OpenRaiser/Intern-Atlas huggingface dataset:
Learning the Compositional Nature of Visual Objects
Authors: Björn Ommer, Joachim M. Buhmann
Venue: CVPR
Year: 2007

The compositional nature of visual objects significantly limits their representation complexity and renders learning of structured object models tractable. Adopting this modeling strategy we both (i) automatically decompose objects into a hierarchy of relevant compositions and we (ii) learn such a compositional representation for each category without supervision. The compositional structure supports feature sharing already on the lowest level of small image patches. Compositions are represented as probability distributions over their constituent parts and the relations between them. The global shape of objects is captured by a graphical model which combines all compositions. Inference based on the underlying statistical model is then employed to obtain a category level object recognition system. Experiments on large standard benchmark datasets underline the competitive recognition performance of this approach and they provide insights into the learned compositional structure of objects.
Visual Odometry System Using Multiple Stereo Cameras and Inertial Measurement Unit
Authors: Taragay Oskiper, Zhiwei Zhu, Supun Samarasekera, Rakesh Kumar 0001
Venue: CVPR
Year: 2007

Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU). This algorithm can run in real-time with 15 Hz update rate on a dual-core 2 GHz laptop PC and it is designed to be a highly accurate local (relative) pose estimation mechanism acting as the front-end to a simultaneous localization and mapping (SLAM) type method capable of global corrections through landmark matching. Extensive tests of our prototype system so far, reveal that without any global landmark matching, we achieve between 0.5% and 1% accuracy in localizing a person over a 500 meter travel indoors and outdoors. To our knowledge, such performance results with a real time system have not been reported before.
Focal Pre-Correction of Projected Image for Deblurring Screen Image
Authors: Yuji Oyamada, Hideo Saito 0001
Venue: CVPR
Year: 2007

We propose a method for reducing out-of-focus blur caused by projector projection. In this method, we estimate the Point-Spread-Function (PSF) of the out-of-focus blur in the image projected onto the screen by comparing the screen image captured by a camera with the original image projected by the projector. According to the estimated PSF, the projected image is pre-corrected, so that the screen image can be deblurred. Experimental results show that our method can reduce out-of-focus projection blur.
Robust Occlusion Handling in Object Tracking
Authors: Jiyan Pan, Bo Hu 0002
Venue: CVPR
Year: 2007

In object tracking, occlusions significantly undermine the performance of tracking algorithms. Unlike the existing methods that solely depend on the observed target appearance to detect occluders, we propose an algorithm that progressively analyzes the occlusion situation by exploiting the spatiotemporal context information, which is further double checked by the reference target and motion constraints. This strategy enables our proposed algorithm to make a clearer distinction between the target and occluders than existing approaches. To further improve the tracking performance, we rectify the occlusion-interfered erroneous target location by employing a variant-mask template matching operation. As a result, correct target location can always be obtained regardless of the occlusion situation. Using these techniques, the robustness of tracking under occlusions is significantly promoted. Experimental results have confirmed the effectiveness of our proposed algorithm.
Part-based Face Recognition Using Near Infrared Images
Authors: Ke Pan, Shengcai Liao, Zhijian Zhang, Stan Z. Li, Peiren Zhang
Venue: CVPR
Year: 2007

Recently, the authors developed NIR based face recognition for highly accurate face recognition under illumination variations. In this paper, we present a part-based method for improving its robustness with respect to pose variations. An NIR face is decomposed into parts. A part classifier is built for each part, using the most discriminative LBP histogram features selected by AdaBoost learning. The outputs of part classifiers are fused to give the final score. Experiments show that the present method outperforms the whole face-based method by 4.53%.
Unsupervised Learning of Hierarchical Semantics of Objects (hSOs)
Authors: Devi Parikh, Tsuhan Chen
Venue: CVPR
Year: 2007

A successful representation of objects in the literature is as a collection of patches, or parts, with a certain appearance and position. The relative locations of the different parts of an object are constrained by the geometry of the object. Going beyond the patches on a single object, consider a collection of images of a particular class of scenes containing multiple (recurring) objects. The parts belonging to different objects are not constrained by such a geometry. However the objects, arguably due to their semantic relationships, themselves demonstrate a pattern in their relative locations, which also propagates to their parts. Analyzing the interactions between the parts across the collection of images would reflect these patterns, and the parts can be grouped accordingly. These groupings are typically hierarchical. We introduce hSO: Hierarchical Semantics of Objects, which is learnt from a collection of images of a particular scene and captures this hierarchical grouping. We propose an approach for the unsupervised learning of the hSO. The hSO simply holds objects, as clusters of patches, at its nodes, but it goes much beyond that and also captures interactions between the objects through its structure. In addition to providing the semantic layout of the scene, learnt hSOs can have several useful applications such as providing context for enhanced object detection and compact scene representation for scene category classification.
Face Recognition in Video: Adaptive Fusion of Multiple Matchers
Authors: Unsang Park, Anil K. Jain 0001, Arun Ross
Venue: CVPR
Year: 2007

Face recognition in video is being actively studied as a covert method of human identification in surveillance systems. Identifying human faces in video is a difficult problem due to the presence of large variations in facial pose and lighting, and poor image resolution. However, by taking advantage of the diversity of the information contained in video, the performance of a face recognition system can be enhanced. In this work we explore (a) the adaptive use of multiple face matchers in order to enhance the performance of face recognition in video, and (b) the possibility of appropriately populating the database (gallery) in order to succinctly capture intra class variations. To extract the dynamic information in video, the facial poses in various frames are explicitly estimated using active appearance model (AAM) and a factorization based 3D face reconstruction technique. We also estimate the motion blur using discrete cosine transformation (DCT). Our experimental results on 204 subjects in CMU's face-in-action (FIA) database show that the proposed recognition method provides consistent improvements in the matching performance using three different face matchers.
Improving Iris Identification using User Quality and Cohort Information
Authors: Arun Passi, Ajay Kumar 0001
Venue: CVPR
Year: 2007

Iris is one of the most distinguishable features of a human body, which remains fairly stable throughout the lifetime of an individual. This makes iris recognition one of the most reliable methods for biometric based identification. This paper investigates a new technique to improve the performance of the system by using cohort information and user-quality as the weight in the matching. The proposed approach uses the cohort information at the decision stage as cascaded classifiers. However, the second stage is only used if the first stage classifier is uncertain of its decision. The experimental results from the decision-level classifiers combination are presented, which show that the cascaded classification system significantly outperforms the single classifier, especially at lower value of FAR which is most likely to be the operating point for any system. This paper also proposes a new approach to ascertain the user-quality (iris) and illustrates its usage in the performance improvement.
Regression tracking with data relevance determination
Authors: Ioannis Patras, Edwin R. Hancock
Venue: CVPR
Year: 2007

This paper addresses the problem of efficient visual 2D template tracking in image sequences. We adopt a discriminative approach in which the observations at each frame yield direct predictions of a parametrisation of the state (e.g. position/scale/rotation) of the tracked target. To this end, a Bayesian mixture of experts (BME) is trained on a dataset of image patches that are generated by applying artificial transformations to the template at the first frame. In contrast to other methods in the literature, we explicitly address the problem that the prediction accuracy can deteriorate drastically for observations that are not similar to the ones in the training set; such observations are common in case of partial occlusions or of fast motion. To do so, we couple the BME with a probabilistic kernel-based classifier which, when trained, can determine the probability that a new/unseen observation can accurately predict the state of the target (the 'relevance' of the observation in question). In addition, in the particle filtering framework, we derive a recursive scheme for maintaining an approximation of the posterior probability of the target's state in which the probabilistic predictions of multiple observations are moderated by their corresponding relevance. We apply the algorithm in the problem of 2D template tracking and demonstrate that the proposed scheme outperforms classical methods for discriminative tracking in case of motions large in magnitude and of partial occlusions.
A Quasi-Minimal Model for Paper-Like Surfaces
Authors: Mathieu Perriollat, Adrien Bartoli
Venue: CVPR
Year: 2007

Smoothly bent paper-like surfaces are developable. They are however difficult to minimally parameterize since the number of meaningful parameters is intrinsically dependent on the actual deformation. Previous generative models are either incomplete, i.e. limited to subsets of developable surfaces, or depend on huge parameter sets. We propose a generative model governed by a quasi-minimal set of intuitive parameters, namely rules and angles. More precisely, a flat mesh is bent along guiding rules, while a number of extra rules controls the level of smoothness. The generated surface is guaranteed to be developable. A fully automatic multi-camera three dimensional reconstruction algorithm, including model-based bundle-adjustment, demonstrates our model on real images.
Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis
Authors: Patrick Peursum, Svetha Venkatesh, Geoff A. W. West
Venue: CVPR
Year: 2007

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model. The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.
Online Learning Asymmetric Boosted Classifiers for Object Detection
Authors: Minh-Tri Pham, Tat-Jen Cham
Venue: CVPR
Year: 2007

We present an integrated framework for learning asymmetric boosted classifiers and online learning to address the problem of online learning asymmetric boosted classifiers, which is applicable to object detection problems. In particular, our method seeks to balance the skewness of the labels presented to the weak classifiers, allowing them to be trained more equally. In online learning, we introduce an extra constraint when propagating the weights of the data points from one weak classifier to another, allowing the algorithm to converge faster. In compared with the Online Boosting algorithm recently applied to object detection problems, we observed about 0-10% increase in accuracy, and about 5-30% gain in learning speed.
Real-Time Posture Analysis in a Crowd using Thermal Imaging
Authors: Quoc Cuong Pham, Laetitia Gond, Julien Begard, Nicolas Allezard, Patrick Sayd
Venue: CVPR
Year: 2007

This article describes a video-surveillance system developed within the ISCAPS project. Thermal imaging provides a robust solution to visibility change (illumination, smoke) and is a relevant technology for discriminating humans in complex scenes. In this article, we demonstrate its efficiency for posture analysis in dense groups of people. The objective is to automatically detect several persons lying down in a very crowded area. The presented method is based on the detection and segmentation of individuals within groups of people using a combination of several weak classifiers. The classification of extracted silhouettes enables to detect abnormal situations. This approach was successfully applied to the detection of terrorist gas attacks on railway platform and experimentally validated in the project. Some of the results are presented here.
Object retrieval with large vocabularies and fast spatial matching
Authors: James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, Andrew Zisserman
Venue: CVPR
Year: 2007

In this paper, we present a large-scale object retrieval system. The user supplies a query object by selecting a region of a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large corpus. We demonstrate the scalability and performance of our system on a dataset of over 1 million images crawled from the photo-sharing site, Flickr [3], using Oxford landmarks as queries. Building an image-feature vocabulary is a major time and performance bottleneck, due to the size of our dataset. To address this problem we compare different scalable methods for building a vocabulary and introduce a novel quantization method based on randomized trees which we show outperforms the current state-of-the-art on an extensive ground-truth. Our experiments show that the quantization has a major effect on retrieval quality. To further improve query performance, we add an efficient spatial verification stage to re-rank the results returned from our bag-of-words model and show that this consistently improves search quality, though by less of a margin when the visual vocabulary is large. We view this work as a promising step towards much larger, "web-scale" image corpora.
An end-to-end eChronicling System for Mobile Human Surveillance
Authors: Gopal Pingali, Ying-li Tian, Shahram Ebadollahi, Jason W. Pelecanos, Mark Podlaseck, Harry Stavropoulos
Venue: CVPR
Year: 2007

Rapid advances in mobile computing devices and sensor technologies are enabling the capture of unprecedented volumes of data by individuals involved in field operations in a variety of applications. As capture becomes ever more rich and pervasive the biggest challenge is in developing information processing and representation tools that maximize the utility of the captured multi-sensory data. The right tools hold the promise of converting captured data into actionable intelligence resulting in improved memory, enhanced situational understanding, and more efficient execution of operations. These tools need to be at least as rich and diverse as the sensors used for capture, and need to be unified within an effective system architecture. This paper presents our initial attempt at such a system and architecture that combines several emerging sensor technologies, state of the art analytic engines, and multi-dimensional navigation tools, into an end-to-end electronic chronicling solution for mobile surveillance by humans.
Mumford-Shah Meets Stereo: Integration of Weak Depth Hypotheses
Authors: Thomas Pock, Christopher Zach, Horst Bischof
Venue: CVPR
Year: 2007

Recent results on stereo indicate that an accurate segmentation is crucial for obtaining faithful depth maps. Variational methods have successfully been applied to both image segmentation and computational stereo. In this paper we propose a combination in a unified framework. In particular, we use a Mumford-Shah-like functional to compute a piecewise smooth depth map of a stereo pair. Our approach has two novel features: First, the regularization term of the functional combines edge information obtained from the color segmentation with flow-driven depth discontinuities emerging during the optimization procedure. Second, we propose a robust data term which adoptively selects the best matches obtained from different weak stereo algorithms. We integrate these features in a theoretically consistent framework. The final depth map is the minimizer of the energy functional, which can be solved by the associated functional derivatives. The underlying numerical scheme allows an efficient implementation on modern graphics hardware. We illustrate the performance of our algorithm using the Middlebury database as well as on real imagery.
Change Detection in a 3-d World
Authors: Thomas B. Pollard, Joseph L. Mundy
Venue: CVPR
Year: 2007

This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence. To the authors' knowledge, this paper is the first to address the change detection problem in such a general framework. Existing change detection algorithms that exploit multiple image viewpoints typically can detect only motion changes or assume a planar world geometry which cannot cope effectively with appearance changes due to occlusion and un-modeled 3-d scene geometry (ego-motion parallax). The approach presented here can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. The probability distributions at each voxel are continuously updated as new images are received. The key question of convergence of this joint estimation problem is answered by a formal proof based on realistic assumptions about the nature of real world scenes. A series of experiments are presented that evaluate change detection accuracy under laboratory-controlled conditions as well as aerial reconnaissance scenarios.-
On the Efficacy of Correcting for Refractive Effects in Iris Recognition
Authors: Jeffery R. Price, Timothy F. Gee, Vincent C. Paquit, Kenneth W. Tobin
Venue: CVPR
Year: 2007

In this study, we aim to determine if iris recognition accuracy might be improved by correcting for the refractive effects of the human eye when the optical axes of the eye and camera are misaligned. We undertake this investigation using an anatomically-approximated, three-dimensional model of the human eye and ray-tracing. We generate synthetic iris imagery from different viewing angles using first a simple pattern of concentric rings on the iris for analysis, and then synthetic texture maps on the iris for experimentation. We estimate the distortion from the concentric-ring iris images and use the results to guide the sampling of textured iris images that are distorted by refraction. Using the well-known Gabor filter phase quantization approach, our model-based results indicate that the Hamming distances between iris signatures from different viewing angles can be significantly reduced by accounting for refraction. Over our experimental conditions comprising viewing angles from 0 to 60 degrees, we observe a median reduction in Hamming distance of 27.4% and a maximum reduction of 70.0% when we compensate for refraction. Maximum improvements are observed at viewing angles o/20deg-25deg.
Offline Signature Verification Using Online Handwriting Registration
Authors: Yu Qiao 0001, Jianzhuang Liu, Xiaoou Tang
Venue: CVPR
Year: 2007

This paper proposes a novel framework for offline signature verification. Different from previous methods, our approach makes use of online handwriting instead of handwritten images for registration. The online registrations enable robust recovery of the writing trajectory from an input offline signature and thus allow effective shape matching between registration and verification signatures. In addition, we propose several new techniques to improve the performance of the new signature verification system: 1. we formulate and solve the recovery of writing trajectory within the framework of conditional random fields; 2. we propose a new shape descriptor, online context, for aligning signatures; 3. we develop a verification criterion which combines the duration and amplitude variances of handwriting. Experiments on a benchmark database show that the proposed method significantly outperforms the well-known offline signature verification methods and achieve comparable performance with online signature verification methods.
Surveillance in Virtual Reality: System Design and Multi-Camera Control
Authors: Faisal Z. Qureshi, Demetri Terzopoulos
Venue: CVPR
Year: 2007

This paper advocates a virtual vision paradigm and demonstrates its usefulness in camera sensor network research. Virtual vision prescribes the use of a visually and behaviorally realistic virtual environment simulator in the design and evaluation of surveillance systems. Impediments to deploying and experimenting with appropriately complex camera networks makes virtual vision an attractive alternative for many vision researchers who are motivated to investigate high level multi-camera control issues within such networks. In particular, we present two prototype surveillance systems comprising passive and active pan/tilt/zoom cameras. We deploy these systems in a virtual train station environment populated by autonomous, lifelike virtual pedestrians. The easily reconfigurable virtual cameras situated throughout this environment generate synthetic video feeds that emulate those acquired by real surveillance cameras monitoring extensive public spaces. Our novel multi-camera control strategies enable the cameras to collaborate in persistently observing pedestrians of interest that move across their fields of view and in capturing close-up videos of pedestrians as they travel through designated areas. The sensor networks support task-dependent camera node selection and aggregation through local decision-making and inter-node communication. Our approach to multi-camera control is robust to node failures and message loss.