OpenRaiser/Intern-Atlas huggingface dataset:
Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction
Authors: Jianhui Chen, Jieping Ye, Qi Li 0001
Venue: CVPR
Year: 2007

Linear discriminant analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximizing the between-class distance and minimizing the within-class distance. However, local geometric structure has recently been shown to be effective for dimensionality reduction. In this paper, a novel dimensionality reduction algorithm is proposed, which integrates both global and local structures. The main contributions of this paper include: (1) We present a least squares formulation for dimensionality reduction, which facilities the integration of global and local structures; (2) We design an efficient model selection scheme for the optimal integration, which balances the tradeoff between the global and local structures; and (3) We present a detailed theoretical analysis on the intrinsic relationship between the proposed framework and LDA. Our extensive experimental studies on benchmark data sets show that the proposed integration framework is competitive with traditional dimensionality reduction algorithms, which use global or local structure only.
Capturing long-range correlations with patch models
Authors: Vincent Cheung, Nebojsa Jojic, Dimitris Samaras
Venue: CVPR
Year: 2007

The use of image patches to capture local correlations between pixels has been growing in popularity for use in various low-level vision tasks. There is a trade-off between using larger patches to obtain additional high-order statistics and smaller patches to capture only the elemental features of the image. Previous work has leveraged short-range correlations between patches that share pixel values for use in patch matching. In this paper, long-range correlations between patches are introduced, where relations between patches that do not necessarily share pixels are learnt. Such correlations arise as an inherent property of the data itself. These long-range patch correlations are shown to be particularly important for video sequences where the patches have an additional time dimension, with correlation links in both space and time. We illustrate the power of our model on tasks such as multiple object registration and detection and missing data interpolation, including a difficult task of photograph relighting, where a single photograph is assumed to be the only observed part of a 3D volume whose two coordinates are the image 𝓍 and 𝒴 coordinates and the third coordinate is the illumination angle θ. We show that in some cases, the long-range correlations observed among the mappings of different volume patches in a small training set are sufficient to infer the possible complex intensity changes in a new photograph due to illumination angle variation.
An Exemplar Model for Learning Object Classes
Authors: Ondrej Chum, Andrew Zisserman
Venue: CVPR
Year: 2007

We introduce an exemplar model that can learn and generate a region of interest around class instances in a training set, given only a set of images containing the visual class. The model is scale and translation invariant. In the training phase, image regions that optimize an objective function are automatically located in the training images, without requiring any user annotation such as bounding boxes. The objective function measures visual similarity between training image pairs, using the spatial distribution of both appearance patches and edges. The optimization is initialized using discriminative features. The model enables the detection (localization) of multiple instances of the object class in test images, and can be used as a precursor to training other visual models that require bounding box annotation. The detection performance of the model is assessed on the PASCAL Visual Object Classes Challenge 2006 test set. For a number of object classes the performance far exceeds the current state of the art of fully supervised methods.
Canonical Face Depth Map: A Robust 3D Representation for Face Verification
Authors: Dirk Colbry, George C. Stockman
Venue: CVPR
Year: 2007

The canonical face depth map (CFDM) is a standardized representation for storing and manipulating 3D data from human faces. Our algorithm automates the process of transforming a 3D face scan into its canonical representation, eliminating the need for hand-labeled anchor points. The presented algorithm is designed to be a robust, fully automatic preprocessor for any 3D face recognition algorithm. The experimental results presented here demonstrate that our CFDM is robust to noise and occlusion, and we show that using such a canonical representation can improve the efficiency efface recognition algorithms and reduce memory requirements. Producing the CFDM takes, on average, 0.85 seconds for 320 times 240 pixel scans, and 3.8 seconds for 640 times 480 pixel scans (using a dual AMD Opteron 275, 2.2 GHz, with 2 MB Cache, and 1 GIG RAM). The CFDM enables both 2D and 3D image processing methods - such as convolution and PCA - to be readily used for feature localization and face recognition.
DigiTable: an interactive multiuser table for collocated and remote collaboration enabling remote gesture visualization
Authors: François Coldefy, Stéphane Louis Dit Picard
Venue: CVPR
Year: 2007

We present DlGlTABLE, an experimental platform we hope lessen the gap between co-present and distant interaction. DlGlTABLE is combining a multiuser tactile interactive tabletop, a video-communication system enabling eye-contact with real size distant user visualization and a spatialized sound system for speech transmission. Based on a robust computer vision module, it provides a fluid gesture visualization of each distant participant whether he/she is moving virtual digital objects or is intending to do so. Remote gesture visualization contributes to the efficiency of distant collaboration tasks because it enables the coordination among participant's actions and talk. Our main contribution addresses the development and the integration of robust and real time projector-camera processing techniques in computer supported cooperative work.
Recognizing objects by piecing together the Segmentation Puzzle
Authors: Timothée Cour, Jianbo Shi
Venue: CVPR
Year: 2007

We present an algorithm that recognizes objects of a given category using a small number of hand segmented images as references. Our method first over segments an input image into superpixels, and then finds a shortlist of optimal combinations of superpixels that best fit one of template parts, under affine transformations. Second, we develop a contextual interpretation of the parts, gluing image segments using top-down fiducial points, and checking overall shape similarity. In contrast to previous work, the search for candidate superpixel combinations is not exponential in the number of segments, and in fact leads to a very efficient detection scheme. Both the storage and the detection of templates only require space and time proportional to the length of the template boundary, allowing us to store potentially millions of templates, and to detect a template anywhere in a large image in roughly 0.01 seconds. We apply our algorithm on the Weizmann horse database, and show our method is comparable to the state of the art while offering a simpler and more efficient alternative compared to previous work.
Nonlinear Dynamical Shape Priors for Level Set Segmentation
Authors: Daniel Cremers
Venue: CVPR
Year: 2007

The introduction of statistical shape knowledge into level set based segmentation methods was shown to improve the segmentation of familiar structures in the presence of noise, clutter or partial occlusions. While most work has been focused on shape priors which are constant in time, it is clear that when tracking deformable shapes certain silhouettes may become more or less likely over time. In fact, the deformations of familiar objects such as the silhouettes of a walking person are often characterized by pronounced temporal correlations. In this paper, we propose a nonlinear dynamical shape prior for level set based image segmentation. Specifically, we propose to approximate the temporal evolution of the eigenmodes of the level set function by means of a mixture of autoregressive models. We detail how such shape priors "with memory" can be integrated into a variational framework for level set segmentation. As an application, we experimentally validate that the nonlinear dynamical prior drastically improves the tracking of a person walking in different directions, despite large amounts of clutter and noise.
A Sequential Monte Carlo Approach to Anomaly Detection in Tracking Visual Events
Authors: Peng Cui 0001, Lifeng Sun, Zhi-Qiang Liu, Shiqiang Yang
Venue: CVPR
Year: 2007

In this paper we propose a technique to detect anomalies in individual and interactive event sequences. We categorize anomalies into two classes: abnormal event, and abnormal context, and model them in the Sequential Monte Carlo framework which is extended by Markov Random Field for tracking interactive events. Firstly, we propose a novel pixel-wise event representation method to construct feature images, in which each blob corresponds to a visual event. Then we transform the original blob-level features into subspaces to model probabilistic appearance manifolds for each event-class. With the probability of an observation associated with each event-class (or state) derived from probabilistic manifolds, and state transitional probability, the prior and posterior state distributions can be estimated. We demonstrate in experiments that the approach can reliably detect such anomalies with low false alarm rates.
Epitomic Representation of Human Activities
Authors: Naresh P. Cuntoor, Rama Chellappa
Venue: CVPR
Year: 2007

We introduce an epitomic representation for modeling human activities in video sequences. A video sequence is divided into segments within which the dynamics of objects is assumed to be linear and modeled using linear dynamical systems. The tuple consisting of the estimated system matrix, statistics of the input signal and the initial state value is said to form an epitome. The system matrices are decomposed using the Iwasawa matrix decomposition to isolate the effect of rotation, scaling and projective action on the state vector. "We demonstrate the usefulness of the proposed representation and decomposition for activity recognition using the TSA airport surveillance dataset and the UCF indoor human action dataset.
Detector Ensemble
Authors: Shengyang Dai, Ming Yang 0007, Ying Wu 0001, Aggelos K. Katsaggelos
Venue: CVPR
Year: 2007

Component-based detection methods have demonstrated their promise by integrating a set of part-detectors to deal with large appearance variations of the target. However, an essential and critical issue, i.e., how to handle the imperfectness of part-detectors in the integration, is not well addressed in the literature. This paper proposes a detector ensemble model that consists of a set of substructure-detectors, each of which is composed of several part-detectors. Two important issues are studied both in theory and in practice, (1) finding an optimal detector ensemble, and (2) detecting targets based on an ensemble. Based on some theoretical analysis, a new model selection strategy is proposed to learn an optimal detector ensemble that has a minimum number of false positives and satisfies the design requirement on the capacity of tolerating missing parts. In addition, this paper also links ensemble-based detection to the inference in Markov random field, and shows that the target detection can be done by a max-product belief propagation algorithm.
A Fast 3D Correspondence Method for Statistical Shape Modeling
Authors: Pahal Dalal, Brent C. Munsell, Song Wang 0002, Jijun Tang, Kenton Oliver, Hiroaki Ninomiya, Xiangrong Zhou, Hiroshi Fujita 0001
Venue: CVPR
Year: 2007

Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In this paper, we address this landmark-based shape-correspondence problem for 3D cases by developing a highly efficient landmark-sliding algorithm. This algorithm is able to quickly refine all the landmarks in a parallel fashion by sliding them on the 3D shape surfaces. We use 3D thin-plate splines to model the shape-correspondence error so that the proposed algorithm is invariant to affine transformations and more accurately reflects the nonrigid biological shape deformations between different shape instances. In addition, the proposed algorithm can handle both open-and closed-surface shape, while most of the current 3D shape-correspondence methods can only handle genus-0 closed surfaces. We conduct experiments on 3D hippocampus data and compare the performance of the proposed algorithm to the state-of-the-art MDL and SPHARM methods. We find that, while the proposed algorithm produces a shape correspondence with a better or comparable quality to the other two, it takes substantially less CPU time. We also apply the proposed algorithm to correspond 3D diaphragm data which have an open-surface shape.
Realizing Super-Resolution with Superimposed Projection
Authors: Niranjan Damera-Venkata, Nelson L. Chang
Venue: CVPR
Year: 2007

We consider the problem of rendering high-resolution images on a display composed of multiple superimposed lower-resolution projectors. A theoretical analysis of this problem in the literature previously concluded that the multi-projector superimposition of low resolution projectors cannot produce high resolution images. In our recent work, we showed to the contrary that super-resolution via multiple superimposed projectors is indeed theoretically achievable. This paper derives practical algorithms for real multi-projector systems that account for the intra- and inter-projector variations and that render high-quality, high-resolution content at real-time interactive frame rates. A camera is used to estimate the geometric, photometric, and color properties of each component projector in a calibration step. Given this parameter information, we demonstrate novel methods for efficiently generating optimal sub-frames so that the resulting projected image is as close as possible to the given high resolution images.
On-line Simultaneous Learning and Tracking of Visual Feature Graphs
Authors: Arnaud Declercq, Justus H. Piater
Venue: CVPR
Year: 2007

Model learning and tracking are two important topics in computer vision. While there are many applications where one of them is used to support the other, there are currently only few where both aid each other simultaneously. In this work, we seek to incrementally learn a graphical model from tracking and to simultaneously use whatever has been learned to improve the tracking in the next frames. The main problem encountered in this situation is that the current intermediate model may be inconsistent with future observations, creating a bias in the tracking results. We propose an uncertain model that explicitly accounts for such uncertainties by representing relations by an appropriately weighted sum of informative (parametric) and uninformative (uniform) components. The method is completely unsupervised and operates in real time.
Detecting Specular Surfaces on Natural Images
Authors: Andrey DelPozo, Silvio Savarese
Venue: CVPR
Year: 2007

Recognizing and localizing specular (or mirror-like) surfaces from a single image is a great challenge to computer vision. Unlike other materials, the appearance of a specular surface changes as function of the surrounding environment as well as the position of the observer. Even though the reflection on a specular surface has an intrinsic ambiguity that might be resolved by high level reasoning, we argue that we can take advantage of low level features to recognize specular surfaces. This intuition stems from the observation that the surrounding scene is highly distorted when reflected off regions of high curvature or occluding contours. We call these features static specular flows (SSF). We show how to characterize SSF and use them for identifying specular surfaces. To evaluate our result we collect a dataset of 120 images containing specular surfaces. Our algorithm can achieve good performances on this challenging dataset. Particularly, our results outperform other methods that follow a more naive approach.
Incorporating On-demand Stereo for Real Time Recognition
Authors: Thomas Deselaers, Antonio Criminisi, John M. Winn, Ankur Agarwal
Venue: CVPR
Year: 2007

A new method for localising and recognising hand poses and objects in real-time is presented. This problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real objects when interacting with a machine. Examples include using a real eraser to remove words from a document displayed on an electronic surface. In this paper the task of simultaneously recognising object classes, hand gestures and detecting touch events is cast as a single classification problem. A random forest algorithm is employed which adaptively selects and combines a minimal set of appearance, shape and stereo features to achieve maximum class discrimination for a given image. This minimal set leads to both efficiency at run time and good generalisation. Unlike previous stereo works which explicitly construct disparity maps, here the stereo matching costs are used directly as visual cue and only computed on-demand, i.e. only for pixels where they are necessary for recognition. This leads to improved efficiency. The proposed method is assessed on a database of a variety of objects and hand poses selected for interacting on a flat surface in an office environment.
Application of the Reeb Graph Technique to Vehicle Occupant's Head Detection in Low-resolution Range Images
Authors: Pandu Ranga Rao Devarakota, Marta Castillo-Franco, Romuald Ginhoux, Bruno Mirbach, Björn E. Ottersten
Venue: CVPR
Year: 2007

In [3], a low-resolution range sensor was investigated for an occupant classification system that distinguish person from child seats or an empty seat. The optimal deployment of vehicle airbags for maximum protection moreover requires information about the occupant's size and position. The detection of occupant's position involves the detection and localization of occupant's head. This is a challenging problem as the approaches based on local shape analysis (in 2D or 3D) alone are not robust enough as other parts of the person's body like shoulders, knee may have similar shapes as the head. This paper discusses and investigate the potential of a Reeb graph approach to describe the topology of vehicle occupants in terms of a skeleton. The essence of the proposed approach is that an occupant sitting in a vehicle has a typical topology which leads to different branches of a Reeb Graph and the possible location of the occupant's head are thus the end points of the Reeb graph. The proposed method is applied on real 3D range images and is compared to Ground truth information. Results show the feasibility of using topological information to identify the position of occupant's head.
Deformable Motion Tracking of Cardiac Structures (DEMOTRACS) for Improved MR Imaging
Authors: Maneesh Dewan, Christine H. Lorenz, Gregory D. Hager
Venue: CVPR
Year: 2007

The speed and quality of imaging cardiac structures (coronary arteries, cardiac valves etc) in MR can be improved by tracking and predicting their motion in MR images. The problem is challenging not only due to the complex motion of these structures that significantly changes the appearance of the region of interest, but also the ability to track at different spatial and temporal resolutions depending on the application. We have developed a multiple-template based tracking approach to track the cardiac structures in MR images. The algorithm has two novel features. First a bidirectional coordinate-descent algorithm is derived to improve accuracy and performance of tracking. Second we propose a method for choosing an optimal set of templates for tracking. The efficacy of the algorithm has been validated by tracking the coronary artery and cardiac valves reliably and accurately in thousands of high resolution cine and low-re solution real-time MR images.
Groupwise Shape Registration on Raw Edge Sequence via A Spatio-Temporal Generative Model
Authors: Huijun Di, Rao Naveed Iqbal, Guangyou Xu, Linmi Tao
Venue: CVPR
Year: 2007

Groupwise shape registration of raw edge sequence is addressed. Automatically extracted edge maps are treated as noised input shape of the deformable object and their registration are considered, results can be used to build statistical shape models without laborious manual labeling process. Dealing with raw edges poses several challenges, to fight against them a novel spatio-temporal generative model is proposed which joints shape registration and trajectory tracking. Mean shape, consistent correspondences among edge sequence and associated non-rigid transformations are jointly inferred under EM framework. Our algorithm is tested on real video sequences of a dancing ballerina, talking face, and walking person. Results achieved are interesting, promising, and prove the robustness of our method. Potential applications can be found in statistical shape analysis, action recognition, object tracking, etc.
Segmental Hidden Markov Models for View-based Sport Video Analysis
Authors: Yi Ding, Guoliang Fan
Venue: CVPR
Year: 2007

We present a generative model approach to explore intrinsic semantic structures in sport videos, e.g., the camera view in American football games. We will invoke the concept of semantic space to explicitly define the semantic structure in the video in terms of latent states. A dynamic model is used to govern the transition between states, and an observation model is developed to characterize visual features pertaining to different states. Then the problem is formulated as a statistical inference process where we want to infer latent states (i.e., camera views) from observations (i.e., visual features). Two generative models, the hidden Markov model (HMM) and the Segmental HMM (SHMM), are involved in this research. In the HMM, both latent states and visual features are shot-based, and in the SHMM, latent states and visual features are defined for shots and frames respectively. Both models provide promising performance for view-based shot classification, and the SHMM outperforms the HMM by involving a two-layer observation model to accommodate the variability of visual features. This approach is also applicable to other video mining tasks.
Feature Mining for Image Classification
Authors: Piotr Dollár, Zhuowen Tu, Hai Tao, Serge J. Belongie
Venue: CVPR
Year: 2007

The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).