OpenRaiser/Intern-Atlas huggingface dataset:
Multiple dynamic models for tracking the left ventricle of the heart from ultrasound data using particle filters and deep learning architectures
Authors: Gustavo Carneiro 0001, Jacinto C. Nascimento
Venue: CVPR
Year: 2010

The problem of automatic tracking and segmentation of the left ventricle (LV) of the heart from ultrasound images can be formulated with an algorithm that computes the expected segmentation value in the current time step given all previous and current observations using a filtering distribution. This filtering distribution depends on the observation and transition models, and since it is hard to compute the expected value using the whole parameter space of segmentations, one has to resort to Monte Carlo sampling techniques to compute the expected segmentation parameters. Generally, it is straightforward to compute probability values using the filtering distribution, but it is hard to sample from it, which indicates the need to use a proposal distribution to provide an easier sampling method. In order to be useful, this proposal distribution must be carefully designed to represent a reasonable approximation for the filtering distribution. In this paper, we introduce a new LV tracking and segmentation algorithm based on the method described above, where our contributions are focused on a new transition and observation models, and a new proposal distribution. Our tracking and segmentation algorithm achieves better overall results on a previously tested dataset used as a benchmark by the current state-of-the-art tracking algorithms of the left ventricle of the heart from ultrasound images.
Constrained parametric min-cuts for automatic object segmentation
Authors: João Carreira 0002, Cristian Sminchisescu
Venue: CVPR
Year: 2010

We present a novel framework for generating and ranking plausible objects hypotheses in an image using bottom-up processes and mid-level cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge about properties of individual object classes, by solving a sequence of constrained parametric min-cut problems (CPMC) on a regular image grid. We then learn to rank the object hypotheses by training a continuous model to predict how plausible the segments are, given their mid-level region properties. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC09 segmentation dataset. It achieves the same average best segmentation covering as the best performing technique to date, 0.61 when using just the top 7 ranked segments, instead of the full hierarchy in. Our method achieves 0.78 average best covering using 154 segments. In a companion paper, we also show that the algorithm achieves state-of-the art results when used in a segmentation-based recognition pipeline.
Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map
Authors: Tat-Jen Cham, Arridhana Ciptadi, Wei-Chian Tan, Minh-Tri Pham, Liang-Tien Chia
Venue: CVPR
Year: 2010

A framework is presented for estimating the pose of a camera based on images extracted from a single omnidirectional image of an urban scene, given a 2D map with building outlines with no 3D geometric information nor appearance data. The framework attempts to identify vertical corner edges of buildings in the query image, which we term VCLH, as well as the neighboring plane normals, through vanishing point analysis. A bottom-up process further groups VCLH into elemental planes and subsequently into 3D structural fragments modulo a similarity transformation. A geometric hashing lookup allows us to rapidly establish multiple candidate correspondences between the structural fragments and the 2D map building contours. A voting-based camera pose estimation method is then employed to recover the correspondences admitting a camera pose solution with high consensus. In a dataset that is even challenging for humans, the system returned a top-30 ranking for correct matches out of 3600 camera pose hypotheses (0.83% selectivity) for 50.9% of queries.
Clustering dynamic textures with the hierarchical EM algorithm
Authors: Antoni B. Chan, Emanuele Coviello, Gert R. G. Lanckriet
Venue: CVPR
Year: 2010

The dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model has been applied to a wide variety of computer vision problems, such as motion segmentation, motion classification, and video registration. In this paper, we derive a new algorithm for clustering DT models that is based on the hierarchical EM algorithm. The proposed clustering algorithm is capable of both clustering DTs and learning novel DT cluster centers that are representative of the cluster members, in a manner that is consistent with the underlying generative probabilistic model of the DT. We then demonstrate the efficacy of the clustering algorithm on several applications in motion analysis, including hierarchical motion clustering, semantic motion annotation, and bag-of-systems codebook generation.
A square-root sampling approach to fast histogram-based search
Authors: Huang-Wei Chang, Hwann-Tzong Chen
Venue: CVPR
Year: 2010

We present an efficient pixel-sampling technique for histogram-based search. Given a template image as a query, a typical histogram-based algorithm aims to find the location of the target in another large test image, by evaluating a similarity measure for comparing the feature histogram of the template with that of each possible subwindow in the test image. The computational cost would be high if each subwindow needs to compute its histogram and evaluate the similarity measure. In this paper, we adopt the probability-product kernels as the similarity measures, and show that the computation of histograms and the evaluation of the kernel-based similarities can be integrated through a sampling approach. Specifically, we present a square-root sampling method to avoid the computation of histograms, and meanwhile, reduce the number of pixels required for evaluating the similarity measure. The proposed approximation algorithm is time- and memory-efficient. The time complexity of computing the similarity for each subwindow is O(1). The memory requirement of our algorithm is not in proportion to the size of the template or the number of histogram bins, which allows the use of more distinctive image representations for better search accuracy.
Factorization towards a classifier
Authors: Qiang Chen 0007, Shuicheng Yan, Tian-Tsong Ng
Venue: CVPR
Year: 2010

In practice, nonnegative data factorization is often performed for data dimensionality reduction prior to a classification task using a classifier which is effective in low dimensional space such as nearest neighbor classifier. In this work, we propose a novel formulation to learn a multi-class classifier directly through a supervised nonnegative data factorization. This new formulation has the following properties: 1) the nonnegative data matrix is approximated as the product of a nonnegative basis matrix and a coefficient matrix where the nonnegative bases distinctively capture the common characteristics of all classes apart from that specific to individual classes; 2) a regularization term is imposed on nonnegative data factorization so that each datum can be predominantly reconstructed by the common basis vectors and its corresponding class-specific basis vectors; and 3) the coefficient vector for each datum is assumed to be transformed from a mapped kernel space, and the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm of the class-specific coefficients reveals the relative confidence of classes, which then directly leads to a multi-class classifier. We also present an iterative optimization technique for our formulation and analytically show its convergence property. Extensive experiments on face recognition, head pose estimation, and handwritten digit recognition tasks clearly demonstrate the advantages of the proposed classifier over the conventional two-step approach of nonnegative data factorization followed by a classic classifier.
Object recognition by discriminative combinations of line segments and ellipses
Authors: Alex Y. S. Chia, Susanto Rahardja, Deepu Rajan, Maylor K. H. Leung
Venue: CVPR
Year: 2010

We present a contour based approach to object recognition in real-world images. Contours are represented by generic shape primitives of line segments and ellipses. These primitives offer substantial flexibility to model complex shapes. We pair connected primitives as shape tokens, and learn category specific combinations of shape tokens. We do not restrict combinations to have a fixed number of tokens, but allow each combination to flexibly evolve to best represent a category. This, coupled with the generic nature of primitives, enables a variety of discriminative shape structures of a category to be learned. We compare our approach with related methods and state-of-the-art contour based approaches on two demanding datasets across 17 categories. Highly competitive results are obtained. In particular, on the challenging Weizmann horse dataset, we attain improved image classification and object detection results over the best contour based results published so far.
A shape-driven MRF model for the segmentation of organs in medical images
Authors: Deepak Roy Chittajallu, Shishir K. Shah, Ioannis A. Kakadiaris
Venue: CVPR
Year: 2010

In this paper, we present a knowledge-driven Markov Random Field (MRF) model for the segmentation of organs in medical images with particular emphasis on the incorporation of shape constraints into the segmentation problem. We cast the problem of image segmentation as the Maximum A Posteriori (MAP) estimation of a Markov Random Field which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. We then incorporate a set of constraints into the Gibbs energy function that collectively force the resulting segmentation contour/surface to have a shape similar to that of a given shape template. In particular, we introduce a flux-maximization constraint and a generalized template-based star-shape constraint that are encoded into the first- and second-order clique potentials of the Gibbs energy function, respectively. Our main contribution is in the translation of a set of global notions about the shape of the desired segmentation contour into a set of local measures that can be conveniently encoded into the Gibbs energy function and used in combination with other traditionally used constraints derived from image information. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography (CT) data.
Authority-shift clustering: Hierarchical clustering by authority seeking on graphs
Authors: Minsu Cho, Kyoung Mu Lee
Venue: CVPR
Year: 2010

In this paper, a novel hierarchical clustering method using link analysis techniques is introduced. The algorithm is formulated as an authority seeking procedure on graphs, which computes the shifts toward nodes with high authority scores. For the authority shift, we adopted the personalized PageRank score of the graph. Based on the concept of authority seeking, we achieve hierarchical clustering by iteratively propagating the authority scores to other nodes and shifting authority nodes. This scheme solves the chicken-egg difficulty in hierarchical clustering by a semiglobal bottom-up approach exploiting the global structure of the graph. The experimental evaluation demonstrates that our algorithm is more powerful compared with existing graph-based approaches in clustering and image segmentation tasks.
Spatialized epitome and its applications
Authors: Xinqi Chu, Shuicheng Yan, Liyuan Li, Kap Luk Chan, Thomas S. Huang
Venue: CVPR
Year: 2010

Due to the lack of explicit spatial consideration, existing epitome model may fail for image recognition and target detection, which directly motivates us to propose the so-called spatialized epitome in this paper. Extended from the original graphical model of epitome, the spatialized epitome provides a general framework to integrate both appearance and spatial arrangement of patches in the image to achieve a more precise likelihood representation for image(s) and eliminate ambiguities in image reconstruction and recognition. From the extended graphical model of epitome, an EM learning procedure is derived under the framework of variational approximation. The learning procedure can generate an optimized summary of the image appearance with spatial distribution of the similar patches. From the spatialized epitome, we present a principled way of inferring the probability of a new input image under the learnt model and thereby enabling image recognition and target detection. We show how the incorporation of spatial information enhances the epitome's ability for discrimination on several vision tasks, e.g., misalignment/cross-pose face recognition and vehicle detection with a few training samples.
Fast approximate energy minimization with label costs
Authors: Andrew Delong, Anton Osokin, Hossam N. Isack, Yuri Boykov
Venue: CVPR
Year: 2010

The α-expansion algorithm has had a significant impact in computer vision due to its generality, effectiveness, and speed. Thus far it can only minimize energies that involve unary, pairwise, and specialized higher-order terms. Our main contribution is to extend α-expansion so that it can simultaneously optimize “label costs” as well. An energy with label costs can penalize a solution based on the set of labels that appear in it. The simplest special case is to penalize the number of labels in the solution. Our energy is quite general, and we prove optimality bounds for our algorithm. A natural application of label costs is multi-model fitting, and we demonstrate several such applications in vision: homography detection, motion segmentation, and unsupervised image segmentation. Our C++/MATLAB implementation is publicly available.
Dynamic texture recognition based on distributions of spacetime oriented structure
Authors: Konstantinos G. Derpanis, Richard P. Wildes
Venue: CVPR
Year: 2010

This paper addresses the challenge of recognizing dynamic textures based on their observed visual dynamics. Typically, the term dynamic texture is used with reference to image sequences of various natural processes that exhibit stochastic dynamics (e.g., smoke, water and windblown vegetation); although, it applies equally well to images of simpler dynamics when analyzed in terms of aggregate region properties (e.g., uniform motion of elements in traffic video). In this paper, a novel approach to dynamic texture representation and an associated recognition method are proposed. The approach pursued here recognizes dynamic textures based on matching distributions (histograms) of spacetime orientation structure. Empirical evaluation on a standard database with controls to remove the effects of identical viewpoint demonstrates that the proposed approach achieves superior performance over alternative state-of-the-art methods.
Linked edges as stable region boundaries
Authors: Michael Donoser, Hayko Riemenschneider, Horst Bischof
Venue: CVPR
Year: 2010

Many of the recently popular shape based category recognition methods require stable, connected and labeled edges as input. This paper introduces a novel method to find the most stable region boundaries in grayscale images for this purpose. In contrast to common edge detection algorithms as Canny, which only analyze local discontinuities in image brightness, our method integrates mid-level information by analyzing regions that support the local gradient magnitudes. We use a component tree where every node contains a single connected region obtained from thresholding the gradient magnitude image. Edges in the tree are defined by an inclusion relationship between nested regions in different levels of the tree. Region boundaries which are similar in shape (i. e. have a low chamfer distance) across several levels of the tree are included in the final result. Since the component tree can be calculated in quasi-linear time and chamfer matching between nodes in the component tree is reduced to analysis of the distance transformation, results are obtained in an efficient manner. The proposed detection algorithm labels all identified edges during calculation, thus avoiding the cumbersome post-processing of connecting and labeling edge responses. We evaluate our method on two reference data sets and demonstrate improved performance for shape prototype based localization of objects in images.
Region moments: Fast invariant descriptors for detecting small image structures
Authors: Gianfranco Doretto, Yi Yao
Venue: CVPR
Year: 2010

This paper presents region moments, a class of appearance descriptors based on image moments applied to a pool of image features. A careful design of the moments and the image features, makes the descriptors scale and rotation invariant, and therefore suitable for vehicle detection from aerial video, where targets appear at different scales and orientations. Region moments are linearly related to the image features. Thus, comparing descriptors by computing costly geodesic distances and non-linear classifiers can be avoided, because Euclidean geometry and linear classifiers are still effective. The descriptor computation is made efficient by designing a fast procedure based on the integral representation. An extensive comparison between region moments and the region covariance descriptors, reports theoretical, qualitative, and quantitative differences among them, with a clear advantage of the region moments, when used for detecting small image structures, such as vehicles in aerial video. The proposed descriptors hold the promise to become an effective building block in other applications.
Visual event recognition in videos by learning from web data
Authors: Lixin Duan, Dong Xu 0001, Ivor Wai-Hung Tsang, Jiebo Luo 0001
Venue: CVPR
Year: 2010

We propose a visual event recognition framework for consumer domain videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). First, we propose a new aligned space-time pyramid matching method to measure the distances between two video clips, where each video clip is divided into space-time volumes over multiple levels. We calculate the pair-wise distances between any two volumes and further integrate the information from different volumes with Integer-flow Earth Mover's Distance (EMD) to explicitly align the volumes. Second, we propose a new cross-domain learning method in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time feature and static SIFT feature) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web domain and consumer domain). For each pyramid level and each type of local features, we train a set of SVM classifiers based on the combined training set from two domains using multiple base kernels of different kernel types and parameters, which are fused with equal weights to obtain an average classifier. Finally, we propose a cross-domain learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), to learn an adapted classifier based on multiple base kernels and the prelearned average classifiers by minimizing both the structural risk functional and the mismatch between data distributions from two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data.
Fast global optimization of curvature
Authors: Noha Youssry El-Zehiry, Leo J. Grady
Venue: CVPR
Year: 2010

Two challenges in computer vision are to accommodate noisy data and missing data. Many problems in computer vision, such as segmentation, filtering, stereo, reconstruction, inpainting and optical flow seek solutions that match the data while satisfying an additional regularization, such as total variation or boundary length. A regularization which has received less attention is to minimize the curvature of the solution. One reason why this regularization has received less attention is due to the difficulty in finding an optimal solution to this image model, since many existing methods are complicated, slow and/or provide a suboptimal solution. Following the recent progress of Schoenemann et al., we provide a simple formulation of curvature regularization which admits a fast optimization which gives globally optimal solutions in practice. We demonstrate the effectiveness of this method by applying this curvature regularization to image segmentation.
Detecting text in natural scenes with stroke width transform
Authors: Boris Epshtein, Eyal Ofek, Yonatan Wexler
Venue: CVPR
Year: 2010

We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.
3D curve sketch: Flexible curve-based stereo reconstruction and calibration
Authors: Ricardo Fabbri, Benjamin B. Kimia
Venue: CVPR
Year: 2010

Interest point-based multiview 3D reconstruction and calibration methods have been very successful in select applications but are not applicable when an abundance of feature points are not available. They also lead to an unorganized point cloud reconstruction where the geometry of the scene is not explicit. The multiview stereo methods on the other hand yield dense surface geometry but require a highly controlled or calibrated setting. We propose and develop a novel framework for 3D reconstruction and calibration based on image curve content, whose output is a 3D curve sketch, an unorganized set of 3D curve fragments. This approach, which is meant to augment the previous approaches, results in a reconstruction of geometric curve structure which can serve as a scaffold on which surface patches can be potentially reconstructed. It is intented for the setting where a number of images are available with coarsely calibrated cameras. The approach operates in two stages. A reliable partial 3D curve sketch is first reconstructed and this is used to refine the cameras to yield a more complete 3D curve sketch in a second stage. A key advantage of this approach is the ability to integrate information across a large number of views. The results have been evaluated on a few datasets.
Harvesting large-scale weakly-tagged image databases from the web
Authors: Jianping Fan 0001, Yi Shen 0005, Ning Zhou, Yuli Gao
Venue: CVPR
Year: 2010

To leverage large-scale weakly-tagged images for computer vision tasks (such as object detection and scene recognition), a novel cross-modal tag cleansing and junk image filtering algorithm is developed for cleansing the weakly-tagged images and their social tags (i.e., removing irrelevant images and finding the most relevant tags for each image) by integrating both the visual similarity contexts between the images and the semantic similarity contexts between their tags. Our algorithm can address the issues of spams, polysemes and synonyms more effectively and determine the relevance between the images and their social tags more precisely, thus it can allow us to create large amounts of training images with more reliable labels by harvesting from large-scale weakly-tagged images, which can further be used to achieve more effective classifier training for many computer vision tasks.