OpenRaiser/Intern-Atlas huggingface dataset:
Weakly Supervised Top-down Image Segmentation
Authors: Manuela Vasconcelos, Nuno Vasconcelos, Gustavo Carneiro 0001
Venue: CVPR
Year: 2006

There has recently been significant interest in top-down image segmentation methods, which incorporate the recognition of visual concepts as an intermediate step of segmentation. This work addresses the problem of top-down segmentation with weak supervision. Under this framework, learning does not require a set of manually segmented examples for each concept of interest, but simply a weakly labeled training set. This is a training set where images are annotated with a set of keywords describing their contents, but visual concepts are not explicitly segmented and no correspondence is specified between keywords and image regions. We demonstrate, both analytically and empirically, that weakly supervised segmentation is feasible when certain conditions hold. We also propose a simple weakly supervised segmentation algorithm that extends state-of-theart bottom-up segmentation methods in the direction of perceptually meaningful segmentation1.
Local Features, All Grown Up
Authors: Andrea Vedaldi, Stefano Soatto
Venue: CVPR
Year: 2006

We present a technique to adapt the domain of local features through the matching process to augment their discriminative power. We start with local affine features selected and normalized independently in training and test images, and jointly expand their domain as part of the correspondence process, akin to a (non-rigid) registration task that yields a (multi-view) segmentation of the object of interest from clutter, including the detection of occlusions. We show how our growth process can be used to validate putative affine matches, to match a given "template" (an image of an object without clutter) to a cluttered and partially occluded image, and to match two images that contain the same unknown object in different clutter under different occlusions (unsupervised object detection).
Performance Modeling and Prediction of Face Recognition Systems
Authors: Peng Wang 0005, Qiang Ji
Venue: CVPR
Year: 2006

It is a challenging task to accurately model the performance of a face recognition system, and to predict its individual recognition results under various environments. This paper presents generic methods to model and predict the face recognition performance based on analysis of similarity measurement. We first introduce a concept of "perfect recognition", which only depends on the intrinsic structure of a recognition system. A metric extracted from perfect recognition similarity scores (PRSS) allows modeling the face recognition performance without empirical testing. This paper also presents an EM algorithm to predict the recognition rate of a query set. Furthermore, features are extracted from similarity scores to predict recognition results of individual queries. The presented methods can select algorithm parameters offline, predict recognition performance online, and adjust face alignment online for better recognition. Experimental results show that the performance of recognition systems can be greatly improved using presented methods.
Hidden Conditional Random Fields for Gesture Recognition
Authors: Sy Bor Wang, Ariadna Quattoni, Louis-Philippe Morency, David Demirdjian, Trevor Darrell
Venue: CVPR
Year: 2006

We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models.
Picture Collage
Authors: Jingdong Wang 0001, Long Quan, Jian Sun 0001, Xiaoou Tang, Heung-Yeung Shum
Venue: CVPR
Year: 2006

In this paper, we address a novel problem of automatically creating a picture collage from a group of images. Picture collage is a kind of visual image summary - to arrange all input images on a given canvas, allowing overlay, to maximize visible visual information. We formulate the picture collage creation problem in a Bayesian framework. The salient regions of each image are firstly extracted and represented as a set of weighted rectangles. Then, the image arrangement is formulated as a Maximum a Posterior (MAP) problem such that the output picture collage shows as many visible salient regions (without being overlaid by others) from all images as possible. Moreover, a very efficientMarkov chain Monte Carlo (MCMC) method is designed for the optimization. Applications to desktop image browsing and image search result summarization demonstrate the effectiveness of our approach.
A Modular Approach to the Analysis and Evaluation of Particle Filters for Figure Tracking
Authors: Ping Wang 0012, James M. Rehg
Venue: CVPR
Year: 2006

This paper presents the first systematic empirical study of the particle filter (PF) algorithms for human figure tracking in video. Our analysis and evaluation follows a modular approach which is based upon the underlying statistical principles and computational concerns that govern the performance of PF algorithms. Based on our analysis, we propose a novel PF algorithm for figure tracking with superior performance called the Optimized Unscented PF. We examine the role of edge and template features, introduce computationally-equivalent sample sets, and describe a method for the automatic acquisition of reference data using standard motion capture hardware. The software and test data are made publicly-available on our project website.
3D Surface Matching and Recognition Using Conformal Geometry
Authors: Sen Wang, Yang Wang 0001, Miao Jin, Xianfeng Gu, Dimitris Samaras
Venue: CVPR
Year: 2006

3D surface matching is a fundamental issue in computer vision with many applications such as shape registration, 3D object recognition and classification. However, surface matching with noise, occlusion and clutter is a challenging problem. In this paper, we analyze a family of conformal geometric maps including harmonic maps, conformal maps and least squares conformal maps with regards to 3D surface matching. As a result, we propose a novel and computationally efficient surface matching framework that uses least squares conformal maps. According to conformal geometry theory, each 3D surface with disk topology can be mapped to a 2D domain through a global optimization and the resulting map is a diffeomorphism, i.e., one-to-one and onto. This allows us to simplify the 3D surface-matching problem to a 2D image-matching problem, by comparing the resulting 2D conformal geometric maps, which are stable, insensitive to resolution changes and robust to occlusion and noise. Therefore, highly accurate and efficient 3D surface matching algorithms can be achieved by using conformal geometric maps. Finally, the performance of conformal geometric maps is evaluated and analyzed comprehensively in 3D surface matching with occlusion, noise and resolution variation. We also provide a series of experiments on real 3D face data that achieve high recognition rates.
Semi-Supervised Classification Using Linear Neighborhood Propagation
Authors: Fei Wang 0001, Changshui Zhang, Helen C. Shen, Jingdong Wang 0001
Venue: CVPR
Year: 2006

In this paper, we address the general problem of learning from both labeled and unlabeled data. Based on the reasonable assumption that the label of each data can be linearly reconstructed from its neighbors’ labels, we develop a novel approach, called Linear Neighborhood Propagation (LNP), to learn the linear construction weights and predict the labels of the unlabeled data from the labeled data. The main procedure is composed of two steps: (1) it computes the local neighborhood geometry according to the image information by solving a constrained least squares problem, (2) the obtained geometry will be used to predict the labels of unlabeled data by solving a linearly constrained quadratic optimization problem. LNP has at least three characteristics. Firstly, it is much practical in that only one free parameter, the size of neighborhood, requires user-tuning. Secondly it can be easily extended to out-of-sample examples. Finally the labels achieved from LNP can be sufficiently smooth with respect to the intrinsic structure collectively revealed by the labeled and unlabeled points. Many promising experimental results, such as object recognition, data ranking and interactive image segmentation, demonstrate the effectiveness and efficiency of the proposed approach.
Improving Recognition of Novel Input with Similarity
Authors: Jerod J. Weinman, Erik G. Learned-Miller
Venue: CVPR
Year: 2006

Many sources of information relevant to computer vision and machine learning tasks are often underused. One example is the similarity between the elements from a novel source, such as a speaker, writer, or printed font. By comparing instances emitted by a source, we help ensure that similar instances are given the same label. Previous approaches have clustered instances prior to recognition. We propose a probabilistic framework that unifies similarity with prior identity and contextual information. By fusing information sources in a single model, we eliminate unrecoverable errors that result from processing the information in separate stages and improve overall accuracy. The framework also naturally integrates dissimilarity information, which has previously been ignored. We demonstrate with an application in printed character recognition from images of signs in natural scenes.
Combining Cues: Shape from Shading and Texture
Authors: Ryan White, David A. Forsyth
Venue: CVPR
Year: 2006

We demonstrate a method for reconstructing the shape of a deformed surface from a single view. After decomposing an image into irradiance and albedo components, we combine normal cues from shading and texture to produce a field of unambiguous normals. Using these normals, we reconstruct the 3D geometry. Our method works in two regimes: either requiring the frontal appearance of the texture or building it automatically from a series of images of the deforming texture. We can recover geometry with errors below four percent of object size on arbitrary textures, and estimate specific geometric parameters using a custom texture even more accurately.
Sparse and Semi-supervised Visual Mapping with the S3P
Authors: Oliver Williams, Andrew Blake 0001, Roberto Cipolla
Venue: CVPR
Year: 2006

This paper is about mapping images to continuous output spaces using powerful Bayesian learning techniques. A sparse, semi-supervised Gaussian process regression model (S3GP) is introduced which learns a mapping using only partially labelled training data. We show that sparsity bestows efficiency on the S3GP which requires minimal CPU utilization for real-time operation; the predictions of uncertainty made by the S3GP are more accurate than those of other models leading to considerable performance improvements when combined with a probabilistic filter; and the ability to learn from semi-supervised data simplifies the process of collecting training data. The S3GP uses a mixture of different image features: this is also shown to improve the accuracy and consistency of the mapping. A major application of this work is its use as a gaze tracking system in which images of a human eye are mapped to screen coordinates: in this capacity our approach is efficient, accurate and versatile.
Perception Strategies in Hierarchical Vision Systems
Authors: Lior Wolf, Stanley M. Bileschi, Ethan Meyers
Venue: CVPR
Year: 2006

Flat appearance-based systems, which combine clever image representations with standard classifiers, might be the most effective way to recognize objects using current technologies. In the future, however, it seems probable that hierarchical representations might have better performance. In such systems, the image representation consists of a sequence of sets of features, where each subsequent set is computed based on the previous sets. The main contributions of this paper are to: (1) pose the question "what is the best way to employ discriminative methods for hierarchical image representations?"; (2) enumerate some of the alternative hierarchies while drawing connections to recent work by brain researchers; (3) study experimentally the different alternatives. As we will show, the strategy used can make a substantial difference.
Homography from Coplanar Ellipses with Application to Forensic Blood Splatter Reconstruction
Authors: John Wright 0001, Andrew Wagner, Shankar R. Rao, Yi Ma 0001
Venue: CVPR
Year: 2006

Reconstruction of the point source of blood splatter in a crime scene is an important and difficult problem in forensic science. We study the problem of automatically reconstructing the 3-D location of the victim of a shooting from photographs of planar surfaces with blood splattered on them. We analyze this problem in terms of the multiple-view geometry of planar conic sections. Using projective invariants associated with pairs of conic sections, we match images of multiple conic sections taken from widely separated viewpoints. We further recover the homography between two views using the common tangents of pairs of conic sections. The location of the point source is then retrieved from the reconstructed scene geometry. We suggest how to extend these results to scenes containing multiple planar surfaces, and verify the proposed method with experiments on both synthetic and real images.
Coupled Bayesian Framework for Dual Energy Image Registration
Authors: Hao Wu, Yunqiang Chen, Tong Fang
Venue: CVPR
Year: 2006

Image registration for X-ray dual energy imaging is challenging due to the overlaid transparent layers (i.e., the bone and soft tissue) and the different appearances between the dual images acquired with X-rays at different energy spectra. Moreover, subpixel accuracy is necessary for good reconstruction of the bone and soft-tissue layers. This paper addresses these problems with a novel coupled Bayesian framework, in which the registration and reconstruction can effectively reinforce each other. With the reconstruction results, we can design accurate matching criteria for aligning the dual images, instead of treating them as multi-modality registration. Furthermore, prior knowledge of the bone and soft tissue can be exploited to detect poor reconstruction due to inaccurate registration; and hence correct registration errors in the coupled framework. A multiscale freeform registration algorithm is implemented to achieve subpixel registration accuracy. Promising results are obtained in the experiments.
Program Committee
Venue: CVPR
Year: 2006

Today's wireless sensor networks (WSN) focus on energy-efficiency as the main metric to optimize. However, an increasing number of scenarios where sensor networks are considered for time-critical purposes in application scenarios like intrusion detection, industrial monitoring, or health care systems demands for an explicit support of performance guarantees in WSNs and, thus, in turn for a respective mathematical framework. In (J. Schmitt and U. Roedig, 2005) , a sensor network calculus was introduced in order to accommodate a worst-case analysis of WSNs. This sensor network calculus focused on the communication aspect in WSNs, but had not yet a possibility to treat in-network processing in WSNs. In this work, we now incorporate in-network processing features as they are typical for WSNs by taking into account computational resources on the sensor nodes. Furthermore, we propose a simple, yet effective priority queue management discipline which achieves a good balance of response times across sensor nodes in the field.
Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach
Authors: Tao Xiong, Jieping Ye, Vladimir Cherkassky
Venue: CVPR
Year: 2006

Several kernel algorithms have recently been proposed for nonlinear discriminant analysis. However, these methods mainly address the singularity problem in the high dimensional feature space. Less attention has been focused on the properties of the resulting discriminant vectors and feature vectors in the reduced dimensional space. In this paper, we present a new formulation for kernel discriminant analysis. The proposed formulation includes, as special cases, kernel uncorrelated discriminant analysis (KUDA) and kernel orthogonal discriminant analysis (KODA). The feature vectors of KUDA are uncorrelated, while the discriminant vectors of KODA are orthogonal to each other in the feature space. We present theoretical derivations of proposed KUDA and KODA algorithms. The experimental results show that both KUDA and KODA are very competitive in comparison with other nonlinear discriminant algorithms in terms of classification accuracy.
A Projective Invariant for Textures
Authors: Yong Xu 0007, Hui Ji 0002, Cornelia Fermüller
Venue: CVPR
Year: 2006

Image texture analysis has received a lot of attention in the past years. Researchers have developed many texture signatures based on texture measurements, for the purpose of uniquely characterizing the texture. Existing texture signatures, in general, are not invariant to 3D transforms such as view-point changes and non-rigid deformations of the texture surface, which is a serious limitation for many applications. In this paper, we introduce a new texture signature, called the multifractal spectrum (MFS). It provides an efficient framework combining global spatial invariance and local robust measurements. The MFS is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes. Experiments demonstrate that the MFS captures the essential structure of textures with quite low dimension.
A Multi-Channel Algorithm for Edge Detection Under Varying Lighting
Authors: Wei Xu, Michael Jenkin, Yves Lespérance
Venue: CVPR
Year: 2006

In vision-based autonomous spacecraft docking multiple views of scene structure captured with the same camera and scene geometry is available under different lighting conditions. These "multiple-exposure" images must be processed to localize visual features to compute the pose of the target object. This paper describes a robust multi-channel edge detection algorithm that localizes the structure of the target object from the local gradient distribution computed over these multiple-exposure images. This approach reduces the effect of the illumination variation including the effect of shadow edges over the use of a single image. Experiments demonstrate that this approach has a lower false detection rate than the average response of the Canny edge detector applied to the individual images separately.
Rank-one Projections with Adaptive Margins for Face Recognition
Authors: Dong Xu 0001, Stephen Lin 0001, Shuicheng Yan, Xiaoou Tang
Venue: CVPR
Year: 2006

In supervised dimensionality reduction, tensor representations of images have recently been employed to enhance classification of high-dimensional data with small training sets. To handle tensor data, this approach has been formulated with tight restrictions on projection directions that, along with convergence issues and the assumption of Gaussian distributed class data, limits its face recognition performance. To overcome these problems, we propose a method of rank-one projections with adaptive margins (RPAM) that gives a provably convergent solution for tensor data over a more general class of projections, while accounting for margins between samples of different classes. In contrast to previous margin based works which determine margin sample pairs within the original high dimensional space, RPAM instead aims to maximize the margins defined in the expected lower dimensional feature subspace by progressive margin refinement after each rank-one projection. In addition to handling tensor data, vector-based variants of RPAM are presented for linear mappings and for nonlinear mappings using kernel tricks. Comprehensive experimental results demonstrate that RPAM brings significant improvement in face recognition over previous subspace learning techniques.
Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects
Authors: Jingyu Yan 0002, Marc Pollefeys
Venue: CVPR
Year: 2006

We investigate the problem of learning the structure of an articulated object, i.e. its kinematic chain, from feature trajectories under affine projections. We demonstrate this possibility by proposing an algorithm which first segments the trajectories by local sampling and spectral clustering, then builds the kinematic chain as a minimum spanning tree of a graph constructed from the segmented motion subspaces. We test our method in challenging data sets and demonstrate the ability to automatically build the kinematic chain of an articulated object from feature trajectories. The algorithm also works when there are multiple articulated objects in the scene. Furthermore, we take into account non-rigid articulated parts that exist in human motions. We believe this advance will have impact on articulated object tracking and dynamical structure from motion.
Non-rigid Image Registration Using Geometric Features and Local Salient Region Features
Authors: Jinzhong Yang, Rick S. Blum, James P. Williams 0001, Yiyong Sun, Chenyang Xu 0001
Venue: CVPR
Year: 2006

We present a novel feature-based non-rigid image registration algorithm using a small number of automatically extracted points and their associated local salient region features. Our automatic registration is a hybrid approach co-optimizing point-based and image-based terms. Motivated by the paradigm of the TPS-RPM algorithm [6], we develop the RHDM (Robust Hybrid Deformable Matching) algorithm by alternatively optimizing correspondences and transformations for registration. The local salient region features and the geometric features, together with the softassign and deterministic annealing techniques, are used for solving correspondences. Thin-plate splines are used for generating a smooth non-rigid spatial transformation. Our algorithm is built to be extremely robust to feature extraction errors. A new dynamic outlier rejection mechanism is described for rejecting outliers and generating accurate spatial mappings. A local refinement technique is used for correcting non-exactly matched correspondences arising from image noise and irregular deformations. In contrast with the TPS-RPM algorithm, which can handle only outliers in one point set, our algorithm is able to handle a considerable number of outliers in both point sets. The experimental results demonstrate the robustness and accuracy of our algorithm.