OpenRaiser/Intern-Atlas huggingface dataset:
Tracking Multiple Humans in Crowded Environment
Authors: Tao Zhao 0001, Ramakant Nevatia
Venue: CVPR
Year: 2004

Tracking of humans in dynamic scenes has been an important topic of research. Most techniques, however, are limited to situations where humans appear isolated and occlusion is small. Typical methods rely on appearance models that must be acquired when the humans enter the scene and are not occluded. We present a method that can track humans in crowded environments, with significant and persistent occlusion by making use of human shape models in addition to camera models, the assumption that humans walk on a plane and acquired appearance models. Experimental results and a quantitative evaluation are included.
Alignment of Continuous Video onto 3D Point Clouds
Authors: Wen-Yi Zhao, David Nistér, Steven C. Hsu
Venue: CVPR
Year: 2004

We propose a general framework for aligning continuous (oblique) video onto 3D sensor data. We align a point cloud computed from the video onto the point cloud directly obtained from a 3D sensor. This is in contrast to existing techniques where the 2D images are aligned to a 3D model derived from the 3D sensor data. Using point clouds enables the alignment for scenes full of objects that are difficult to model; for example, trees. To compute 3D point clouds from video, motion stereo is used along with a state-of-the-art algorithm for camera pose estimation. Our experiments with real data demonstrate the advantages of the proposed registration algorithm for texturing models in large-scale semiurban environments. The capability to align video before a 3D model is built from the 3D sensor data offers new practical opportunities for 3D modeling. We introduce a novel modeling-through-registration approach that fuses 3D information from both the 3D sensor and the video. Initial experiments with real data illustrate the potential of the proposed approach.
Probabilistic Identity Characterization for Face Recognition
Authors: Shaohua Kevin Zhou, Rama Chellappa
Venue: CVPR
Year: 2004

We present a general framework for characterizing the object identity in a single image or a group of images with each image containing a transformed version of the object, with applications to face recognition. In terms of the transformation, the group is made of either many still images or frames of a video sequence. The object identity is either discrete- or continuous-valued. This probabilistic framework integrates all the evidence of the set and handles the localization problem, illumination and pose variations through subspace identity encoding. Issues and challenges arising in this framework are addressed and efficient computational schemes are presented. Good face recognition results using the PIE database are reported.
A Unified Framework for Uncertainty Propagation in Automatic Shape Tracking
Authors: Xiang Sean Zhou, Dorin Comaniciu, Binglong Xie, R. Cruceanu, Alok Gupta
Venue: CVPR
Year: 2004

Uncertainty handling plays an important role during shape tracking. We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences [22]. Nevertheless, this approach required user initialization of the tracking process. This paper solves the automatic initialization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework. We show how to propagate the local detection uncertainties of multiple shape candidates during shape alignment, fusion with the predicted shape prior, and fusion with subspace constraints. As a result, we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood. Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart. Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations.
An EM-Like Algorithm for Color-Histogram-Based Object Tracking
Authors: Zoran Zivkovic, Ben J. A. Kröse
Venue: CVPR
Year: 2004

The iterative procedure called 'mean-shift' is a simple robust method for finding the position of a local mode (local maximum) of a kernel-based estimate of a density function. A new robust algorithm is given here that presents a natural extension of the 'mean-shift' procedure. The new algorithm simultaneously estimates the position of the local mode and the covariance matrix that describes the approximate shape of the local mode. We apply the new method to develop a new 5-degrees of freedom (DOF) color histogram based non-rigid object tracking algorithm.
Shape Constrained Image Segmentation by Parametric Distributional Clustering
Authors: Thomas Zöller, Joachim M. Buhmann
Venue: CVPR
Year: 2004

The automated segmentation of images into semantically meaningful parts requires shape information since lowlevel feature analysis alone often fails to reach this goal. We introduce a novel method of shape constrained image segmentation which is based on mixtures of feature distributions for color and texture as well as probabilistic shape knowledge. The combined approach is formulated in the framework of Bayesian statistics to account for the robustness requirement in image understanding. Experimental evidence shows that semantically meaningful segments are inferred, even when image data alone gives rise to ambiguous segmentations.
Pose-Robust Face Recognition Using Geometry Assisted Probabilistic Modeling
Authors: Xiaoming Liu 0002, Tsuhan Chen
Venue: CVPR
Year: 2005

Researchers have been working on human face recognition for decades. Face recognition is hard due to different types of variations in face images, such as pose, illumination and expression, among which pose variation is the hardest one to deal with. To improve face recognition under pose variation, this paper presents a geometry assisted probabilistic approach. We approximate a human head with a 3D ellipsoid model, so that any face image is a 2D projection of such a 3D ellipsoid at a certain pose. In this approach, both training and test images are back projected to the surface of the 3D ellipsoid, according to their estimated poses, to form the texture maps. Thus the recognition can be conducted by comparing the texture maps instead of the original images, as done in traditional face recognition. In addition, we represent the texture map as an array of local patches, which enables us to train a probabilistic model for comparing corresponding patches. By conducting experiments on the CMU PIE database, we show that the proposed algorithm provides better performance than the existing algorithms.
Online Modeling and Tracking of Pose-Varying Faces in Video
Authors: Xiaoming Liu 0002, Tsuhan Chen
Venue: CVPR
Year: 2005

We propose a face mosaicing approach to model both the facial appearance and geometry from pose-varying videos, and apply it in face tracking and recognition. The basic idea is that by approximating the human head as a 3D ellipsoid, multi-view face images can be back projected onto the surface of the ellipsoid, and the surface texture map is decomposed into an array of local patches. During the online modeling process, the position and pose of the first frame is assumed to be known for a given video sequence. For each frame in the sequence, the algorithm estimates the face-position and pose, and generates a texture map, which is further utilized in updating the mosaic model.
Probabilistic Modeling-Based Vessel Enhancement in Thoracic CT Scans
Authors: Gady Agam, Changhua Wu
Venue: CVPR
Year: 2005

Vessel enhancement in volumetric data is a necessary prerequisite in various medical imaging applications with particular importance for automated nodule detection. Ideally, vessel enhancement filters should enhance vessels and vessel junctions while suppressing nodules and other non-vessel elements. A distinction between vessels and nodules is normally obtained through eigenvalue analysis of the curvature tensor which is a second order differential quantity and so is sensitive to noise. Furthermore, by relying on principal curvatures alone, existing vessel enhancement filters are incapable of distinguishing between nodules and vessel junctions. In this paper we propose probabilistic vessel models from which novel vessel enhancement filters capable of enhancing junctions while suppressing nodules are derived. The proposed filters are based on eigenvalue analysis of the structure tensor which is a first order differential quantity and so are less sensitive to noise. The proposed filters are evaluated and compared to known techniques based on actual clinical data.
Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors
Authors: Jaume Amores, Nicu Sebe, Petia Radeva
Venue: CVPR
Year: 2005

We present a novel approach for fast object class recognition incorporating contextual information into boosting. The object is represented as a constellation of generalized correlograms that integrate both information of local parts and their spatial relations. Incorporating the spatial relations into our constellation of descriptors, we show that an exhaustive search for the best matching can be avoided. Combining the contextual descriptors with boosting, the system simultaneously learns the information that characterize each part of the object along with their characteristic mutual spatial relations. The proposed framework includes a matching step between homologous parts in the training set, and learning the spatial pattern after matching. In the matching part two approaches are provided: a supervised algorithm and an unsupervised one. Our results are favorably compared against state-of-the-art results.
Learning Spatiotemporal T-Junctions for Occlusion Detection
Authors: Nicholas Apostoloff, Andrew W. Fitzgibbon
Venue: CVPR
Year: 2005

The goal of motion segmentation and layer extraction can be viewed as the detection and localization of occluding surfaces. A feature that has been shown to be a particularly strong indicator of occlusion, in both computer vision and neuroscience, is the T-junction; however, little progress has been made in T-junction detection. One reason for this is the difficulty in distinguishing false T-junctions (i.e. those not on an occluding edge) and real T-junctions in cluttered images. In addition to this, their photometric profile alone is not enough for reliable detection. This paper overcomes the first problem by searching for T-junctions not in space, but in space-time. This removes many false T-junctions and creates a simpler image structure to explore. The second problem is mitigated by learning the appearance of T-junctions in these spatiotemporal images. An RVM T-junction classifier is learnt from hand-labelled data using SIFT to capture its redundancy. This detector is then demonstrated in a novel occlusion detector that fuses Canny edges and T-junctions in the spatiotemporal domain to detect occluding edges in the spatial domain.
Tracking Multiple Colored Blobs with a Moving Camera
Authors: Antonis A. Argyros, Manolis I. A. Lourakis
Venue: CVPR
Year: 2005

This paper concerns a method for tracking multiple blobs exhibiting certain color distributions in images acquired by a possibly moving camera. The method encompasses a collection of techniques that enable modeling and detecting the blobs possessing the desired color distribution(s), as well as inferring their temporal association across image sequences. Appropriately colored blobs are detected with a Bayesian classifier, which is bootstrapped with a small set of training data. Then, an online iterative training procedure is employed to refine the classifier using additional training images. Online adaptation of color probabilities is used to enable the classifier to cope with illumination changes. Tracking over time is realized through a novel technique, which can handle multiple colored blobs. Such blobs may move in complex trajectories and occlude each other in the field of view of a possibly moving camera, while their number may vary over time. A prototype implementation of the developed system running on a conventional Pentium IV processor at 2.5 GHz operates on 320/spl times/240 live video in real time (30Hz). It is worth pointing out that currently, the cycle time of the tracker is determined by the maximum acquisition frame rate that is supported by our IEEE 1394 camera, rather than the latency introduced by the computational overhead for tracking blobs.
Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures
Authors: Vassilis Athitsos, Jonathan Alon, Stan Sclaroff
Venue: CVPR
Year: 2005

This paper proposes a method for efficient nearest neighbor classification in non-Euclidean spaces with computationally expensive similarity/distance measures. Efficient approximations of such measures are obtained using the BoostMap algorithm, which produces embeddings into a real vector space. A modification to the BoostMap algorithm is proposed, which uses an optimization cost that is more appropriate when our goal is classification accuracy as opposed to nearest neighbor retrieval accuracy. Using the modified algorithm, multiple approximate nearest neighbor classifiers are obtained, that provide a wide range of trade-offs between accuracy and efficiency. The approximations are automatically combined to form a cascade classifier, which applies the slower and more accurate approximations only to the hardest cases. The proposed method is experimentally evaluated in the domain of handwritten digit recognition using shape context matching. Results on the MNIST database indicate that a speed-up of two to three orders of magnitude is gained over brute force search, with minimal losses in classification accuracy.
Ensemble Tracking
Authors: Shai Avidan
Venue: CVPR
Year: 2005

We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pixels in the next frame as either belonging to the object or the background, giving a confidence map. The peak of the map, and hence the new position of the object, is found using mean shift. Temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained online during tracking. We show a realization of this method and demonstrate it on several video sequences.
Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering
Authors: Suyash P. Awate, Ross T. Whitaker
Venue: CVPR
Year: 2005

The restoration of images is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Therefore, these methods typically lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsupervised, information-theoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing the joint entropy between them. Thus UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images and applications. This paper describes the formulation required to minimize the joint entropy measure, presents several important practical considerations in estimating image-region statistics, and then presents results on both real and synthetic data.
Object Class Recognition by Boosting a Part-Based Model
Authors: Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall
Venue: CVPR
Year: 2005

We propose a new technique for object class recognition, which learns a generative appearance model in a discriminative manner. The technique is based on the intermediate representation of an image as a set of patches, which are extracted using an interest point detector. The learning problem becomes an instance of supervised learning from sets of unordered features. In order to solve this problem, we designed a classifier based on a simple, part based, generative object model. Only the appearance of each part is modeled. When learning the model parameters, we use a discriminative boosting algorithm which minimizes the loss of the training error directly. The models thus learnt have clear probabilistic semantics, and also maintain good classification performance. The performance of the algorithm has been tested using publicly available benchmark data, and shown to be comparable to other state of the art algorithms for this task; our main advantage in these comparisons is speed (order of magnitudes faster) and scalability.
Wide-Baseline Stereo Matching with Line Segments
Authors: Herbert Bay, Vittorio Ferrari, Luc Van Gool
Venue: CVPR
Year: 2005

We present a new method for matching line segments between two uncalibrated wide-baseline images. Most current techniques for wide-baseline matching are based on viewpoint invariant regions. Those methods work well with highly textured scenes, but fail with poorly textured ones. We show that such scenes can be successfully matched using line segments. Moreover, since line segments and regions provide complementary information, their combined matching allows to deal with a broader range of scenes. We generate an initial set of line segment correspondences, and then iteratively increase their number by adding matches consistent with the topological structure of the current ones. Finally, a coplanar grouping stage allows to estimate the fundamental matrix even from line segments only.
A Sparse Support Vector Machine Approach to Region-Based Image Categorization
Authors: Jinbo Bi, Yixin Chen 0002, James Ze Wang
Venue: CVPR
Year: 2005

Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.
Face Recognition Based on Frontal Views Generated from Non-Frontal Images
Authors: Volker Blanz, Patrick Grother, P. Jonathon Phillips, Thomas Vetter
Venue: CVPR
Year: 2005

This paper presents a method for face recognition across large changes in viewpoint. Our method is based on a morphable model of 3D faces that represents face-specific information extracted from a dataset of 3D scans. For non-frontal face recognition in 2D still images, the morphable model can be incorporated in two different approaches: in the first, it serves as a preprocessing step by estimating the 3D shape of novel faces from the non-frontal input images, and generating frontal views of the reconstructed faces at a standard illumination using 3D computer graphics. The transformed images are then fed into state-of-the-art face recognition systems that are optimized for frontal views. This method was shown to be extremely effective in the Face Recognition Vendor Test FRVT 2002. In the process of estimating the 3D shape of a face from an image, a set of model coefficients are estimated. In the second method, face recognition is performed directly from these coefficients. In this paper we explain the algorithm used to preprocess the images in FRVT 2002, present additional FRVT 2002 results, and compare these results to recognition from the model coefficients.
Single Image Phase-Based MRI Fat Suppression Expectation Maximization Algorithm
Authors: Daniel J. Blezek, W. Thomas Dixon, Paritosh J. Dhawale
Venue: CVPR
Year: 2005

MRI fat suppression techniques are primarily approached by variations of the phase based methods. Multipoint methods dominate the literature, specifically methods that require the acquisition of three images at different echo times. These methods estimate the contribution of fat and water signals by solving a set of equations using echos acquired with the relative phase of fat and water at -/spl pi/, 0, /spl pi/. We present a novel approach to fat suppression that requires only a single image. Using an expectation maximization algorithm on a gradient echo image with the relative phase of fat and water at approximately /spl pi//2, the pixel composition and inhomogeneity of the magnetic field are estimated. Fat suppressed images are generated from estimated inhomogeneity. Our method does not need custom pulse sequences. Results from mathematical phantoms and volunteer images are reported.
Tracking Multiple Mouse Contours (without Too Many Samples)
Authors: Kristin Branson, Serge J. Belongie
Venue: CVPR
Year: 2005

We present a particle filtering algorithm for robustly tracking the contours of multiple deformable objects through severe occlusions. Our algorithm combines a multiple blob tracker with a contour tracker in a manner that keeps the required number of samples small. This is a natural combination because both algorithms have complementary strengths. The multiple blob tracker uses a natural multi-target model and searches a smaller and simpler space. On the other hand, contour tracking gives more fine-tuned results and relies on cues that are available during severe occlusions. Our choice of combination of these two algorithms accentuates the advantages of each. We demonstrate good performance on challenging video of three identical mice that contains multiple instances of severe occlusion.
A Non-Local Algorithm for Image Denoising
Authors: Antoni Buades, Bartomeu Coll, Jean-Michel Morel
Venue: CVPR
Year: 2005

We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters.
Camera Calibration and Light Source Estimation from Images with Shadows
Authors: Xiaochun Cao, Mubarak Shah
Venue: CVPR
Year: 2005

In this paper, we describe how camera parameters and light source orientation can be recovered from two perspective views of a scene given only two vertical lines and their cast shadows. Compared to the traditional calibration methods that involve images of some precisely machined calibration pattern, our method uses new calibration objects: the vertical objects and their parallel shadow lines, which are common in natural environments. In addition to the benefit of increasing accessibility of the calibration objects, the proposed method is also especially useful in cases where only limited information is available. To demonstrate the accuracy and the applications of the proposed algorithm, we present results on both synthetic and real images.