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Learning texton models for real-time scene context

Learning texton models for real-time scene context

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Alex Flint Ian Reid David Murray Active Vision Laboratory Oxford University UK

ISBN: (纸本)9781424439942

We present a new model for scene context based on the distribution of textons within images. Our approach provides continuous, consistent scene gist throughout a video sequence and is suitable for applications in which the camera regularly views uninformative parts of the scene. We show that our model outperforms the state-of-the-art for place recognition. We further show how to deduce the camera orientation from our scene gist and finally show how our system can be applied to active object search.

关键词： Context modeling Layout Cameras Object detection computer vision Humans Laboratories Video sequences Lighting Feedback

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Scenes vs. objects: A comparative study of two approaches to context based recognition

Scenes vs. objects: A comparative study of two approaches to...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrew Rabinovich Serge Belongie Google Inc. USA University of California San Diego USA

Contextual models play a very important role in the task of object recognition. Over the years, two kinds of contextual models have emerged: models with contextual inference based on the statistical summary of the scene (we will refer to these as scene based context models, or SBC), and models representing the context in terms of relationships among objects in the image (object based context, or OBC). In designing object recognition systems, it is necessary to understand the theoretical and practical properties of such approaches. This work provides an analysis of these models and evaluates two of their representatives using the LabelMe dataset. We demonstrate a considerable margin of improvement using the OBC style approach.

关键词： Layout Context modeling Refrigerators Object recognition Statistics Keyboards Fires computer vision Information resources Humans

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A syntax for image understanding

A syntax for image understanding

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Narendra Ahuja Department of Electrical and Computer Engineering Coordinated Science Laboratory and Beckman Institute University of Illinois Urbana-Champaign USA

We consider one of the most basic questions in computer vision, that of finding a low-level image representation that could be used to seed diverse, subsequent computations of image understanding. Can we define a relatively general purpose image representation which would serve as the syntax for diverse needs of image understanding? What makes good image syntax? How do we evaluate it? We pose a series of such questions and evolve a set of answers to them, which in turn help evolve an image representation. For concreteness, we first perform this exercise in the specific context of the following problem.

关键词： Image segmentation Object oriented modeling computer vision Image representation Photometry Layout Tree graphs Taxonomy Humans Probability distribution

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Beyond one-to-one feature correspondence: The need for many-to-many matching and image abstraction

Beyond one-to-one feature correspondence: The need for many-...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sven Dickinson Department of Computer Science University of Toronto Canada

Summary form only given: In this paper briefly review three formulations of the many-to-many matching problem as applied to model acquisition, model indexing, and object recognition. In the first scenario, I will describe the problem of learning a prototypical shape model from a set of exemplars in which the exemplars may not share a single local feature in common. We formulate the problem as a search through the intractable space of feature combinations, or abstractions, to find the "lowest common abstraction" that is derivable from each input exemplar. This abstraction, in turn, defines a many-to-many feature correspondence among the extracted input features.

关键词： Shape Indexing Object recognition Prototypes Feature extraction computer science Artificial intelligence Focusing Noise shaping Image segmentation

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Synchronization and rolling shutter compensation for consumer video camera arrays

Synchronization and rolling shutter compensation for consume...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Derek Bradley Bradley Atcheson Ivo Ihrke Wolfgang Heidrich University of British Columbia Canada

Two major obstacles to the use of consumer camcorders in computer vision applications are the lack of synchronization hardware, and the use of a "rolling" shutter, which introduces a temporal shear in the video volume. We present two simple approaches for solving both the rolling shutter shear and the synchronization problem at the same time. The first approach is based on strobe illumination, while the second employs a subframe warp along optical flow vectors. In our experiments we have used the proposed methods to effectively remove temporal shear, and synchronize up to 16 consumer-grade camcorders in multiple geometric configurations.

关键词： Cameras Video equipment computer vision Application software Image motion analysis Video sequences Hardware Lighting High speed optical techniques Optical sensors

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Multi-modal laughter recognition in video conversations

Multi-modal laughter recognition in video conversations

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sergio Escalera Eloi Puertas Petia Radeva Oriol Pujol Universitat de Barcelona Barcelona Spain

Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier.

关键词： Human computer interaction Pervasive computing Speech recognition Mouth Feedback Event detection Automatic speech recognition Hidden Markov models Support vector machines Support vector machine classification

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High Level Activity recognition using Low Resolution Wearable vision

High Level Activity Recognition using Low Resolution Wearabl...

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Sudeep Sundaram Walterio W. Mayol Cuevas Department of Computer Science University of Bristol UK

ISBN: (纸本)9781424439942

This paper presents a system aimed to serve as the enabling platform for a wearable assistant. The method observes manipulations from a wearable camera and classifies activities from roughly stabilized low resolution images (160×120 pixels) with the help of a 3-level Dynamic Bayesian Network and adapted temporal templates. Our motivation is to explore robust but computationally inexpensive visual methods to perform as much activity inference as possible without resorting to more complex object or hand detectors. The description of the method and results obtained are presented, as well as the motivation for further work in the area of wearable visual sensing.

关键词： Cameras Wearable sensors Bayesian methods Manipulator dynamics Robustness Sensor arrays Image resolution Pixel Object detection Wearable computers

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A fast mouth detection algorithm based on face organs

A fast mouth detection algorithm based on face organs

引用

2009 2nd conference on Power Electronics and Intelligent Transportation System, PEITS 2009

作者： Wang, Qi Yang, Sheng Li, Xian-Wei College of Computer and Communication Hunan University Changsha Hunan China

ISBN: (纸本)9781424442461

Automatic mouth detection in a complex background is one of the most challenging and significant tasks in the field of computer vision and pattern recognition. A new mouth detection method that uses the relationships of face organs based on the award-winning mosaic method. The new method redesigns the policy of face blocks. Experimental results show that the algorithm can finds out the mouth region rapidly. It is also useful in a wide range. Moreover, it is effectual for complex background such as public mouth detection. ©2009 ieee.

关键词： pattern recognition

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Learning a hierarchical compositional representation of multiple object classes

Learning a hierarchical compositional representation of mult...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ales Leonardis University of Ljubljana Slovenia

Summary form only given. Visual categorization, recognition, and detection of objects has been an area of active research in the vision community for decades. Ultimately, the goal is to recognize and detect a large number of object classes in images within an acceptable time frame. This problem entangles three highly interconnected issues: the internal object representation which should expand sublinearly with the number of classes, means to learn the representation from a set of images, and an effective inference algorithm that matches the object representation against the representation produced from the scene. In the main part of the talk I will present our framework for learning a hierarchical compositional representation of multiple object classes. Learning is unsupervised, statistical, and is performed bottom-up. The approach takes simple contour fragments and learns their frequent spatial configurations which recursively combine into increasingly more complex and class-specific contour compositions.

关键词： Object detection computer vision Image recognition Inference algorithms Layout Solid modeling Rendering (computer graphics) Streaming media Hardware Production

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Multilinear isometric embedding for visual pattern analysis

Multilinear isometric embedding for visual pattern analysis

引用

2009 ieee 12th International conference on computer vision workshops, ICCV workshops 2009

作者： Liu, Yan Liu, Yang Chan, Keith C. C. Department of Computing Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

ISBN: (纸本)9781424444427

This paper proposes a novel tensor based dimensionality reduction algorithm called Multilinear Isometric Embedding (MIE) based on a representative manifold learning algorithm Isomap. Unlike Isomap that unfolds input data to the vector form, MIE directly works on more general tensor representation and utilizes iterative strategy to seek the low-dimensional equivalence, which best preserves the global geometry. By avoiding the problems caused by data vectorization, MIE reduces the data analysis difficulty and computational cost. More importantly, MIE keeps the intrinsic tensor structure of the data in low-dimensional representation. Meanwhile, MIE inherits the merits of Isomap, i.e., the ability of uncovering the global geometry of high-dimensional observations. By providing explicit embedding function, MIE makes the embedding of new data points to the low-dimensional space straightforward. Experiments on various datasets validate the effectiveness of proposed method. ©2009 ieee.

关键词： Tensors

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