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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1111-1120 订阅

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Windows detection using K-means in CIE-Lab color space

Windows detection using K-means in CIE-Lab color space

引用

International conference on pattern recognition

作者： Recky, Michal Leberl, Franz Institute for Computer Graphics and Vision TU Graz Austria

ISBN: (纸本)9780769541099

In this paper, we present a method for window detection, robust enough to process complex façades of historical buildings. this method is able to provide results even for facades under severe perspective distortion. Our algorithm is able to detect many different window types and does not require a learning step. We achieve these features thanks to an extended gradient projection method and introduction of a façade color descriptor based on a k-means clustering in a CIE-Lab color space into the process. this method is an important step towards creating large 3D city models in an automated workflow from large online image databases, or industrial systems. As such, it was designed to provide a high level of robustness for processing a large variety of façade types. © 2010 ieee.

关键词： Online systems

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Multiple plane detection in image pairs using J-Linkage

Multiple plane detection in image pairs using J-Linkage

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International conference on pattern recognition

作者： Fouhey, David F. Scharstein, Daniel Briggs, Amy J. Department of Computer Science Middlebury College Middlebury VT 05753 United States

ISBN: (纸本)9780769541099

We present a new method for the robust detection and matching of multiple planes in pairs of images. Such planes can serve as stable landmarks for vision-based urban navigation. Our approach starts from SIFT matches and generates multiple local homography hypotheses using the recent J-linkage technique by Toldo and Fusiello, a robust randomized multi-model estimation algorithm. these hypotheses are then globally merged, spatially analyzed, robustly fitted, and checked for stability. When tested on more than 30,000 image pairs taken from panoramic views of a college campus, our method yields no false positives and recovers 72% of the matchable building walls identified by a human, despite significant occlusions and viewpoint changes. © 2010 ieee.

关键词： Shape optimization

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Detecting paper fibre cross sections in microtomy images

Detecting paper fibre cross sections in microtomy images

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International conference on pattern recognition

作者： Kontschieder, Peter Donoser, Michael Bischof, Horst Kritzinger, Johannes Bauer, Wolfgang Institute for Computer Graphics and Vision Graz University of Technology Austria Institute for Paper Pulp and Fibre Technology Graz University of Technology

ISBN: (纸本)9780769541099

the goal of this work is the fully-automated detection of cellulose fibre cross sections in microtomy images. A lack of significant appearance information makes edges the only reliable cue for detection. We present a novel and highly discriminative edge fragment descriptor that represents angular relations between fragment points. We train a Random Forest with a plurality of these descriptors including their respective center votes. In such a way, the Random Forest exploits the knowledge about the object centroid for detection using a generalized Hough voting scheme. In the experiments we found that our method is able to robustly detect fibre cross sections in microtomy images and can therefore serve as initialization for successive fibre segmentation or tracking algorithms. © 2010 ieee.

关键词： Fibers

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A meta-learning approach to conditional random fields using error-correcting output codes

A meta-learning approach to conditional random fields using ...

引用

International conference on pattern recognition

作者： Ciompi, Francesco Pujol, Oriol Radeva, Petia Dept. of Applied Mathematics and Analysis University of Barcelona Computer Vision Center Spain

ISBN: (纸本)9780769541099

We present a meta-learning framework for the design of potential functions for Conditional Random Fields. the design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. the set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. the Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. the proposed model exhibits very promising results when applied to two real detection problems. © 2010 ieee.

关键词： Ultrasonic applications

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A content spotting system for line drawing graphic document images

A content spotting system for line drawing graphic document ...

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International conference on pattern recognition

作者： Luqman, Muhammad Muzzamil Brouard, thierry Ramel, Jean-Yves Lladós, Josep Laboratoire d'Informatique Université François Rabelais de Tours 37200 France Computer Vision Center Universitat Autónoma de Barcelona 08193 Spain

ISBN: (纸本)9780769541099

We present a content spotting system for line drawing graphic document images. the proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository, represent them by attributed relational graphs, extract regions of interest (ROIs) from them, convert each ROI to a fuzzy structural signature, cluster similar signatures to form ROI classes and build an index for the repository. During online querying mode: a Bayesian network classifier recognizes the ROIs in the query image and the corresponding documents are fetched by looking up in the repository index. Experimental results are presented for synthetic images of architectural and electronic documents. © 2010 ieee.

关键词： Bayesian networks

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Inverse multiple instance learning for classifier grids

Inverse multiple instance learning for classifier grids

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International conference on pattern recognition

作者： Sternig, Sabine Roth, Peter M. Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9780769541099

Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one drawback of such approaches is drifting if an object is not moving over a long period of time. thus, the goal of this work is to increase the recall of such classifiers while preserving their accuracy and speed. In particular, this is realized by adapting ideas from Multiple Instance Learning within a boosting framework. Since the set of positive samples is well defined, we apply this concept to the negative samples extracted from the scene: Inverse Multiple Instance Learning. By introducing temporal bags, we can ensure that each bag contains at least one sample having a negative label, providing the required stability. the experimental results demonstrate that using the proposed approach state-of-the-art detection results can by obtained, however, showing superior classification results in presence of non-moving objects. © 2010 ieee.

关键词： Object detection

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A color invariant based binary coded structured light range scanner for shiny objects

A color invariant based binary coded structured light range ...

引用

International conference on pattern recognition

作者： Benveniste, Rifat Ünsalan, Cem Computer Vision Research Laboratory Department of Electrical and Electronics Engineering Yeditepe University Istanbul 34755 Turkey

ISBN: (纸本)9780769541099

Object range data provide valuable information in recognition and modeling applications. therefore, it is extremely important to reliably extract the range data from a given object. there are various range scanners based on different principles. Among these, structured light based range scanners deserve spacial attention. In these systems, coded light stripes are projected onto the object. Using the bending of these light stripes on the object and the triangulation principle, range information can be obtained. Since this method is simple and fast, it is used in most industrial range scanners. Unfortunately, these range scanners can not scan shiny objects reliably. the main reason is either highlights on the shiny object or the ambient light in the environment. these disturb the coding by illumination. As the code is changed, the range data extracted from it will also be disturbed. In this study, we propose a color invariant based binary coded structured light range scanner to solve this problem. the color invariant used can eliminate the effects of highlights on the object and the ambient light from the environment. this way, we can extract the range data of shiny objects in a robust manner. To test our method, we developed a prototype range scanner. We provide the obtained range data of various test objects with our range scanner. © 2010 ieee.

关键词： Scanning

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On adapting pixel-based classification to unsupervised texture segmentation

On adapting pixel-based classification to unsupervised textu...

引用

2010 20th International conference on pattern recognition, ICPR 2010

作者： Melendez, Jaime Puig, Domenec Angel Garcia, Miguel Intelligent Robotics and Computer Vision Group Rovira I Virgili University Dept. of Computer Science and Mathematics Av. Paisos Catalans 26 43007 Tarragona Spain Autonomous University of Madrid Dept. of Informatics Engineering Francisco Tomas y Valiente 11 28049 Madrid Spain

ISBN: (纸本)9780769541099

An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. the proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of-the-art unsupervised texture segmenters are also provided. © 2010 ieee.

关键词： Clustering algorithms

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Online next-best-view planning for accuracy optimization using an extended E-criterion

Online next-best-view planning for accuracy optimization usi...

引用

International conference on pattern recognition

作者： Trummer, Michael Munkelt, Christoph Denzler, Joachim Friedrich-Schiller University of Jena Department of Computer Vision Germany Fraunhofer Society Optical Systems

ISBN: (纸本)9780769541099

Next-best-view (NBV) planning is an important aspect for three-dimensional (3D) reconstruction within controlled environments, such as a camera mounted on a robotic arm. NBV methods aim at a purposive 3D reconstruction sustaining predefined goals and limitations. Up to now, literature mainly presents NBV methods for range sensors, model-based approaches or algorithms that address the reconstruction of a finite set of primitives. For this work, we use an intensity camera without active illumination. We present a novel combined online approach comprising feature tracking, 3D reconstruction, and NBV planning that addresses arbitrary unknown objects. In particular we focus on accuracy optimization based on the reconstruction uncertainty. To this end we introduce an extension of the statistical E-criterion to model directional uncertainty, and we present a closed-form, optimal solution to this NBV planning problem. Our experimental evaluation demonstrates the effectivity of our approach using an absolute error measure. © 2010 ieee.

关键词： Cameras

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Shape code based word-image matching for retrieval of Indian multi-lingual documents

Shape code based word-image matching for retrieval of Indian...

引用

International conference on pattern recognition

作者： Tarafdar, Arundhati Mondal, Ranju Pal, Srikanta Pal, Umapada Kimura, Fumitaka Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-700108 India Graduate School of Engineering Mie University Kurimamachiya-cho Tsu Japan

ISBN: (纸本)9780769541099

In the current scenario retrieving information from document images is a challenging problem. In this paper we propose a shape code based word-image matching (words-potting) technique for retrieval of multilingual documents written in Indian languages. Here, each query word image to be searched is represented by a primitive shape code using (i) zonal information of extreme points (ii) vertical shape based feature (iii) crossing count (with respect to vertical bar position) (iv) loop shape and position (v) background information etc. Each candidate word (a word having similar aspect ratio and topological feature to the query word) of the document is also coded accordingly. then, an inexact string matching technique is used to measure the similarity between the primitive codes generated from the query word image and each candidate word of the document with which the query image is to be searched. Based on the similarity score, we retrieve the document where the query image is found. Experimental results on Bangla, Devnagari and Gurumukhi scripts document image databases confirm the feasibility and efficiency of our proposed approach. © 2010 ieee.

关键词： Image matching

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