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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是1211-1220 订阅

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Latent Topic Random Fields: Learning using a taxonomy of labels

Latent Topic Random Fields: Learning using a taxonomy of lab...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Xuming He Richard S. Zemel University of Toronto Canada

An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn from a semantic hierarchy, and can also readily cope with missing labels, and roughly-specified object boundaries. We introduce a new form of latent topic model, learning a novel context representation in the joint label-and-image space by capturing co-occurring patterns within and between image features and object labels. Given a topic, the model generates the input data, as well as a topic-dependent probabilistic classifier to predict labels for image regions. We present results on two real-world datasets, demonstrating significant improvements gained by including the coarsely labeled images.

关键词： Taxonomy Labeling Context modeling Predictive models Pixel Vocabulary Helium pattern analysis Tagging Text analysis

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From Skeletons to Bone Graphs: Medial Abstraction for Object recognition

From Skeletons to Bone Graphs: Medial Abstraction for Object...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Diego Macrini Kaleem Siddiqi Sven Dickinson University of Toronto Canada McGill University Canada

Medial descriptions, such as shock graphs, have gained significant momentum in the shape-based object recognition community due to their invariance to translation, rotation, scale and articulation and their ability to cope with moderate amounts of within-class deformation. While they attempt to decompose a shape into a set of parts, this decomposition can suffer from ligature-induced instability. In particular, the addition of even a small part can have a dramatic impact on the representation in the vicinity of its attachment. We present an algorithm for identifying and representing the ligature structure, and restoring the non-ligature structures that remain. this leads to a bone graph, a new medial shape abstraction that captures a more intuitive notion of an object's parts than a skeleton or a shock graph, and offers improved stability and within-class deformation invariance. We demonstrate these advantages by comparing the use of bone graphs to shock graphs in a set of view-based object recognition and pose estimation trials.

关键词： Skeleton Bones Object recognition Shape Electric shock Leg Image segmentation pattern recognition Stability Tail

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Semi-Supervised Boosting using Visual Similarity Learning

Semi-Supervised Boosting using Visual Similarity Learning

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Christian Leistner Helmut Grabner Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria

the required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semi-supervised learning holds the promise to ease the tedious and time consuming labeling effort. this paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. the approach capable of exploiting both labeled and unlabeled data is formulated in a boosting framework. One classifier (the learned similarity) serves as a prior which is steadily improved via training a second classifier on labeled and unlabeled samples. We demonstrate the approach on challenging computer vision applications. First, we show how we can train a classifier using only a few labeled samples and many unlabeled data. Second, we improve (specialize) a state-of-the-art detector by using labeled and unlabeled data.

关键词： Boosting Semisupervised learning computer vision Detectors Training data Application software Machine learning Object detection Support vector machines Face detection

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Closing the Loop in Scene Interpretation

Closing the Loop in Scene Interpretation

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Derek Hoiem Alexei A. Efros Martial Hebert Beckman Institute University of Illinois USA Robotics Institute Carnegie Mellon University USA

Image understanding involves analyzing many different aspects of the scene. In this paper, we are concerned with how these tasks can be combined in a way that improves the performance of each of them. Inspired by Barrow and Tenenbaum, we present a flexible framework for interfacing scene analysis processes using intrinsic images. Each intrinsic image is a registered map describing one characteristic of the scene. We apply this framework to develop an integrated 3D scene understanding system with estimates of surface orientations, occlusion boundaries, objects, camera viewpoint, and relative depth. Our experiments on a set of 300 outdoor images demonstrate that these tasks reinforce each other, and we illustrate a coherent scene understanding with automatically reconstructed 3D models.

关键词： Layout Image analysis Surface reconstruction Feedforward systems Object detection Inference algorithms Robot kinematics Cameras Object recognition Reflectivity

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Granularity and Elasticity Adaptation in Visual Tracking

Granularity and Elasticity Adaptation in Visual Tracking

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Ming Yang Ying Wu Department of EECS Northwestern University Evanston IL USA

the observation models in tracking algorithms are critical to both tracking performance and applicable scenarios but are often simplified to focus on fixed level of certain target properties such as appearances and structures. In this paper, we propose a unified tracking paradigm in which targets are represented by Markov random fields of interest regions and introduce a new way to adapt observation models by automatically tuning the feature granularity and model elasticity, i.e. the abstraction level of features and the model's degree of flexibility to tolerate deformations. Specifically, we employ a multi-scale scheme to extract features from interest regions and adjust the parameters of the potential functions of the MRF model to maximize the likelihoods of tracking results. Experiments demonstrate the method can estimate translation, scaling and rotation and deal with deformation, partial occlusions, and camouflage objects within this unified framework.

关键词： Elasticity Target tracking Deformable models Solid modeling Feature extraction Markov random fields Statistics Humans Motion analysis pattern matching

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A Statistical Deformation Prior for Non-Rigid Image and Shape Registration

A Statistical Deformation Prior for Non-Rigid Image and Shap...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： thomas Albrecht Marcel Luthi thomas Vetter Computer Science Department University of Basel Basel Switzerland

Non-rigid registration is central to many problems in computer vision and medical image analysis. We propose a registration algorithm which is regularized by prior knowledge in the form of a statistical deformation model. this model is obtained from previous registrations performed on a set of noise-free training examples given by images, or shapes represented by level set functions. Contrary to similar approaches, our method does not strictly constrain the result to lie in the span of the statistical model but rather uses the model for Tikhonov regularization. therefore, our method can be used to reduce the influence of noise and artifacts even when the model contains only a few typical examples. this automatically gives rise to a bootstrapping strategy for building statistical models from noisy data sets requiring only a limited number of high quality examples. We demonstrate the effectiveness of the approach on synthetic and medical images.

关键词： Shape Deformable models Image registration Biomedical imaging computer vision Image analysis Noise shaping Image segmentation computer science Noise level

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Efficient Object Shape Recovery via Slicing Planes

Efficient Object Shape Recovery via Slicing Planes

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Po-Lun Lai Alper Yilmaz Photogrammetric Computer Vision Laboratory Ohio State Uinversity Columbus OH USA

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.

关键词： Shape Cameras Image reconstruction Calibration Layout Geometry computer vision Computational complexity Image analysis Equations

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Object Categorization using Co-Occurrence, Location and Appearance

Object Categorization using Co-Occurrence, Location and Appe...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Carolina Galleguillos Andrew Rabinovich Serge Belongie Department of Computer Science and Engineering University of California San Diego USA

In this work we introduce a novel approach to object categorization that incorporates two types of context - co-occurrence and relative location - with local appearance-based features. Our approach, named CoLA (for Co-occurrence, Location and Appearance), uses a conditional random field (CRF) to maximize object label agreement according to both semantic and spatial relevance. We model relative location between objects using simple pairwise features. By vector quantizing this feature space, we learn a small set of prototypical spatial relationships directly from the data. We evaluate our results on two challenging datasets: PASCAL 2007 and MSRC. the results show that combining co-occurrence and spatial context improves accuracy in as many as half of the categories compared to using co-occurrence alone.

关键词： Layout Object recognition Image segmentation Context modeling computer science Prototypes Face detection Lighting Psychology computer vision

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Facial Expression recognition Using Encoded Dynamic Features

Facial Expression Recognition Using Encoded Dynamic Features

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Peng Yang Qingshan Liu Xinyi Cui Dimitris N. Metaxas Computer Science Department Rutgers University Piscataway NJ USA National Laboratory of Pattern Recognition Chinese Academy and Sciences Beijing China

In this paper, we propose a novel framework for video-based facial expression recognition, which can handle the data with various time resolution including a single frame. We first use the haar-like features to represent facial appearance, due to their simplicity and effectiveness. then we perform K-Means clustering on the facial appearance features to explore the intrinsic temporal patterns of each expression. Based on the temporal pattern models, we further map the facial appearance variations into dynamic binary patterns. Finally, boosting learning is performed to construct the expression classifiers. Compared to previous work, the dynamic binary patterns encode the intrinsic dynamics of expression, and our method makes no assumption on the time resolution of the data. Extensive experiments carried on the Cohn-Kanade database show the promising performance of the proposed method.

关键词： Face recognition pattern recognition Cameras computer science Laboratories Boosting Databases Psychology Active shape model Multi-stage noise shaping

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Action Snippets: How many frames does human action recognition require?

Action Snippets: How many frames does human action recogniti...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Konrad Schindler Luc van Gool BIWI ETH Zurich Switzerland ESAT-K.U. Leuven Leuven Belgium

Visual recognition of human actions in video clips has been an active field of research in recent years. However, most published methods either analyse an entire video and assign it a single action label, or use relatively large look-ahead to classify each frame. Contrary to these strategies, human vision proves that simple actions can be recognised almost instantaneously. In this paper, we present a system for action recognition from very short sequences ("snippets") of 1-10 frames, and systematically evaluate it on standard data sets. It turns out that even local shape and optic flow for a single frame are enough to achieve ≈ 90% correct recognitions, and snippets of 5-7 frames (0.3-0.5 seconds of video) are enough to achieve a performance similar to the one obtainable with the entire video sequence.

关键词： Humans Image motion analysis Video sequences Feature extraction Shape Legged locomotion Layout Surveillance Voting Visual databases

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