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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621-630 订阅

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Performance evaluation of DTW and its variants for word spotting in degraded documents 13

Performance evaluation of DTW and its variants for word spot...

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13th International conference on Document Analysis and recognition, ICDAR 2015

作者： Mondal, Tanmoy Ragot, Nicolas Ramel, Jean-Yves Pal, Umapada Tours France Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781479918058

In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. this paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. this research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results. © 2015 ieee.

关键词： Dynamic programming

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Writer Identification from offline isolated Bangla characters and numerals 13

Writer Identification from offline isolated Bangla character...

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13th International conference on Document Analysis and recognition, ICDAR 2015

作者： Adak, Chandranath Chaudhuri, Bidyut B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9781479918058

Writer identification is an essential component in computational forensic. In this paper, we attempt to do this job based only on isolated characters and numerals. For that, at first, some points of interest (keypoints) on the image are detected by structural analysis and SIFT based detector. then we calculate a set of features within a certain neighborhood of the keypoint and employ fusion rule on multiple probabilistic SVM classifiers output for writer identification. For experimental analysis, a database containing 212,300 isolated Bangla orthosyllabic characters and numerals are generated with the help of 100 writers. We obtain fairly good result to identify a writer. We also try to find a small set of highly discriminative characters storing extra information about the writing style of an individual. © 2015 ieee.

关键词： handwriting recognition natural language processing probability support vector machines Bangla orthosyllabic characters SIFT based detector computational forensic isolated Bangla characters isolated Bangla numerals isolated characters multiple probabilistic SVM classifiers structural analysis writer identification Chlorine Discriminative Bangla character Handwriting analysis Writer identification Numerals handwriting recognition structural analysis Structural analysis Support Vector Network Natural Language Processing Writers Chlorine

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Rotating your face using multi-task deep neural network

Rotating your face using multi-task deep neural network

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conference on computer vision and pattern recognition (cvpr)

作者： Junho Yim Heechul Jung ByungIn Yoo Changkyu Choi Dusik Park Junmo Kim KAIST School of Electrical Engineering South Korea Samsung Advanced Institute of Technology

ISBN: (纸本)9781467369657

Face recognition under viewpoint and illumination changes is a difficult problem, so many researchers have tried to solve this problem by producing the pose- and illumination- invariant feature. Zhu et al. [26] changed all arbitrary pose and illumination images to the frontal view image to use for the invariant feature. In this scheme, preserving identity while rotating pose image is a crucial issue. this paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity. the target pose can be controlled by the user's intention. this novel type of multi-task model significantly improves identity preservation over the single task model. By using all the synthesized controlled pose images, called Controlled Pose Image (CPI), for the pose-illumination-invariant feature and voting among the multiple face recognition results, we clearly outperform the state-of-the-art algorithms by more than 4~6% on the MultiPIE dataset.

关键词： Face Lighting Face recognition Feature extraction Image reconstruction Training three-dimensional displays

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Exemplary Sequence Cardinality: An effective application for word spotting 13

Exemplary Sequence Cardinality: An effective application for...

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13th International conference on Document Analysis and recognition, ICDAR 2015

作者： Mondal, Tanmoy Ragot, Nicolas Ramel, Jean-Yves Pal, Umapada France Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781479918058

In this paper proposed. ESC combines several abilities of other sequence matching algorithms e.g. DTW, SSDTW, CDP, FSM, MVM, OSB1. Depending on the application domain, ESC can be tuned to∗∗a new sequence matching algorithm called as Exemplary Sequence Cardinality (ESC) is behave such as these different sequence matching algorithms. Its generality and robustness comes from its ability to find subsequences (as in CDP and SSDTW), to skip outliers inside the target sequences (as in MVM and FSM) and also in the query sequence (as in OSB ) and it has the ability to have many to one and one to many correspondences (as in DTW) between the elements of the query and the target sequences. It's special characteristic of skipping noisy elements from query sequence along with other afore mentioned properties gives it an edge over FSM. In case of word spotting application, the outliers skipping capability of ESC makes it less sensible to local variations in the spelling of words, and also to noise present in the query and/or in the target word images. Due to it's capability of sub-sequence matching, the ESC algorithm has the ability to retrieve a query inside a line or piece of line. Finally, its multiple matching facilities (many to one and one to many matching) is proven to be well advantageous in case of different length of target and query sequences due to the variability in scale, font, type/size factors. By experimenting on printed historical document images, we have demonstrated the interest of proposed ESC algorithm in specific cases when incorrect word segmentation and word level local variations occur regularly. © 2015 ieee.

关键词： Dynamic programming

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DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets

DEEP-CARVING: Discovering Visual Attributes by Carving Deep ...

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ieee conference on computer vision and pattern recognition

作者： Sukrit Shankar Vikas K. Garg Roberto Cipolla Machine Intelligence Lab (MIL) Cambridge University Computer Science & Artificial Intelligence Lab (CSAIL) MIT

ISBN: (纸本)9781467369657

Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines. For instance, given a noun (say forest) and its associated attributes (say dense, sunlit, autumn), search engines can now generate many valid images for any attribute-noun pair (dense forests, autumn forests, etc). However, images for an attribute-noun pair do not contain any information about other attributes (like which forests in the autumn are dense too). thus, a weakly supervised scenario occurs: each of the M attributes corresponds to a class such that a training image in class m ∈ {1,...,M} contains a single label that indicates the presence of the m~(th) attribute only. the task is to discover all the attributes present in a test image. Deep Convolutional Neural Networks (CNNs) [20] have enjoyed remarkable success in vision applications recently. However, in a weakly supervised scenario, widely used CNN training procedures do not learn a robust model for predicting multiple attribute labels simultaneously. the primary reason is that the attributes highly co-occur within the training data, and unlike objects, do not generally exist as well-defined spatial boundaries within the image. To ameliorate this limitation, we propose Deep-Carving, a novel training procedure with CNNs, that helps the net efficiently carve itself for the task of multiple attribute prediction. During training, the responses of the feature maps are exploited in an ingenious way to provide the net with multiple pseudo-labels (for training images) for subsequent iterations. the process is repeated periodically after a fixed number of iterations, and enables the net carve itself iteratively for efficiently disentangling features. Additionally, we contribute a noun-adjective pairing inspired Natural Scenes Attribute

关键词： feature map Dataset images Search engines Neural network iterative methods Sculpture Exploration Sun

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Probabilistic Labeling Cost for High-Accuracy Multi-View Reconstruction 27

Probabilistic Labeling Cost for High-Accuracy Multi-View Rec...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kostrikov, Ilya Horbert, Esther Leibe, Bastian Rhein Westfal TH Aachen Comp Vis Grp Aachen Germany

ISBN: (纸本)9781479951178

In this paper, we propose a novel labeling cost for multi-view reconstruction. Existing approaches use data terms with specific weaknesses that are vulnerable to common challenges, such as low-textured regions or specularities. Our new probabilistic method implicitly discards outliers and can be shown to become more exact the closer we get to the true object surface. Our approach achieves top results among all published methods on the Middlebury DINO SPARSE dataset and also delivers accurate results on several other datasets with widely varying challenges, for which it works in unchanged form.

关键词： pattern recognition

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Histograms of pattern Sets for Image Classification and Object recognition 27

Histograms of Pattern Sets for Image Classification and Obje...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Voravuthikunchai, Winn Cremilleux, Bruno Jurie, Frederic Univ Caen Basse Normandie CNRS UMR 6072 ENSICAEN Caen France

ISBN: (纸本)9781479951178

this paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. this representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. the method relies on (i) multiple random projections of the input space followed by local binarization of projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of pattern Sets (HoPS). the approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, Kth Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. the proposed image representation reaches state-of-the-art performance on each one of these datasets.

关键词： computer vision data mining image classification object detection visual recognition

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A Compositional Model for Low-Dimensional Image Set Representation 27

A Compositional Model for Low-Dimensional Image Set Represen...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mobahi, Hossein Liu, Ce Freeman, William T. MIT Cambridge MA 02139 USA Microsoft Res Cambridge MA USA

ISBN: (纸本)9781479951178

Learning a low-dimensional representation of images is useful for various applications in graphics and computer vision. Existing solutions either require manually specified landmarks for corresponding points in the images, or are restricted to specific objects or shape deformations. this paper alleviates these limitations by imposing a specific model for generating images;the nested composition of color, shape, and appearance. We show that each component can be approximated by a low-dimensional subspace when the others are factored out. Our formulation allows for efficient learning and experiments show encouraging results.

关键词： images image set point image graphic images computer vision representations

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Transparent Object Reconstruction via Coded Transport of Intensity 27

Transparent Object Reconstruction via Coded Transport of Int...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ma, Chenguang Lin, Xing Suo, Jinli Dai, Qionghai Wetzstein, Gordon Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781479951178

Capturing and understanding visual signals is one of the core interests of computer vision. Much progress has been made w.r.t. many aspects of imaging, but the reconstruction of refractive phenomena, such as turbulence, gas and heat flows, liquids, or transparent solids, has remained a challenging problem. In this paper, we derive an intuitive formulation of light transport in refractive media using light fields and the transport of intensity equation. We show how coded illumination in combination with pairs of recorded images allow for robust computational reconstruction of dynamic two and three-dimensional refractive phenomena.

关键词： pattern recognition

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Hierarchical Feature Hashing for Fast Dimensionality Reduction 27

Hierarchical Feature Hashing for Fast Dimensionality Reducti...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Bin Xing, Eric P. Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9781479951178

Curse of dimensionality is a practical and challenging problem in image categorization, especially in cases with a large number of classes. Multi-class classification encounters severe computational and storage problems when dealing with these large scale tasks. In this paper, we propose hierarchical feature hashing to effectively reduce dimensionality of parameter space without sacrificing classification accuracy, and at the same time exploit information in semantic taxonomy among categories. We provide detailed theoretical analysis on our proposed hashing method. Moreover, experimental results on object recognition and scene classification further demonstrate the effectiveness of hierarchical feature hashing.

关键词： Object recognition

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