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Image parsing with stochastic grammar: The Lotus Hill dataset and inference scheme

Image parsing with stochastic grammar: The Lotus Hill datase...

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

作者： Benjamin Yao Xiong Yang Tianfu Wu Department of Statistics University of California Los Angeles USA Lotus Hill Institute of Computer Vision and Information Science Ezhou China

Summary form only given: We present the LHI dataset, a large-scale ground truth image dataset, and a top-down/bottom-up scheme for scheduling the inference processes in stochastic image grammar (SIG). Development of stochastic image grammar needs ground truth image data for diverse training and evaluation purposes, which can only be collected through manual annotation of thousands of images for a variety of object categories. This is too time-consuming a task for each research lab to do independently and a centralized general purpose ground truth dataset is much needed. In response to this need, the Lotus Hill Institute (LHI), an independent non-profit research institute in China, is founded in the summer of 2005. It has a full time annotation team for parsing the image structures and a development team for the annotation tools and database construction. Each image or object is parsed, semi-automatically, into a parse graph where the relations are specified and objects are named using the WordNet standard. The Lotus Hill Institute has now over 500,000 images (or video frames) parsed, covering 280 object categories. In computing, we present a method for scheduling bottom-up and top-down processes in image parsing with and-or graph (AoG) for advancing performance and speeding up on-line computation. For each node in an AoG, two types of bottom-up computing processes and one kind of top-down computing process are identified.

关键词： Stochastic processes Processor scheduling Image segmentation computer vision Information science Statistics Large-scale systems Image databases Tree graphs Entropy

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An on-line cursive word recognition system

An on-line cursive word recognition system

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

作者： Seni Nasrabadi Srihari Center of Excellence for Document Analysis and Recognition State University of New York University at Buffalo Buffalo NY USA Department of Electrical and Computer Engineering State University of New York University at Buffalo Buffalo NY USA

This paper presents a system for large vocabulary recognition of on-line handwritten cursive words. The system first uses a filtering module, based on simple letter features, to quickly reduce a large reference dictionary to a smaller number of candidates; the reduced lexicon along with the original input is subsequently fed to a recognition module. In order to exploit the sequential nature of the temporal data, we employ a TDNN-style network architecture which has been successfully used in the speech recognition domain. Explicit segmentation of the input words into characters is avoided by using a sliding window concept where the input word representation (a set of frames) is presented to the neural network-based recognizer sequentially. The outputs of the recognition module are collected and converted into a string of characters that can be matched with the candidate words. A description of the complete system and its components is given.< >

关键词： Character recognition, hand-written Neural network applications

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Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

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

作者： Andrea Panzino Simone Maurizio La Cava Giulia Orrù Gian Luca Marcialis University of Cagliari Cagliari Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Due to the possibility of automatically verifying an individual’s identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： computer vision Face recognition conferences Employment Process control Detectors Market research

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Real-Time Detection and Understanding of Isolated Protruded Fingers

Real-Time Detection and Understanding of Isolated Protruded ...

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

作者： S. Chandran A. Sawa Computer Science & Engineering Department IIT Bombay & CfAR University of Maryland Mumbai India Computer Science & Engineering Department IIT Bombay Mumbai India

Making sense of hand gestures in applications such as novel user interfaces is an active area of research. Many current methods use complex 3D model of non-rigid hands and involved paradigms in solving the general problem. In this paper we look at a subproblem of sign language alphabet recognition where gestures are made with protruded fingers. In contrast to the more intricate schemes, we propose a simple, fast classification algorithm using two dimensional projection of Euler angles. Not only is our scheme novel, it lends itself to a real time implementation.

关键词： Fingers Handicapped aids computer vision computer science Application software User interfaces Video sequences pattern recognition Data gloves Cameras

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In between 3D Active Appearance Models and 3D Morphable Models

In between 3D Active Appearance Models and 3D Morphable Mode...

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

作者： Jingu Heo Marios Savvides Biometrics Laboratory Cy Laboratory Carnegie Mellon University Pittsburgh PA USA

In this paper we propose a novel method of generating 3D morphable models (3DMMs) from 2D images. We develop algorithms of 3D face reconstruction from a sparse set of points acquired from 2D images. In order to establish correspondence between images precisely, we combined active shape models (ASMs) and active appearance models (AAMs)(CASAAMs) in an intelligent way, showing improved performance on pixel-level accuracy and generalization to unseen faces. The CASAAMs are applied to the images of different views of the same person to extract facial shapes across pose. These 2D shapes are combined for reconstructing a sparse 3D model. The point density of the model is increased by the loop subdivision method, which generates new vertices by a weighted sum of the existing vertices. Then, the depth of the dense 3D model is modified with an average 3D depth-map in order to preserve facial structure more realistically. Finally, all 249 3D models with expression changes are combined to generate a 3DMM for a compact representation. The first session of the multi-PIE database, consisting of 249 persons with expression and illumination changes, is used for the modeling. Unlike typical 3DMMs, our model can generate 3D human faces more realistically and efficiently (2-3 seconds on P4 machine) under diverse illumination conditions.

关键词： Active appearance model Image reconstruction Lighting Humans Face detection Face recognition computer vision Cameras Active shape model Surface reconstruction

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Efficient point process inference for large-scale object detection

Efficient point process inference for large-scale object det...

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2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Pham, Trung T. Rezatofighi, Seyed Hamid Reid, Ian Chin, Tat-Jun School of Computer Science University of Adelaide Australia

ISBN: (纸本)9781467388511

We tackle the problem of large-scale object detection in images, where the number of objects can be arbitrarily large, and can exhibit significant overlap/occlusion. A successful approach to modelling the large-scale nature of this problem has been via point process density functions which jointly encode object qualities and spatial interactions. But the corresponding optimisation problem is typically difficult or intractable, and many of the best current methods rely on Monte Carlo Markov Chain (MCMC) simulation, which converges slowly in a large solution space. We propose an efficient point process inference for largescale object detection using discrete energy minimization. In particular, we approximate the solution space by a finite set of object proposals and cast the point process density function to a corresponding energy function of binary variables whose values indicate which object proposals are accepted. We resort to the local submodular approximation (LSA) based trust-region optimisation to find the optimal solution. Furthermore we analyse the error of LSA approximation, and show how to adjust the point process energy to dramatically speed up the convergence without harming the optimality. We demonstrate the superior efficiency and accuracy of our method using a variety of large-scale object detection applications such as crowd human detection, birds, cells counting/localization.

关键词： Object detection

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6D dynamic camera relocalization from single reference image

6D dynamic camera relocalization from single reference image

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2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Feng, Wei Tian, Fei-Peng Zhang, Qian Sun, Jizhou School of Computer Science and Technology Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China

ISBN: (纸本)9781467388511

Dynamic relocalization of 6D camera pose from single reference image is a costly and challenging task that requires delicate hand-eye calibration and precision positioning platform to do 3D mechanical rotation and translation. In this paper, we show that high-quality camera relocalization can be achieved in a much less expensive way. Based on inexpensive platform with unreliable absolute repositioning accuracy (ARA), we propose a hand-eye calibration free strategy to actively relocate camera into the same 6D pose that produces the input reference image, by sequentially correcting 3D relative rotation and translation. We theoretically prove that, by this strategy, both rotational and translational relative pose can be effectively reduced to zero, with bounded unknown hand-eye pose displacement. To conquer 3D rotation and translation ambiguity, this theoretical strategy is further revised to a practical relocalization algorithm with faster convergence rate and more reliability by jointly adjusting 3D relative rotation and translation. Extensive experiments validate the effectiveness and superior accuracy of the proposed approach on laboratory tests and challenging real-world applications.

关键词： Rotation

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Face2Face: Real-time face capture and reenactment of RGB videos

Face2Face: Real-time face capture and reenactment of RGB vid...

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2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Thies, Justus Zollhöfer, Michael Stamminger, Marc Theobalt, Christian Nießner, Matthias University of Erlangen-Nuremberg Germany Max-Planck-Institute for Informatics Germany Stanford University United States

ISBN: (纸本)9781467388511

We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.

关键词： Rendering (computer graphics)

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TVParser: An automatic TV video parsing method

TVParser: An automatic TV video parsing method

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作者： Liang, Chao Xu, Changsheng Cheng, Jian Lu, Hanqing National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China China-Singapore Institute of Digital Media Singapore 119615 Singapore

ISBN: (纸本)9781457703942

In this paper, we propose an automatic approach to simultaneously name faces and discover scenes in TV shows. We follow the multi-modal idea of utilizing script to assist video content understanding, but without using timestamp (provided by script-subtitles alignment) as the connection. Instead, the temporal relation between faces in the video and names in the script is investigated in our approach, and an global optimal video-script alignment is inferred according to the character correspondence. The contribution of this paper is two-fold: (1) we propose a generative model, named TVParser, to depict the temporal character correspondence between video and script, from which face-name relationship can be automatically learned as a model parameter, and meanwhile, video scene structure can be effectively inferred as a hidden state sequence;(2) we find fast algorithms to accelerate both model parameter learning and state inference, resulting in an efficient and global optimal alignment. We conduct extensive comparative experiments on popular TV series and report comparable and even superior performance over existing methods. © 2011 ieee.

关键词： Alignment

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Heterogeneous structure fusion for Target recognition in infrared imagery

Heterogeneous structure fusion for Target Recognition in inf...

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

作者： Guangfeng Lin Guoliang Fan Liangjiang Yu Xiaobing Kang Erhu Zhang Department of Information Science Xi'an University of Technology Xi'an Shaanxi China School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

ISBN: (纸本)9781467367608

We study Automatic Target recognition (ATR) in infrared (IR) imagery from the perspective of feature fusion. The key to feature fusion is to take advantage of the discriminative and complementary information from different feature sets, which can be represented as internal (within each feature set) or external structures (across different feature sets). Traditional approaches tend to preserve either internal or external structures via certain feature projection. Some early attempts consider both structures implicitly or indirectly without revealing their relative importance and relevance. We propose a new unsupervised heterogeneous structure fusion (HSF) algorithm that is able to jointly optimize two kinds of structures explicitly and directly via a unified feature projection. The objective function of HSF integrates two feature structures in a closed form which can be optimized alternately via linear programming and eigenvector methods. The HSF solution provides not only the optimal feature projection but also the weight coefficients that encode the relative importance between two kinds of structures and among multiple feature sets. The experimental results on the COMANCHE IR dataset demonstrate that HSF outperforms state-of-the-art methods.

关键词： Feature extraction Linear programming Correlation Target recognition Optimization Measurement Yttrium

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