multi-objective space (precision and recall) that outperform the values selected by an expert engineer. We present the results of optimizing a commercial detection algorithm for different data sets and parameter sets. In this paper we show that GAs drastically improved our approach and enabled fast prototyping and performance tuning of commercial detection algorithms.
Discovering Clusters in Motion Time-Series Data
Authors: Jonathan Alon, Stan Sclaroff, George Kollios, Vladimir Pavlovic 0001
Venue: CVPR
Year: 2003

An approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models (HMMs) is fitted to the motion data using the expectation maximization (EM) framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence is assigned to only a single HMM. In contrast, the formulation presented in this paper allows each sequence to belong to more than a single HMM with some probability, and the hard decision about the sequence class membership can be deferred until a later time when such a decision is required. Experiments with simulated data demonstrate the benefit of using this EM-based approach when there is more "overlap" in the processes generating the data. Experiments with real data show the promising potential of HMM-based motion clustering in a number of applications.