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Notice of Violation of ieee Publication Principles: Hand gesture recognition framework for recognizing sign gestures and handling movement epenthesis using Level Building nested dynamic programming approach

Notice of Violation of IEEE Publication Principles: Hand ges...

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Canadian conference on Electrical and computer Engineering (CCECE)

作者： R Elakkiya K Selvamani S Kanimozhi Agni College of Technology Chennai Anna University Chennai

Notice of Violation of ieee Publication Principles “Hand Gesture recognition Framework for Recognizing Sign Gestures and Handling Movement Epenthesis Using Level Building Nested Dynamic Programming Approach” by Elakkiya A., Selvamani K., Kanimozhi S. in the Proceedings of the ieee 27th Canadian conference on Electrical and computer Engineering (CCECE), May 2014 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of ieee’s Publication Principles. this paper duplicated extensive amounts of text from the paper cited below. the original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. A. Elakkiya was solely responsible for the copied material. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: “Enhanced Level Building Algorithm for the Movement Epenthesis Problem in Sign Language recognition” by Ruiduo Yang, Sudeep Sarkar, Barbara Loeding in the Proceedings of the ieee conference on computer vision and pattern recognition (cvpr), June 2007 In this research paper, two crucial problems in continuous sign language recognition from unaided video sequences are considered. At the feature level, the problem of hand segmentation and grouping is considered and at the sentence level, the movement epenthesis problem is considered. A framework that can handle both of these problems based on an enhanced, nested version of the dynamic programming approach is constructed. To handle movement epenthesis problem, a nested version of a dynamic programming framework, called Level Building is used to simultaneously segment and to match signs from continuous sign language sentences. this approach is then coupled with a trigram grammar model to optimally segment and l

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A Low-cost Approach Towards Streaming 3D Videos of Large-scale Sport Events to Mixed Reality Headsets in Real-time

A Low-cost Approach Towards Streaming 3D Videos of Large-sca...

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27th ieee conference on Virtual Reality and 3D User Interfaces (ieee VR)

作者： Marty, Kevin Rajasekaran, Prithvi Sun, Yongbin Fuchs, Klaus MIT Auto ID Labs Cambridge MA 02139 USA ETHZ Zurich Switzerland

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165325

Watching sports events via 3D-instead of two-dimensional video streaming allows for increased immersion, e.g. via mixed reality headsets in comparison to traditional screens. So far, capturing 3D video of sports events required expensive outside-in tracking with numerous cameras. this study demonstrates the feasibility of streaming sports content to mixed reality headsets as holographs in real-time using inside-out tracking and low-cost equipment only. We demonstrate our system by streaming a race car on an indoor track as 3D models, which are then rendered in an Magic Leap One headset. An onboard camera, mounted on the race car provides the video stream used to localize the car via computer vision. the localization is estimated by an end-to-end convolutional neural network (CNN). the study compares three state-of-the-art CNN models in their respective accuracy and execution time, with PoseNet+LSTM achieving position and orientation accuracy of 0.35m and 3.95 degrees. the total streaming latency in this study was 1041ms, suggesting technical feasibility of streaming 3D sports content, e.g. on large playgrounds, in near real-time onto mixed-reality headsets.

关键词： Augmented Reality Visualization Head mounted display Sport streaming Deep learning Image processing pattern recognition Localization

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Nonparametric Part Transfer for Fine-grained recognition

Nonparametric Part Transfer for Fine-grained Recognition

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ieee conference on computer vision and pattern recognition

作者： Christoph Goring Erik Rodner Alexander Freytag Joachim Denzler Friedrich-Schiller-Universitat Jena Jena ThÃ¼ringen DE Computer Vision Group Friedrich Schiller University Jena

In the following paper, we present an approach for fine-grained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations from objects with similar global shapes. the possibility for transferring part annotations to unseen images allows for coping with a high degree of pose and view variations in scenarios where traditional detection models (such as deformable part models) fail. Our approach is especially valuable for fine-grained recognition scenarios where intraclass variations are extremely high, and precisely localized features need to be extracted. Furthermore, we show the importance of carefully designed visual extraction strategies, such as combination of complementary feature types and iterative image segmentation, and the resulting impact on the recognition performance. In experiments, our simple yet powerful approach achieves 35.9% and 57.8% accuracy on the CUB-2010 and 2011 bird datasets, which is the current best performance for these benchmarks.

关键词： Feature extraction Image color analysis Birds Visualization Training Shape Deformable models

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Modern Advances in Applied Intelligence 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Moonis Ali Jeng-Shyang Pan Shyi-Ming Chen Mong-Fong Horng

ISBN: (数字)9783319074559

ISBN: (纸本)9783319074542

the two volume set LNAI 8481 and 8482 constitutes the refereed conference proceedings of the 27th International conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, held in Kaohsiung, Taiwan, in June 2014. the total of 106 papers selected for the proceedings were carefully reviewed and selected from various submissions. the papers deal with a wide range of topics from applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation and robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace and human-machine interaction.

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery computer Imaging, vision, pattern recognition and Graphics

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Modern Advances in Applied Intelligence 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Moonis Ali Jeng-Shyang Pan Shyi-Ming Chen Mong-Fong Horng

ISBN: (数字)9783319074672

ISBN: (纸本)9783319074665

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Learning Deep Features for Discriminative Localization

Learning Deep Features for Discriminative Localization

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ieee conference on computer vision and pattern recognition

作者： Bolei Zhou Aditya Khosla Agata Lapedriza Aude Oliva Antonio Torralba Computer Science and Artificial Intelligence Laboratory MIT

ISBN: (纸本)9781467388528

In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability despite being trained on image-level labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that exposes the implicit attention of CNNs on an image. Despite the apparent simplicity of global average pooling, we are able to achieve 37.1% top-5 error for object localization on ILSVRC 2014 without training on any bounding box annotation. We demonstrate in a variety of experiments that our network is able to localize the discriminative image regions despite just being trained for solving classification task.

关键词： region of an image pooling bounding boxes Imagery (Psychotherapy) images SCRAM Functional training

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Partial Optimality by Pruning for MAP-Inference with General Graphical Models

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ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1370-82页

作者： Swoboda, Paul Shekhovtsov, Alexander Kappes, Joerg Hendrik Schnoerr, Christoph Savchynskyy, Bogdan Heidelberg Univ Image & Pattern Anal Grp IPA Speyerer Str 6 D-69115 Heidelberg Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16 A-8010 Graz Austria Heidelberg Univ Heidelberg Collaboratory Image Proc HCI Speyerer Str 6 D-69115 Heidelberg Germany

We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. the method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method's runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.

关键词： MAP-inference Markov random fields energy minimization persistency partial optimality local polytope

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DeepFace: Closing the Gap to Human-Level Performance in Face Verification

DeepFace: Closing the Gap to Human-Level Performance in Face...

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ieee conference on computer vision and pattern recognition

作者： Yaniv Taigman Ming Yang Marc'Aurelio Ranzato Lior Wolf Facebook AI Research Tel Aviv University

ISBN: (纸本)9781479951192

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. this deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities. the learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

关键词： Face three-dimensional displays Training Face recognition Agriculture Solid modeling Shape

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Rethinking the Inception Architecture for computer vision

Rethinking the Inception Architecture for Computer Vision

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ieee conference on computer vision and pattern recognition

作者： Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens Zbigniew Wojna Google Inc. University College London

ISBN: (纸本)9781467388528

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error and 17.3% top-1 error on the validation set and 3.6% top-5 error on the official test set.

关键词： Convolution computer architecture Training Computational efficiency computer vision Benchmark testing Computational modeling

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Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks

Online Detection and Classification of Dynamic Hand Gestures...

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ieee conference on computer vision and pattern recognition

作者： Pavlo Molchanov Xiaodong Yang Shalini Gupta Kihwan Kim Stephen Tyree Jan Kautz NVIDIA

ISBN: (纸本)9781467388528

Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult;2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification;in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three-dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from multi-modal data. We employ connectionist temporal classification to train the network to predict class labels from inprogress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multimodal dynamic hand gesture dataset captured with depth, color and stereo-IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83.8%, outperforms competing state-of-the-art algorithms, and approaches human accuracy of 88.4%. Moreover, our method achieves state-of-the-art performance on SKIG and ChaLearn2014 benchmarks.

关键词： Gestures Hand Taxonomy computer human interaction automatic detection Neural network

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