OpenRaiser/Intern-Atlas huggingface dataset:
Kernel integral images: A framework for fast non-uniform filtering
Authors: Mohamed E. Hussein 0001, Fatih Porikli, Larry S. Davis
Venue: CVPR
Year: 2008

Integral images are commonly used in computer vision and computer graphics applications. Evaluation of box filters via integral images can be performed in constant time, regardless of the filter size. Although Heckbert (1986) extended the integral image approach for more complex filters, its usage has been very limited, in practice. In this paper, we present an extension to integral images that allows for application of a wide class of non-uniform filters. Our approach is superior to Heckbertpsilas in terms of precision requirements and suitability for parallelization. We explain the theoretical basis of the approach and instantiate two concrete examples: filtering with bilinear interpolation, and filtering with approximated Gaussian weighting. Our experiments show the significant speedups we achieve, and the higher accuracy of our approach compared to Heckbertpsilas.
Fast image search for learned metrics
Authors: Prateek Jain 0002, Brian Kulis, Kristen Grauman
Venue: CVPR
Year: 2008

We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the imagespsila underlying relationships well. To allow sub-linear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality make it infeasible to learn an explicit weighting over the feature dimensions. We demonstrate the approach applied to a variety of image datasets. Our learned metrics improve accuracy relative to commonly-used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.
Selective hidden random fields: Exploiting domain-specific saliency for event classification
Authors: Vidit Jain, Amit Singhal 0001, Jiebo Luo 0001
Venue: CVPR
Year: 2008

Classifying an event captured in an image is useful for understanding the contents of the image. The captured event provides context to refine models for the presence and appearance of various entities, such as people and objects, in the captured scene. Such contextual processing facilitates the generation of better abstractions and annotations for the image. Consider a typical set of consumer images with sports-related content. These images are taken mostly by amateur photographers, and often at a distance. In the absence of manual annotation or other sources of information such as time and location, typical recognition tasks are formidable on these images. Identifying the sporting event in these images provides a context for further recognition and annotation tasks. We propose to use the domain-specific saliency of the appearances of the playing surfaces, and ignore the noninformative parts of the image such as crowd regions, to discriminate among different sports. To this end, we present a variation of the hidden-state conditional random field that selects a subset of the observed features suitable for classification. The inferred hidden variables in this model represent a selection criteria desirable for the problem domain. For sports-related images, this selection criteria corresponds to the segmentation of the playing surface in the image. We demonstrate the utility of this model on consumer images collected from the Internet.
Motion blur identification from image gradients
Authors: Hui Ji 0002, Chaoqiang Liu
Venue: CVPR
Year: 2008

Restoration of a degraded image from motion blurring is highly dependent on the estimation of the blurring kernel. Most of the existing motion deblurring techniques model the blurring kernel with a shift-invariant box filter, which holds true only if the motion among images is of uniform velocity. In this paper, we present a spectral analysis of image gradients, which leads to a better configuration for identifying the blurring kernel of more general motion types (uniform velocity motion, accelerated motion and vibration). Furthermore, we introduce a hybrid Fourier-Radon transform to estimate the parameters of the blurring kernel with improved robustness to noise over available techniques. The experiments on both simulated images and real images show that our algorithm is capable of accurately identifying the blurring kernel for a wider range of motion types.
A unified framework for generalized Linear Discriminant Analysis
Authors: Shuiwang Ji, Jieping Ye
Venue: CVPR
Year: 2008

Linear Discriminant Analysis (LDA) is one of the well-known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.
Global pose estimation using non-tree models
Authors: Hao Jiang 0007, David R. Martin 0001
Venue: CVPR
Year: 2008

We propose a novel global pose estimation method to detect body parts of articulated objects in images based on non-tree graph models. There are two kinds of edges defined in the body part relation graph: Strong (tree) edges corresponding to the body plan that can enforce any type of constraint, and weak (non-tree) edges that express exclusion constraints arising from inter-part occlusion and symmetry conditions. We express optimal part localization as a multiple shortest path problem in a set of correlated trellises constructed from the graph model. Strong model edges generate the trellises, while weak model edges prohibit implausible poses by generating exclusion constraints among trellis nodes and edges. The optimization may be expressed as an integer linear program and solved using a novel two-stage relaxation scheme. Experiments show that the proposed method has a high chance of obtaining the globally optimal pose at low computational cost.
Learning based coarse-to-fine image registration
Authors: Jiayan Jiang, Songfeng Zheng, Arthur W. Toga, Zhuowen Tu
Venue: CVPR
Year: 2008

This paper describes a coarse-to-fine learning based image registration algorithm which has particular advantages in dealing with multi-modality images. Many existing image registration algorithms [18] use a few designed terms or mutual information to measure the similarity between image pairs. Instead, we push the learning aspect by selecting and fusing a large number of features for measuring the similarity. Moreover, the similarity measure is carried in a coarse-to-fine strategy: global similarity measure is first performed to roughly locate the component, we then learn/compute similarity on the local image patches to capture the fine level information. When estimating the transformation parameters, we also engage a coarse-to-fine strategy. Off-the-shelf interest point detectors such as SIFT [12] have degraded results on medical images. We further push the learning idea to extract the main structures/landmarks. Our algorithm is illustrated on three applications: (1) registration of mouse brain images of different modalities, (2) registering human brain image of MRI T1 and T2 images, (3) faces of different expressions. We show greatly improved results over the existing algorithms based on either mutual information or geometric structures.
Fuzzy chamfer distance and its probabilistic formulation for visual tracking
Authors: Yonggang Jin, Farzin Mokhtarian, Miroslaw Bober, John Illingworth
Venue: CVPR
Year: 2008

The paper presents a fuzzy chamfer distance and its probabilistic formulation for edge-based visual tracking. First, connections of the chamfer distance and the Hausdorff distance with fuzzy objective functions for clustering are shown using a reformulation theorem. A fuzzy chamfer distance (FCD) based on fuzzy objective functions and a probabilistic formulation of the fuzzy chamfer distance (PFCD) based on data association methods are then presented for tracking, which can all be regarded as reformulated fuzzy objective functions and minimized with iterative algorithms. Results on challenging sequences demonstrate the performance of the proposed tracking method.
Object recognition and segmentation by non-rigid quasi-dense matching
Authors: Juho Kannala, Esa Rahtu, Sami S. Brandt, Janne Heikkilä
Venue: CVPR
Year: 2008

In this paper, we present a non-rigid quasi-dense matching method and its application to object recognition and segmentation. The matching method is based on the match propagation algorithm which is here extended by using local image gradients for adapting the propagation to smooth non-rigid deformations of the imaged surfaces. The adaptation is based entirely on the local properties of the images and the method can be hence used in non-rigid image registration where global geometric constraints are not available. Our approach for object recognition and segmentation is directly built on the quasi-dense matching. The quasi-dense pixel matches between the model and test images are grouped into geometrically consistent groups using a method which utilizes the local affine transformation estimates obtained during the propagation. The number and quality of geometrically consistent matches is used as a recognition criterion and the location of the matching pixels directly provides the segmentation. The experiments demonstrate that our approach is able to deal with extensive background clutter, partial occlusion, large scale and viewpoint changes, and notable geometric deformations.
Combining brain computer interfaces with vision for object categorization
Authors: Ashish Kapoor, Pradeep Shenoy, Desney S. Tan
Venue: CVPR
Year: 2008

Human-aided computing proposes using information measured directly from the human brain in order to perform useful tasks. In this paper, we extend this idea by fusing computer vision-based processing and processing done by the human brain in order to build more effective object categorization systems. Specifically, we use an electroencephalograph (EEG) device to measure the subconscious cognitive processing that occurs in the brain as users see images, even when they are not trying to explicitly classify them. We present a novel framework that combines a discriminative visual category recognition system based on the Pyramid Match Kernel (PMK) with information derived from EEG measurements as users view images. We propose a fast convex kernel alignment algorithm to effectively combine the two sources of information. Our approach is validated with experiments using real-world data, where we show significant gains in classification accuracy. We analyze the properties of this information fusion method by examining the relative contributions of the two modalities, the errors arising from each source, and the stability of the combination in repeated experiments.
3D occlusion recovery using few cameras
Authors: Mark A. Keck, James W. Davis
Venue: CVPR
Year: 2008

We present a practical framework for detecting and modeling 3D static occlusions for wide-baseline, multi-camera scenarios where the number of cameras is small. The framework consists of an iterative learning procedure where at each frame the occlusion model is used to solve the voxel occupancy problem, and this solution is then used to update the occlusion model. Along with this iterative procedure, there are two contributions of the proposed work: (1) a novel energy function (which can be minimized via graph cuts) specifically designed for use in this procedure, and (2) an application that incorporates our probabilistic occlusion model into a 3D tracking system. Both qualitative and quantitative results of the proposed algorithm and its incorporation with a 3D tracker are presented for support.
3D shape reconstruction of Mooney faces
Authors: Ira Kemelmacher-Shlizerman, Ronen Basri, Boaz Nadler
Venue: CVPR
Year: 2008

Two-tone (ldquoMooneyrdquo) images seem to arouse vivid 3D percept of faces, both familiar and unfamiliar, despite their seemingly poor content. Recent psychological and fMRI studies suggest that this percept is guided primarily by top-down procedures in which recognition precedes reconstruction. In this paper we investigate this hypothesis from a mathematical standpoint. We show that indeed, under standard shape from shading assumptions, a Mooney image can give rise to multiple different 3D reconstructions even if reconstruction is restricted to the Mooney transition curve (the boundary curve between black and white) alone. We then use top-down reconstruction methods to recover the shape of novel faces from single Mooney images exploiting prior knowledge of the structure of at least one face of a different individual. We apply these methods to thresholded images of real faces and compare the reconstruction quality relative to reconstruction from gray level images.
Reconstructing non-stationary articulated objects in monocular video using silhouette information
Authors: Saad M. Khan, Mubarak Shah
Venue: CVPR
Year: 2008

This paper presents an approach to reconstruct non-stationary, articulated objects from silhouettes obtained with a monocular video sequence. We introduce the concept of motion blurred scene occupancies, a direct analogy of motion blurred images but in a 3D object scene occupancy space resulting from the motion/deformation of the object. Our approach starts with an image based fusion step that combines color and silhouette information from multiple views. To this end we propose to use a novel construct: the temporal occupancy point (TOP), which is the estimated 3D scene location of a silhouette pixel and contains information about duration of time it is occupied. Instead of explicitly computing the TOP in 3D space we directly obtain itpsilas imaged(projected) locations in each view. This enables us to handle monocular video and arbitrary camera motion in scenarios where complete camera calibration information may not be available. The result is a set of blurred scene occupancy images in the corresponding views, where the values at each pixel correspond to the fraction of total time duration that the pixel observed an occupied scene location. We then use a motion de-blurring approach to de-blur the occupancy images. The de-blurred occupancy images correspond to a silhouettes of the mean/motion compensated object shape and are used to obtain a visual hull reconstruction of the object. We show promising results on challenging monocular datasets of deforming objects where traditional visual hull intersection approaches fail to reconstruct the object correctly.
Real time object tracking based on dynamic feature grouping with background subtraction
Authors: ZuWhan Kim
Venue: CVPR
Year: 2008

Object detection and tracking has various application areas including intelligent transportation systems. We introduce an object detection and tracking approach that combines the background subtraction algorithm and the feature tracking and grouping algorithm. We first present an augmented background subtraction algorithm which uses a low-level feature tracking as a cue. The resulting background subtraction cues are used to improve the feature detection and grouping result. We then present a dynamic multi-level feature grouping approach that can be used in real time applications and also provides high-quality trajectories. Experimental results from video clips of a challenging transportation application are presented.
Motion estimation for multi-camera systems using global optimization
Authors: Jae-Hak Kim, Hongdong Li, Richard I. Hartley
Venue: CVPR
Year: 2008

We present a motion estimation algorithm for multi-camera systems consisting of more than one calibrated camera securely attached on a moving object. So, they move all together, but do not require to have overlapping views across the cameras. The geometrically optimal solution of the motion for the multi-camera systems under L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> norm is provided in this paper using a global optimization technique which has been introduced recently in the computer vision research field. Taking advantage of an optimal estimate of the essential matrix through searching rotation space, we provide the optimal solution for translation by using linear programming and branch & bound algorithm. Synthetic and real data experiments are conducted, and they show more robust and improved performance than the previous methods.
Conjugate rotation: Parameterization and estimation from an affine feature correspondence
Authors: Kevin Köser, Christian Beder, Reinhard Koch
Venue: CVPR
Year: 2008

When rotating a pinhole camera, images are related by the infinite homography KRK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , which is algebraically a conjugate rotation. Although being a very common image transformation, e.g. important for self-calibration or panoramic image mosaicing, it is not completely understood yet. We show that a conjugate rotation has 7 degrees of freedom (as opposed to 8 for a general homography) and give a minimal parameterization. To estimate the conjugate rotation, authors traditionally made use of point correspondences, which can be seen as local zero order Taylor approximations to the image transformation. Recently however, affine feature correspondences have become increasingly popular. We observe that each such affine correspondence now provides a local first order Taylor approximation, which has not been exploited in the context of geometry estimation before. Using those two novel concepts above, we finally show that it is possible to estimate a conjugate rotation from a single affine feature correspondence under the assumption of square pixels and zero skew. As a byproduct, the proposed algorithm directly yields rotation, focal length and principal point.
Fully automatic feature localization for medical images using a global vector concentration approach
Authors: Tatsuo Kozakaya, Tomoyuki Shibata, Tomoyuki Takeguchi, Masahide Nishiura
Venue: CVPR
Year: 2008

In this paper, we propose a novel feature localization method based on a global vector concentration approach. Our approach does not rely on the detection of local salient features around feature points. Instead, we exploit global structural information of the object extracted by calculating the concentration of directional vectors from sampling points. Those vectors are combined with local pattern descriptors of a query image and selected from preliminarily trained extended templates by nearest neighbor search. Due to the insensitivity of local changes, our method can handle partially occluded and noisy objects. We apply the proposed method to fully automatic feature localization of the left ventricular in echocardiograms. The results show the effectiveness of our method in comparison with a conventional edge-based method in terms of accuracy and robustness.
Efficient object shape recovery via slicing planes
Authors: Po-Lun Lai, Alper Yilmaz 0001
Venue: CVPR
Year: 2008

Recovering the three-dimensional (3D) object shape remains an unresolved area of research on the cross-section of computer vision, photogrammetry and bioinformatics. Although various techniques have been developed, the computational complexity and the constraints introduced to overcome the problems have limited their applicability in the real world scenarios. In this paper, we propose a method that is based on the projective geometry between the object space and the silhouette-images taken from multiple view-points. The approach eliminates the problems related to dense feature point matching and camera calibration that are generally adopted by many state of the art shape reconstruction methods. The object shape is reconstructed by establishing a set of hypothetical planes slicing the object volume and estimating the projective geometric relations between the images of these planes. The experimental results show that the 3D object shape can be recovered by applying minimal constraints.
Correspondences between parts of shapes with particle filters
Authors: Rolf Lakämper, Marc Sobel
Venue: CVPR
Year: 2008

Given two shapes, the correspondence between distinct visual features is the basis for most alignment processes and shape similarity measures. This paper presents an approach introducing particle filters to establish perceptually correct correspondences between point sets representing shapes. Local shape feature descriptors are used to establish correspondence probabilities. The global correspondence structure is calculated using additional constraints based on domain knowledge. Domain knowledge is characterized as prior distributions expressing hypotheses about the global relationships between shapes. These hypotheses are generated during the iterative particle filtering process. Experiments using standard alignment techniques, based on the given correspondence relationships, demonstrate the advantages of this approach.
Partitioning of image datasets using discriminative context information
Authors: Christoph H. Lampert
Venue: CVPR
Year: 2008

We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how well the resulting parts can be separated from a context class of disjoint data points. This is in contrast to typical clustering techniques like K-means that are based on a generative model by implicitly or explicitly searching for modes in the distribution of samples. The discriminative criterion in DCP avoids the problems that density based methods have when the a priori assumption of multimodality is violated, when the number of samples becomes small in relation to the dimensionality of the feature space, or if the cluster sizes are strongly unbalanced. We formulate DCPpsilas separation property as a large-margin criterion, and show how the resulting optimization problem can be solved efficiently. Experiments on the MNIST and USPS datasets of handwritten digits and on a subset of the Caltech256 dataset show that, given a suitable context, DCP can achieve good results even in situation where density-based clustering techniques fail.
Modeling the structure of multivariate manifolds: Shape maps
Authors: Georg Langs, Nikos Paragios
Venue: CVPR
Year: 2008

We propose a shape population metric that reflects the interdependencies between points observed in a set of examples. It provides a notion of topology for shape and appearance models that represents the behavior of individual observations in a metric space, in which distances between points correspond to their joint modeling properties. A Markov chain is learnt using the description lengths of models that describe sub sets of the entire data. The according diffusion map or shape map provides for the metric that reflects the behavior of the training population. With this metric functional clustering, deformation- or motion segmentation, sparse sampling and the treatment of outliers can be dealt with in a unified and transparent manner. We report experimental results on synthetic and real world data and compare the framework with existing specialized approaches.