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作者： Jung, Keechul Kim, Kwang In Kurata, Takeshi Kourogi, Masakastu Han, JungHyun Pattern Recognition and Image Processing Lab. Michigan State Univerisity United States Artificial Intelligence Lab. KAIST Korea Republic of National Institute of Advanced Industrial Science and Technology Japan School of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

We propose a text scanner, which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron. To provide enhanced resolution of the extracted text images, we perform the text detection process after generating a mosaic image in a fast and robust image registration method. © 2002 IEEE.

关键词： Text processing

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Compact codebook generation towards scale-invariance

Compact codebook generation towards scale-invariance

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Pacific Rim Symposium on Image and Video Technology

作者： Liu, Si Yan, Shuicheng Xu, Changsheng Lu, Hanqing National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore China-Singapore Institute of Digital Media Singapore 119613 Singapore

ISBN: (纸本)9780769542850

In this paper, we present a novel visual codebook learning approach towards compactness and scale-invariance for dense patch image encoding. Firstly, each image is described as a bag of orderless gridding local patches, each of which is expressed in three scales. Then a unified objective function is proposed to simultaneously enforce the codebook compactness and select the optimal scale for each local patch, and a convergency provable iterative procedure is utilized for optimization. A direct advantage of the new codebook is that each local patch is essentially described by its best scale, and thus shares certain characteristic of SIFT yet not constrained to any salient point detectors. The experiments on PASCAL 07 dataset validate the effectiveness and efficiency of our proposed method for image classification task. © 2010 IEEE.

关键词： Image classification

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A generic framework for event detection in various video domains

A generic framework for event detection in various video dom...

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18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Zhang, Tianzhu Xu, Changsheng Zhu, Guangyu Liu, Si 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 Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9781605589336

Event detection is essential for the extensively studied video analysis and understanding area. Although various approaches have been proposed for event detection, there is a lack of a generic event detection framework that can be applied to various video domains (e.g. sports, news, movies, surveillance). In this paper, we present a generic event detection approach based on semi-supervised learning and Internet vision. Concretely, a Graph-based Semi-Supervised Multiple Instance Learning (GSSMIL) algorithm is proposed to jointly explore small-scale expert lab.led videos and large-scale unlab.led videos to train the event models to detect video event boundaries. The expert lab.led videos are obtained from the analysis and alignment of well-structured video related text (e.g. movie scripts, web-casting text, close caption). The unlab.led data are obtained by querying related events from the video search engine (e.g. YouTube) in order to give more distributive information for event modeling. A critical issue of GSSMIL in constructing a graph is the weight assignment, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers. We perform the thorough experiments in three popular video domains: movies, sports and news. The results compared with the state-of-the-arts are promising and demonstrate our proposed approach is performance-effective. © 2010 ACM.

关键词： Sports

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Mining student learning behavior and self-assessment for adaptive learning management system

Communications in Computer and Information Science

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Communications in computer and Information Science 2013年 384卷 70-79页

作者： Moutafi, Konstantina Vergeti, Paraskevi Alexakos, Christos Dimitrakopoulos, Christos Giotopoulos, Konstantinos Antonopoulou, Hera Likothanassis, Spiros Pattern Recognition Lab. Dept. of Computer Engineering and Informatics University of Patras Greece Technological Educational Institute of Patras Greece

The specific contribution aims to provide a web-based adaptive Learning Management System (LMS), named EVMATHEIA, which integrates specific innovative fundamental aspects of Student Learning Style and Intelligent Self-Assessment Mechanisms. More specifically the proposed adaptive system encapsulates an integrated student model that facilitates the decision about the learning style of the student monitoring his/her behavior. Furthermore, the platform utilizes semantic modeling techniques for the representation of the knowledge, semantically annotated educational material and an intelligent mechanism for the self-assessment and recommendation process. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Semantics

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Efficient Image Super-Resolution Using Pixel Attention 16th

Efficient Image Super-Resolution Using Pixel Attention

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Workshops held at the 16th European Conference on computer vision, ECCV 2020

作者： Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030670696

This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel attention scheme. Pixel attention (PA) is similar as channel attention and spatial attention in formulation. The difference is that PA produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results. On the basis of PA, we propose two building blocks for the main branch and the reconstruction branch, respectively. The first one—SC-PA block has the same structure as the Self-Calibrated convolution but with our PA layer. This block is much more efficient than conventional residual/dense blocks, for its two-branch architecture and attention scheme. While the second one—U-PA block combines the nearest-neighbor upsampling, convolution and PA layers. It improves the final reconstruction quality with little parameter cost. Our final model—PAN could achieve similar performance as the lightweight networks—SRResNet and CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN). The effectiveness of each proposed component is also validated by ablation study. The code is availab.e at https://***/zhaohengyuan1/PAN. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Conditional Sequential Modulation for Efficient Global Image Retouching 16th

Conditional Sequential Modulation for Efficient Global Image...

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16th European Conference on computer vision, ECCV 2020

作者： He, Jingwen Liu, Yihao Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT - SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030586003

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of 1 × 1 convolution, CSRNet only contains less than 37 k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is availab.e at https://***/hejingwenhejingwen/CSRNet. © 2020, Springer Nature Switzerland AG.

关键词： Pixels

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MagiWrite: Towards touchless digit entry using 3D space around mobile devices

MagiWrite: Towards touchless digit entry using 3D space arou...

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12th International Conference on Human-computer Interaction with Mobile Devices and Services, Mobile HCI2010

作者： Ketabdar, Hamed Roshandel, Mehran Yüksel, Kamer Ali Quality and Usability Lab. Deutsche Telekom Laboratories TU Berlin Ernst-Reuter-Platz 7 10587 Berlin Germany Deutsche Telekom Laboratories Ernst-Reuter-Platz 7 10587 Berlin Germany Computer Vision and Pattern Analysis Lab. Sabanci University Istanbul Turkey

ISBN: (纸本)9781605588353

In this work, we present a new approach for text (mainly digit) entry based on digit shaped gestures created in 3D space around a mobile device. Some new mobile devices such as Apple iPhone 3GS and Google Android are equipped with magnetic (compass) sensor. The main idea is to influence the magnetic sensor using a magnet taken in hand. The user draws (writes) digits in the 3D space around the device using the magnet taken in hand. Movement of the magnet changes temporal pattern of magnetic field around the device which is sensed and registered by the magnetic (compass) sensor. The registered pattern is then compared against already recorded templates for different digits. Such a text (digit) entry approach can be especially useful for small mobile devices in which it is hard to operate small buttons or touch screen. Using our technique, the text entry space extends beyond physical boundaries of the device. A demonstrator for this approach is implemented on Apple iPhone 3GS platform. It demonstrates registering a few templates for different digits, and recognizing digits written in the space around the device.

关键词： Magnets

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Learning Discriminative Representation For Facial Expression recognition From Uncertainties

Learning Discriminative Representation For Facial Expression...

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IEEE International Conference on Image Processing

作者： Xingyu Fan Zhongying Deng Kai Wang Xiaojiang Peng Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recent progresses on Facial Expression recognition (FER) heavily rely on deep learning models trained with large scale datasets. However, large-scale facial expression datasets always suffer from annotation uncertainties caused by ambiguous expressions, low-quality facial images, and the subjectiveness of annotators, which limits FER performance. To address this challenge, this paper introduces novel Rayleigh and weighted-softmax loss from two aspects. First, we propose Rayleigh loss to extract discriminative representation, which aims at minimizing within-class distances and maximizing inter-class distances simultaneously. Moreover, Rayleigh loss has a Euclidean form which make it easily be optimized with SGD and be combined with other forms. Second, we introduce a weight to measure the uncertainty of a given sample, by considering its distance to class center. Extensive experiments on RAF-DB, FERPlus and AffectNet show the effectiveness of our method with SOTA performance.

关键词： Weight measurement Emotion recognition Uncertainty Face recognition Image processing Measurement uncertainty Task analysis

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CANDID: Robust change dynamics and deterministic update policy for dynamic background subtraction

arXiv

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arXiv 2018年

作者： Mandal, Murari Saxena, Prafulla Vipparthi, Santosh Kumar Murala, Subrahmanyam Department of Computer Science & Engineering MNIT Jaipur India Computer Vision and Pattern Recognition Lab Department of Electrical Engineering IIT Ropar India

Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. These parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. The performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. The qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Efficient adaptive face recognition systems based on capture conditions

Efficient adaptive face recognition systems based on capture...

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2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014

作者： Pagano, C. Granger, E. Sabourin, R. Rattani, A. Marcialis, G.-L. Roli, F. Lab. d'Imagerie De Vision et d'Intelligence Artificielle École de Technologie Supérieure Université du Québec MontrealQC Canada Pattern Recognition and Applications Group Dept. of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

ISBN: (纸本)9781479945344

In many face recognition (FR) applications, changing capture conditions lead to divergence between facial models stored during enrollment and faces captured during operations. Moreover, it is often costly or infeasible to capture several high quality reference samples a priori to design representative facial models. Although self-updating models using high-confidence face captures appear promising, they raise several challenges when capture conditions change. In particular, face models of individuals may be corrupted by misclassified input captures, and their growth may require pruning to bound system complexity over time. This paper presents a system for self-update of facial models that exploits changes in capture conditions to assure the relevance of templates and to limit the growth of template galleries. The set of reference templates (facial model) of an individual is only updated to include new faces that are captured under significantly different conditions. In a particular implementation of this system, illumination changes are detected in order to select face captures from bio-login to be stored in a gallery. Face captures from a built-in still or video camera are taken at periodic intervals to authenticate the user having accessed a secured computer or network. Experimental results produced with the DIEE dataset show that the proposed system provides a comparable level of performance to the FR system that self-updates the gallery on all high-confidence face captures, but with significantly lower complexity, i.e., number of templates per individual. © 2014 IEEE.

关键词： Face recognition

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