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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51-60 订阅

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Accurate Localization of 3D Objects from RGB-D Data using Segmentation Hypotheses

Accurate Localization of 3D Objects from RGB-D Data using Se...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kim, Byung-soo Xu, Shili Savarese, Silvio Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9780769549897

In this paper we focus on the problem of detecting objects in 3D from RGB-D images. We propose a novel framework that explores the compatibility between segmentation hypotheses of the object in the image and the corresponding 3D map. Our framework allows to discover the optimal location of the object using a generalization of the structural latent SVM formulation in 3D as well as the definition of a new loss function defined over the 3D space in training. We evaluate our method using two existing RGB-D datasets. Extensive quantitative and qualitative experimental results show that our proposed approach outperforms state-of-the-art as methods well as a number of baseline approaches for both 3D and 2D object recognition tasks.

关键词： image colour analysis image segmentation object detection object recognition stereo image processing support vector machines

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First-Person Activity recognition: What Are they Doing to Me?

First-Person Activity Recognition: What Are They Doing to Me...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ryoo, M. S. Matthies, Larry CALTECH Jet Prop Lab Pasadena CA 91125 USA

ISBN: (纸本)9780769549897

this paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. the goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. these include friendly interactions such as 'a person hugging the observer' as well as hostile interactions like 'punching the observer' or 'throwing objects to the observer', whose videos involve a large amount of camera ego-motion caused by physical interactions. the paper investigates multi-channel kernels to integrate global and local motion information, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experiments, we not only show classification results with segmented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably.

关键词： image classification image segmentation interactive video learning (artificial intelligence) object recognition video cameras

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Light Field Distortion Feature for Transparent Object recognition

Light Field Distortion Feature for Transparent Object Recogn...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Maeno, Kazuki Nagahara, Hajime Shimada, Atsushi Taniguchi, Rin-ichiro Kyushu Univ Grad Sch Informat Sci & Elect Engn Nishi Ku Fukuoka 8190395 Japan

ISBN: (纸本)9780769549897

Current object-recognition algorithms use local features, such as scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), for visually learning to recognize objects. these approaches though cannot apply to transparent objects made of glass or plastic, as such objects take on the visual features of background objects, and the appearance of such objects dramatically varies with changes in scene background. Indeed, in transmitting light, transparent objects have the unique characteristic of distorting the background by refraction. In this paper, we use a single-shot light field image as an input and model the distortion of the light field caused by the refractive property of a transparent object. We propose a new feature, called the light field distortion (LFD) feature, for identifying a transparent object. the proposal incorporates this LFD feature into the bag-of-features approach for recognizing transparent objects. We evaluated its performance in laboratory and real settings.

关键词： image classification object recognition refraction

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It's Not Polite To Point: Describing People With Uncertain Attributes

It's Not Polite To Point: Describing People With Uncertain A...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sadovnik, Amir Gallagher, Andrew Chen, Tsuhan Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9780769549897

Visual attributes are powerful features for many different applications in computer vision such as object detection and scene recognition. Visual attributes present another application that has not been examined as rigorously: verbal communication from a computer to a human. Since many attributes are nameable, the computer is able to communicate these concepts through language. However, this is not a trivial task. Given a set of attributes, selecting a subset to be communicated is task dependent. Moreover, because attribute classifiers are noisy, it is important to find ways to deal with this uncertainty. We address the issue of communication by examining the task of composing an automatic description of a person in a group photo that distinguishes him from the others. We introduce an efficient, principled method for choosing which attributes are included in a short description to maximize the likelihood that a third party will correctly guess to which person the description refers. We compare our algorithm to computer baselines and human describers, and show the strength of our method in creating effective descriptions.

关键词： face recognition feature extraction object detection

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Cartesian k-means

Cartesian k-means

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Norouzi, Mohammad Fleet, David J. Univ Toronto Dept Comp Sci Toronto ON M5S 1A1 Canada

ISBN: (纸本)9780769549897

A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity increases super-linearly in the number of parameters. this allows one to effectively quantize data using billions or trillions of centers. We formulate two such models, Orthogonal k-means and Cartesian k-means. they are closely related to one another, to k-means, to methods for binary hash function optimization like ITQ [5], and to Product Quantization for vector quantization [7]. the models are tested on large-scale ANN retrieval tasks (1M GIST, 1B SIFT features), and on codebook learning for object recognition (CIFAR-10).

关键词： feature extraction image representation learning (artificial intelligence) neural nets object recognition pattern clustering transforms vector quantisation

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Discriminative Sub-categorization

Discriminative Sub-categorization

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hoai, Minh Zisserman, Andrew Univ Oxford Oxford England

ISBN: (纸本)9780769549897

the objective of this work is to learn sub-categories. Rather than casting this as a problem of unsupervised clustering, we investigate a weakly supervised approach using both positive and negative samples of the category. We make the following contributions: (i) we introduce a new model for discriminative sub-categorization which determines cluster membership for positive samples whilst simultaneously learning a max-margin classifier to separate each cluster from the negative samples;(ii) we show that this model does not suffer from the degenerate cluster problem that afflicts several competing methods (e.g., Latent SVM and Max-Margin Clustering);(iii) we show that the method is able to discover interpretable sub-categories in various datasets. the model is evaluated experimentally over various datasets, and its performance advantages over k-means and Latent SVM are demonstrated. We also stress test the model and show its resilience in discovering sub-categories as the parameters are varied.

关键词： learning (artificial intelligence) pattern clustering

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Bilinear Programming for Human Activity recognition with Unknown MRF Graphs

Bilinear Programming for Human Activity Recognition with Unk...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Zhenhua Shi, Qinfeng Shen, Chunhua van den Hengel, Anton Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

ISBN: (纸本)9780769549897

Markov Random Fields (MRFs) have been successfully applied to human activity modelling, largely due to their ability to model complex dependencies and deal with local uncertainty. However, the underlying graph structure is often manually specified, or automatically constructed by heuristics. We show, instead, that learning an MRF graph and performing MAP inference can be achieved simultaneously by solving a bilinear program. Equipped with the bilinear program based MAP inference for an unknown graph, we show how to estimate parameters efficiently and effectively with a latent structural SVM. We apply our techniques to predict sport moves (such as serve, volley in tennis) and human activity in TV episodes (such as kiss, hug and Hi-Five). Experimental results show the proposed method outperforms the state-of-the-art.

关键词： Markov processes entertainment graph theory inference mechanisms learning (artificial intelligence) support vector machines

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Watching Unlabeled Video Helps Learn New Human Actions from Very Few Labeled Snapshots

Watching Unlabeled Video Helps Learn New Human Actions from ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Chao-Yeh Grauman, Kristen Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9780769549897

We propose an approach to learn action categories from static images that leverages prior observations of generic human motion to augment its training process. Using unlabeled video containing various human activities, the system first learns how body pose tends to change locally in time. then, given a small number of labeled static images, it uses that model to extrapolate beyond the given exemplars and generate "synthetic" training examples-poses that could link the observed images and/or immediately precede or follow them in time. In this way, we expand the training set without requiring additional manually labeled examples. We explore both example-based and manifold-based methods to implement our idea. Applying our approach to recognize actions in both images and video, we show it enhances a state-of-the-art technique when very few labeled training examples are available.

关键词： computer based training image motion analysis video signal processing

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Wide-baseline Hair Capture using Strand-based Refinement

Wide-baseline Hair Capture using Strand-based Refinement

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Luo, Linjie Zhang, Cha Zhang, Zhengyou Rusinkiewicz, Szymon Princeton Univ Princeton NJ 08544 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9780769549897

We propose a novel algorithm to reconstruct the 3D geometry of human hairs in wide-baseline setups using strand-based refinement. the hair strands are first extracted in each 2D view, and projected onto the 3D visual hull for initialization. the 3D positions of these strands are then refined by optimizing an objective function that takes into account cross-view hair orientation consistency, the visual hull constraint and smoothness constraints defined at the strand, wisp and global levels. Based on the refined strands, the algorithm can reconstruct an approximate hair surface: experiments with synthetic hair models achieve an accuracy of similar to 3mm. We also show real-world examples to demonstrate the capability to capture full-head hair styles as well as hair in motion with as few as 8 cameras.

关键词： image motion analysis image reconstruction object recognition stereo image processing

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Poselet Key-framing: A Model for Human Activity recognition

Poselet Key-framing: A Model for Human Activity Recognition

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Raptis, Michalis Sigal, Leonid Disney Res Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

In this paper, we develop a new model for recognizing human actions. An action is modeled as a very sparse sequence of temporally local discriminative keyframes - collections of partial key-poses of the actor(s), depicting key states in the action sequence. We cast the learning of keyframes in a max-margin discriminative framework, where we treat keyframes as latent variables. this allows us to (jointly) learn a set of most discriminative keyframes while also learning the local temporal context between them. Keyframes are encoded using a spatially-localizable poselet-like representation with HoG and BoW components learned from weak annotations;we rely on structured SVM formulation to align our components and mine for hard negatives to boost localization performance. this results in a model that supports spatio-temporal localization and is insensitive to dropped frames or partial observations. We show classification performance that is competitive with the state of the art on the benchmark UT-Interaction dataset and illustrate that our model outperforms prior methods in an on-line streaming setting.

关键词： image representation image sequences pose estimation support vector machines

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