OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised learning for graph matching
Authors: Marius Leordeanu, Martial Hebert
Venue: CVPR
Year: 2009

Graph matching is an important problem in computer vision. It is used in 2D and 3D object matching and recognition. Despite its importance, there is little literature on learning the parameters that control the graph matching problem, even though learning is important for improving the matching rate, as shown by this and other work. In this paper we show for the first time how to perform parameter learning in an unsupervised fashion, that is when no correct correspondences between graphs are given during training. We show empirically that unsupervised learning is comparable in efficiency and quality with the supervised one, while avoiding the tedious manual labeling of ground truth correspondences. We also verify experimentally that this learning method can improve the performance of several state-of-the art graph matching algorithms.
Predicting high resolution image edges with a generic, adaptive, 3-D vehicle model
Authors: Matthew J. Leotta, Joseph L. Mundy
Venue: CVPR
Year: 2009

In traffic surveillance applications a good prior model of vehicle shape and appearance is becoming increasingly more important for tracking, shape recovery, and recognition from video. The usefulness of 2-d vehicle models is limited to a fixed viewing direction; 3-d models are nearly always more suitable. Existing 3-d vehicle models are either generic but far too simple to utilize high resolution imagery, or far too complex and limited to specific vehicle instances. This paper presents a deformable vehicle model that spans these two extremes. The model is constructed with a multi-resolution approach to fit various image resolutions. At each resolution, a small number of parameters controls the deformation to accurately represent a wide variety of passenger vehicles. The parameters control both 3-d shape and appearance of parts that deform in the 2-d manifold of the vehicle surface. These parts are regions representing windows, headlights, taillights, etc. The combination of part boundaries and surface occluding contours account for the most consistent edges observed in images of vehicles. It is shown that the model parameters can be recovered by fitting the deformable model to real images of vehicles.
A projector-based movable hand-held display system
Authors: Man Chuen Leung, Kai Ki Lee, Kin-hong Wong, Michael Ming Yuen Chang
Venue: CVPR
Year: 2009

In this paper, we proposed a movable hand-held display system which uses a projector to project display content onto an ordinary cardboard which can move freely within the projection area. Such a system can give users greater freedom of control of the display such as the viewing angle and distance. At the same time, the size of the cardboard can be made to a size that fits one's application. A projector-camera pair is calibrated and used as the tracking and projection system. We present a vision based algorithm to detect an ordinary cardboard and track its subsequent motion. Display content is then pre-warped and projected onto the cardboard at the correct position. Experimental results show that our system can project onto the cardboard in reasonable precision.
Understanding and evaluating blind deconvolution algorithms
Authors: Anat Levin, Yair Weiss, Frédo Durand, William T. Freeman
Venue: CVPR
Year: 2009

Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally. We explain the previously reported failure of the naive MAP approach by demonstrating that it mostly favors no-blur explanations. On the other hand we show that since the kernel size is often smaller than the image size a MAP estimation of the kernel alone can be well constrained and accurately recover the true blur. The plethora of recent deconvolution techniques makes an experimental evaluation on ground-truth data important. We have collected blur data with ground truth and compared recent algorithms under equal settings. Additionally, our data demonstrates that the shift-invariant blur assumption made by most algorithms is often violated.
Efficient reduction of L-infinity geometry problems
Authors: Hongdong Li
Venue: CVPR
Year: 2009

This paper presents a new method for computing optimal L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> solutions for vision geometry problems, particularly for those problems of fixed-dimension and of large-scale. Our strategy for solving a large L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> problem is to reduce it to a finite set of smallest possible subproblems. By using the fact that many of the problems in question are pseudoconvex, we prove that such a reduction is possible. To actually solve these small subproblems efficiently, we propose a direct approach which makes no use of any convex optimizer (e.g. SOCP or LP), but is based on a simple local Newton method. We give both theoretic justification and experimental validation to the new method. Potentially, our new method can be made extremely fast.
A robust parametric method for bias field estimation and segmentation of MR images
Authors: Chunming Li, Chris Gatenby, Li Wang 0026, John C. Gore
Venue: CVPR
Year: 2009

This paper proposes a new energy minimization framework for simultaneous estimation of the bias field and segmentation of tissues for magnetic resonance images. The bias field is modeled as a linear combination of a set of basis functions, and thereby parameterized by the coefficients of the basis functions. We define an energy that depends on the coefficients of the basis functions, the membership functions of the tissues in the image, and the constants approximating the true signal from the corresponding tissues. This energy is convex in each of its variables. Bias field estimation and image segmentation are simultaneously achieved as the result of minimizing this energy. We provide an efficient iterative algorithm for energy minimization, which converges to the optimal solution at a fast rate. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. The proposed method has been successfully applied to 3-Tesla MR images with desirable results. Comparisons with other approaches demonstrate the superior performance of this algorithm.
Contextual decomposition of multi-label images
Authors: Teng Li 0001, Tao Mei 0001, Shuicheng Yan, In-So Kweon, Chil-Woo Lee
Venue: CVPR
Year: 2009

Most research on image decomposition, e.g. image segmentation and image parsing, has predominantly focused on the low-level visual clues within single image and neglected the contextual information across different images. In this paper, we present a new perspective to image decomposition piloted by the multi-labels associated with individual images. Observing that the context information (i.e., local label representations of the same label are similar while those from different labels are dissimilar) exists across different images, we propose to perform image decomposition in a collective way, and then the image decomposition problem is formulated as an optimization which maximizes inter-label difference and at the same time minimizes intra-label difference of the target label representations. Such contextual image decomposition has a wide variety of applications, among which the two exemplary ones are: 1) multi-label image annotation in which the sparse coding of a query image over the bases consisting of all learned label representations naturally produces the multi-label annotation, and 2) label ranking in which the annotated labels are re-ordered according to the sparse coding coefficients on those learned label representations. It is worth noting that these two applications can be performed simultaneously via the label propagation process in sparse coding.
Joint and implicit registration for face recognition
Authors: Peng Li, Simon J. D. Prince
Venue: CVPR
Year: 2009

Contemporary face recognition algorithms rely on precise localization of keypoints (corner of eye, nose etc.). Unfortunately, finding keypoints reliably and accurately remains a hard problem. In this paper we pose two questions. First, is it possible to exploit the gallery image in order to find keypoints in the probe image? For instance, consider finding the left eye in the probe image. Rather than using a generic eye model, we use a model that is informed by the appearance of the eye in the gallery image. To this end we develop a probabilistic model which combines recognition and keypoint localization. Second, is it necessary to localize keypoints? Alternatively we can consider keypoint position as a hidden variable which we marginalize over in a Bayesian manner. We demonstrate that both of these innovations improve performance relative to conventional methods in both frontal and cross-pose face recognition.
Global optimization for alignment of generalized shapes
Authors: Hongsheng Li 0001, Tian Shen, Xiaolei Huang 0001
Venue: CVPR
Year: 2009

In this paper, we introduce a novel algorithm to solve global shape registration problems. We use gray-scale “images” to represent source shapes, and propose a novel two-component Gaussian Mixtures (GM) distance map representation for target shapes. Based on this flexible asymmetric image-based representation, a new energy function is defined. It proves to be a more robust shape dissimilarity metric that can be computed efficiently. Such high efficiency is essential for global optimization methods. We adopt one of them, the Particle Swarm Optimization (PSO), to effectively estimate the global optimum of the new energy function. Experiments and comparison performed on generalized shape data including continuous shapes, unstructured sparse point sets, and gradient maps, demonstrate the robustness and effectiveness of the algorithm.
Localized content-based image retrieval through evidence region identification
Authors: Wu-Jun Li, Dit-Yan Yeung
Venue: CVPR
Year: 2009

Over the past decade, multiple-instance learning (MIL) has been successfully utilized to model the localized content-based image retrieval (CBIR) problem, in which a bag corresponds to an image and an instance corresponds to a region in the image. However, existing feature representation schemes are not effective enough to describe the bags in MIL, which hinders the adaptation of sophisticated single-instance learning (SIL) methods for MIL problems. In this paper, we first propose an evidence region (or evidence instance) identification method to identify the evidence regions supporting the labels of the images (i.e., bags). Then, based on the identified evidence regions, a very effective feature representation scheme, which is also very computationally efficient and robust to labeling noise, is proposed to describe the bags. As a result, the MIL problem is converted into a standard SIL problem and a support vector machine (SVM) can be easily adapted for localized CBIR. Experimental results on two challenging data sets show that our method, called EC-SVM, can outperform the state-of-the-art methods in terms of accuracy, robustness and efficiency.
Hardware-efficient belief propagation
Authors: Chia-Kai Liang, Chao-Chung Cheng, Yen-Chieh Lai, Liang-Gee Chen, Homer H. Chen
Venue: CVPR
Year: 2009

Belief propagation (BP) is an effective algorithm for solving energy minimization problems in computer vision. However, it requires enormous memory, bandwidth, and computation because messages are iteratively passed between nodes in the Markov random field (MRF). In this paper, we propose two methods to address this problem. The first method is a message passing scheme called tile-based belief propagation. The key idea of this method is that a message can be well approximated from other faraway ones. We split the MRF into many tiles and perform BP within each one. To preserve the global optimality, we store the outgoing boundary messages of a tile and use them when performing BP in the neighboring tiles. The tile-based BP only requires 1-5% memory and 0.2-1% bandwidth of the ordinary BP. The second method is an O(L) message construction algorithm for the robust functions commonly used for describing the smoothness terms in the energy function. We find that many variables in constructing a message are repetitive; thus these variables can be calculated once and reused many times. The proposed algorithms are suitable for parallel implementations. We design a low-power VLSI circuit for disparity estimation that can construct 440 M messages per second and generate high quality disparity maps in near real-time. We also implement the proposed algorithms on a GPU, which can calculate messages 4 times faster than the sequential O(L) method.
Multiple instance fFeature for robust part-based object detection
Authors: Zhe Lin 0001, Gang Hua 0001, Larry S. Davis
Venue: CVPR
Year: 2009

Feature misalignment in object detection refers to the phenomenon that features which fire up in some positive detection windows do not fire up in other positive detection windows. Most often it is caused by pose variation and local part deformation. Previous work either totally ignores this issue, or naively performs a local exhaustive search to better position each feature. We propose a learning framework to mitigate this problem, where a boosting algorithm is performed to seed the position of the object part, and a multiple instance boosting algorithm further pursues an aggregated feature for this part, namely multiple instance feature. Unlike most previous boosting based object detectors, where each feature value produces a single classification result, the value of the proposed multiple instance feature is the Noisy-OR integration of a bag of classification results. Our approach is applied to the task of human detection and is tested on two popular benchmarks. The proposed approach brings significant improvement in performance, i.e., smaller number of features used in the cascade and better detection accuracy.
Layered graph matching by composite cluster sampling with collaborative and competitive interactions
Authors: Liang Lin, Kun Zeng, Xiaobai Liu, Song Chun Zhu
Venue: CVPR
Year: 2009

This paper studies a framework for matching an unknown number of corresponding structures in two images (shapes), motivated by detecting objects in cluttered background and learning parts from articulated motion. Due to the large distortion between shapes and ambiguity caused by symmetric or cluttered structures, many inference algorithms often get stuck in local minimums and converge slowly. We propose a composite cluster sampling algorithm with a “candidacy graph” representation, where each vertex (candidate) is a possible match for a pair of source and target primitives (local structure or small curves), and the layered matching is then formulated as a multiple coloring problem. Each two vertices can be linked by either a competitive edge or a collaborative edge. These edges indicate the connected vertices should/shouldn't be assigned the same color. With this representation, the stochastic sampling contains two steps: (i) Sampling the competitive and collaborative edges to form a composite cluster, in which a few mutual-conflicting connected components are in different colors; (ii) Sampling the new colors to this cluster remaining consistency with Markov Chain Monte Carlo (MCMC) mechanism. The algorithm is applied to many applications on many public datasets and outperform the state of the art approaches.
Continuous depth estimation for multi-view stereo
Authors: Yebin Liu, Xun Cao, Qionghai Dai, Wenli Xu
Venue: CVPR
Year: 2009

Depth-map merging approaches have become more and more popular in multi-view stereo (MVS) because of their flexibility and superior performance. The quality of depth map used for merging is vital for accurate 3D reconstruction. While traditional depth map estimation has been performed in a discrete manner, we suggest the use of a continuous counterpart. In this paper, we first integrate silhouette information and epipolar constraint into the variational method for continuous depth map estimation. Then, several depth candidates are generated based on a multiple starting scales (MSS) framework. From these candidates, refined depth maps for each view are synthesized according to path-based NCC (normalized cross correlation) metric. Finally, the multiview depth maps are merged to produce 3D models. Our algorithm excels at detail capture and produces one of the most accurate results among the current algorithms for sparse MVS datasets according to the Middlebury benchmark. Additionally, our approach shows its outstanding robustness and accuracy in free-viewpoint video scenario.
Learning color and locality cues for moving object detection and segmentation
Authors: Feng Liu 0015, Michael Gleicher
Venue: CVPR
Year: 2009

This paper presents an algorithm for automatically detecting and segmenting a moving object from a monocular video. Detecting and segmenting a moving object from a video with limited object motion is challenging. Since existing automatic algorithms rely on motion to detect the moving object, they cannot work well when the object motion is sparse and insufficient. In this paper, we present an unsupervised algorithm to learn object color and locality cues from the sparse motion information. We first detect key frames with reliable motion cues and then estimate moving sub-objects based on these motion cues using a Markov Random Field (MRF) framework. From these sub-objects, we learn an appearance model as a color Gaussian Mixture Model. To avoid the false classification of background pixels with similar color to the moving objects, the locations of these sub-objects are propagated to neighboring frames as locality cues. Finally, robust moving object segmentation is achieved by combining these learned color and locality cues with motion cues in a MRF framework. Experiments on videos with a variety of object and camera motion demonstrate the effectiveness of this algorithm.
Recognizing realistic actions from videos
Authors: Jingen Liu, Jiebo Luo 0001, Mubarak Shah
Venue: CVPR
Year: 2009

In this paper, we present a systematic framework for recognizing realistic actions from videos “in the wild.” Such unconstrained videos are abundant in personal collections as well as on the web. Recognizing action from such videos has not been addressed extensively, primarily due to the tremendous variations that result from camera motion, background clutter, changes in object appearance, and scale, etc. The main challenge is how to extract reliable and informative features from the unconstrained videos. We extract both motion and static features from the videos. Since the raw features of both types are dense yet noisy, we propose strategies to prune these features. We use motion statistics to acquire stable motion features and clean static features. Furthermore, PageRank is used to mine the most informative static features. In order to further construct compact yet discriminative visual vocabularies, a divisive information-theoretic algorithm is employed to group semantically related features. Finally, AdaBoost is chosen to integrate all the heterogeneous yet complementary features for recognition. We have tested the framework on the KTH dataset and our own dataset consisting of 11 categories of actions collected from YouTube and personal videos, and have obtained impressive results for action recognition and action localization.
Granularity-tunable gradients partition (GGP) descriptors for human detection
Authors: Yazhou Liu, Shiguang Shan, Wenchao Zhang, Xilin Chen 0001, Wen Gao 0001
Venue: CVPR
Year: 2009

This paper proposes a novel descriptor, granularity-tunable gradients partition (GGP), for human detection. The concept granularity is used to define the spatial and angular uncertainty of the line segments in the Hough space. Then this uncertainty is backprojected into the image space by orientation-space partitioning to achieve efficient implementation. By changing the granularity parameter, the level of uncertainty can be controlled quantitatively. Therefore a family of descriptors with versatile representation property can be generated. Specifically, the finely granular GGP descriptors can represent the specific geometry information of the object (the same as Edgelet); while the coarsely granular GGP descriptors can provide the statistical representation of the object (the same as histograms of oriented gradients, HOG). Moreover, the position, orientation, strength and distribution of the gradients are embedded into a unified descriptor to further improve the GGP's representation power. A cascade structured classifier is built by boosting the linear regression functions. Experimental results on INRIA dataset show that the proposed method achieves comparable results to those of the state-of-the-art methods.
Multi-camera activity correlation analysis
Authors: Chen Change Loy, Tao Xiang 0002, Shaogang Gong
Venue: CVPR
Year: 2009

We propose a novel approach for modelling correlations between activities in a busy public space captured by multiple non-overlapping and uncalibrated cameras. In our approach, each camera view is automatically decomposed into semantic regions, across which different spatio-temporal activity patterns are observed. A novel Cross Canonical Correlation Analysis (xCCA) framework is formulated to detect and quantify temporal and causal relationships between regional activities within and across camera views. The approach accomplishes three tasks: (1) estimate the spatial and temporal topology of the camera network; (2) facilitate more robust and accurate person re-identification; (3) perform global activity modelling and video temporal segmentation by linking visual evidence collected across camera views. Our approach differs from the state of the art in that it does not rely on either intra or inter camera tracking. It therefore can be applied to even the most challenging video surveillance settings featured with severe occlusions and extremely low spatial and temporal resolutions. Its effectiveness is demonstrated using 153 hours of videos from 8 cameras installed in a busy underground station.
Image categorization with spatial mismatch kernels
Authors: Zhiwu Lu 0001, Horace Ho-Shing Ip
Venue: CVPR
Year: 2009

This paper presents a new class of 2D string kernels, called spatial mismatch kernels, for use with support vector machine (SVM) in a discriminative approach to the image categorization problem. We first represent images as 2D sequences of those visual keywords obtained by clustering all the blocks that we divide images into on a regular grid. Through decomposing each 2D sequence into two parallel 1D sequences (i.e. the row-wise and column-wise ones), our spatial mismatch kernels can then measure 2D sequence similarity based on shared occurrences of k-length 1D subsequences, counted with up to m mismatches. While those bag-of-words methods ignore the spatial structure of an image, our spatial mismatch kernels can capture the spatial dependencies across visual keywords within the image. Experiments on the natural and histological image databases then demonstrate that our spatial mismatch kernel methods can achieve superior results.