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作者： Sim, Kelvin Yap, Ghim-Eng Phua, Clifton Biswas, Jit Wai, Aung Aung Phyo Toistikov, Andrei Huang, Weimin Yap, Philip Data Mining Department Institute for Infocomm Research Singapore Networking Protocols Department Institute for Infocomm Research Singapore Computer Vision and Image Understanding Department Institute for Infocomm Research Singapore Department of Geriatric Medicine Alexandra Hospital Singapore

ISBN: (纸本)9781424463749

Using ambient intelligence to assist people with dementia in carrying out their Activities of Daily Living (ADLs) independently in smart home environment is an important research area, due to the projected increasing number of people with dementia. We present herein, a system and algorithms for the automated recognition of ADLs;the ADLs are in terms of plans made up encoded sequences of micro-context information gathered by sensors in a smart home. Previously, the Erroneous Plan Recognition (EPR) system was developed to specifically handle the wide spectrum of micro contexts from multiple sensing modalities. The EPR system monitors the person with dementia and determines if he has executed a correct or erroneous ADL. However, due to the noisy readings of the sensing modalities, the EPR system has problems in accurately detecting the erroneous ADLs. We propose to improve the accuracy of the EPR system by two new key components. First, we model the smart home environment as a Markov decision process (MDP), with the EPR system built upon it. Simple referencing of this model allows us to filter erroneous readings of the sensing modalities. Second, we use the reinforcement learning concept of probability and reward to infer erroneous readings that are not filtered by the first key component. We conducted extensive experiments and showed that the accuracy of the new EPR system is 26.2 % higher than the previous system, and is therefore a better system for ambient assistive living applications. © 2010 IEEE.

关键词： Markov processes

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Factorization towards a classifier

Factorization towards a classifier

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Qiang Chen Shuicheng Yan Tian-Tsong Ng Department of Electrical and Computer Engineering National University of Singapore Singapore Computer Vision and Image Understanding Department Singapore

In practice, nonnegative data factorization is often performed for data dimensionality reduction prior to a classification task using a classifier which is effective in low dimensional space such as nearest neighbor classifier. In this work, we propose a novel formulation to learn a multi-class classifier directly through a supervised nonnegative data factorization. This new formulation has the following properties: 1) the nonnegative data matrix is approximated as the product of a nonnegative basis matrix and a coefficient matrix where the nonnegative bases distinctively capture the common characteristics of all classes apart from that specific to individual classes; 2) a regularization term is imposed on nonnegative data factorization so that each datum can be predominantly reconstructed by the common basis vectors and its corresponding class-specific basis vectors; and 3) the coefficient vector for each datum is assumed to be transformed from a mapped kernel space, and the l 2 norm of the class-specific coefficients reveals the relative confidence of classes, which then directly leads to a multi-class classifier. We also present an iterative optimization technique for our formulation and analytically show its convergence property. Extensive experiments on face recognition, head pose estimation, and handwritten digit recognition tasks clearly demonstrate the advantages of the proposed classifier over the conventional two-step approach of nonnegative data factorization followed by a classic classifier.

关键词： Testing Face recognition Nearest neighbor searches Kernel Principal component analysis Independent component analysis Embedded computing computer vision image reconstruction Convergence

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Optic disc region of interest localization in fundus image for Glaucoma detection in ARGALI

Optic disc region of interest localization in fundus image f...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhuo Zhang Beng Hai Lee Jiang Liu Damon Wing Kee Wong Ngan Meng Tan Joo Hwee Lim Fengshou Yin Weimin Huang Huiqi Li Tien Yin Wong Department of Computer Vision and Image Understanding Institute for Infocomm Research Singapore Department of Computer Vision and Image Understanding ASTAR Institute for Infocomm Research Singapore Singapore National Eye Centre Singapore Eye Research Institute Singapore

Glaucoma is the second leading cause of permanent blindness worldwide. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Correctly determining the optic disc region of interest (ROI) will produce a smaller initial image which takes much lesser time taken to process compared to the entire image. The earlier ROI localization in the ARGALI system used a grid based method. The new algorithm adds a preprocessing step before analyzing the image. This step significantly improves the performance of the ROI detection. A batch of 1564 retinal images from the Singapore Eye Research Centre was used to compare the performance of the two methods. From the results, the earlier and new algorithm detects the ROI correctly for 88% and 96% of the images respectively. The results indicate potential applicability of the method for automated and objective mass screening for early detection of glaucoma.

关键词： Retina Blindness Head Degenerative diseases image analysis Cameras computer vision Algorithm design and analysis Tomography Availability

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Binarization of historical document images using the local maximum and minimum 10

Binarization of historical document images using the local m...

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2010 IAPR Workshop on Document Analysis Systems, DAS 2010

作者： Su, Bolan Lu, Shijian Tan, Chew Lim Department of Computer Science School of Computing National University of Singapore Computing 1 13 Computing Drive Singapore 117417 Singapore Department of Computer Vision and Image Understanding Institute for Infocomm Research 1 Fusionopolis Way #21-01 Connexis Singapore 138632 Singapore

ISBN: (纸本)9781605587738

This paper presents a new document image binarization technique that segments the text from badly degraded historical document images. The proposed technique makes use of the image contrast that is defined by the local image maximum and minimum. Compared with the image gradient, the image contrast evaluated by the local maximum and minimum has a nice property that it is more tolerant to the uneven illumination and other types of document degradation such as smear. Given a historical document image, the proposed technique first constructs a contrast image and then detects the high contrast image pixels which usually lie around the text stroke boundary. The document text is then segmented by using local thresholds that are estimated from the detected high contrast pixels within a local neighborhood window. The proposed technique has been tested over the dataset that is used in the recent Document image Binarization Contest (DIBCO) 2009. Experiments show its superior performance. Copyright 2010 ACM.

关键词： Pixels

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Restoration of motion blurred document images 12

Restoration of motion blurred document images

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27th Annual ACM Symposium on Applied Computing, SAC 2012

作者： Su, Bolan Lu, Shijian Lim, Tan Chew Department of Computer Science School of Computing National University of Singapore Computing 1 13 Computing Drive Singapore 117417 Singapore Department of Computer Vision and Image Understanding Institute for Infocomm Research 1 Fusionopolis Way #21-01 Connexis Singapore 138632 Singapore

ISBN: (纸本)9781450308571

Motion blur often decreases the quality of document image and makes the text information within the document images unreachable by optical character recognition (OCR) or by a person. This paper presents a blur correction technique that aims to correct motion blur within document images. Given a blurred document image, an alpha channel map is first constructed based on specific image characteristics that are associated with text documents. Several blur parameters including blur direction and blur extent are then estimated from the constructed alpha channel map. Finally the blurred document image is restored by using Richardson-Lucy deconvolution technique based on the estimated blur parameters. Experiments on a number of document images with motion blur show that the proposed technique improves the document visual quality as well as the OCR performance significantly. © 2012 ACM.

关键词： Optical character recognition

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image processing in quantum computers

arXiv

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arXiv 2018年

作者： Dendukuri, Aditya Luu, Khoa Computer Vision and Image Understanding Lab Computer Science and Computer Engineering Department University of Arkansas FayettevilleAR72701 United States

Quantum image Processing (QIP) is an exciting new field showing a lot of promise as a powerful addition to the arsenal of image Processing techniques. Representing image pixel by pixel using classical information requires enormous amount of computational resources. Hence, exploring methods to represent images in a different paradigm of information is important. In this work, we study the representation of images in Quantum Information. The main motivation for this pursuit is the ability of storing N bits of classical information in only log2N quantum bits (qubits). The promising first step was the exponentially efficient implementation of the Fourier transform in quantum computers as compared to Fast Fourier Transform in classical computers. In addition, images encoded in quantum information could obey unique quantum properties like superposition or entanglement. Copyright © 2018, The Authors. All rights reserved.

关键词： Qubits

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Combination of Document image Binarization Techniques

Combination of Document Image Binarization Techniques

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International Conference on Document Analysis and Recognition

作者： Bolan Su Shijian Lu Chew Lim Tan Department of Computer Vision and Image Understanding Institute for Infocomm Research Singapore Department of Computer Science School of Computing National University of Singapore Singapore

Document image binarization has been studied for decades, and many practical binarization techniques have been proposed for different kinds of document images. However, many state-of-the-art methods are particularly suitable for the document images that suffer from certain specific type of image degradation or have certain specific type of image characteristics. In this paper, we propose a classification framework to combine different thresholding methods and produce better performance for document image binarization. Given the binarization results of some reported methods, the proposed framework divides the document image pixels into three sets, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then applied to iteratively classify those uncertain pixels into foreground and background, based on the pre-selected froeground and background sets. Extensive experiments over different datasets including the Document image Binarization Contest(DIBCO)2009 and Handwritten Document image Binarization Competition(H-DIBCO)2010 show that our proposed framework outperforms most state-of-the-art methods significantly.

关键词： Text analysis Equations Feature extraction Lighting Degradation PSNR

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vision-based techniques for efficient Wireless Capsule Endoscopy examination

Vision-based techniques for efficient Wireless Capsule Endos...

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Defense Science Research Conference and Expo (DSR)

作者： That Mon Htwe Chee Khun Poh Liyuan Li Jiang Liu Eng Hui Ong Khek Yu Ho Department of Computer Vision and Image Understanding Institute for Infocomm Research Singapore Department of Gastroenterology & Hepatology National University Hospital Singapore

ISBN: (纸本)9781424492763

Although there are several devices for screening the digestive tract, Wireless Capsule Endoscopy (WCE) is now the gold standard for a non-invasive viewing of small intestine. However, in each examination, more than 55,000 images are recorded. Reading so many images can be time consuming. Thus, image processing and vision recognition techniques are being created to help doctors to save the time for diagnosis. This paper presents a comprehensive overview of WCE devices and recent progresses in vision-based techniques on reducing the time needed for WCE video reading. The recently developed techniques can be classified into three categories: disease (bleeding) detection, image-level and video-level summarizations. Firstly, the features and classification strategies employed for automatic bleeding, disease or abnormal detection are presented. Second, this paper presents the image-level summarization based on motion estimation, C-mean clustering and epitome techniques. Third, we present and compare the various color-bar based global-level WCE video summarizations. The capabilities and advantages of different techniques for improving the efficiency of diagnosis are evaluated.

关键词： Hemorrhaging Endoscopes Wireless communication image color analysis Diseases Feature extraction Wireless sensor networks

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A learning framework for degraded document image binarization using Markov Random Field

A learning framework for degraded document image binarizatio...

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International Conference on Pattern Recognition

ISBN: (纸本)9781467322164

Document image binarization is an important preprocessing technique for document image analysis that segments the text from the document image backgrounds. Many techniques have been proposed and successfully applied in different applications, such as document image retrieval. However, these techniques may perform poorly on degraded document images. In this paper, we propose a learning framework that makes use of the Markov Random Field to improve the performance of the existing document image binarization methods for those degraded document images. Extensive experiments on the recent Document image Bina-rization Contest datasets demonstrate that significant improvements of the existing binarization methods when applying our proposed framework.

关键词： image edge detection Markov random fields Vectors Equations Mathematical model Text analysis

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Character extraction in web image for text recognition

Character extraction in web image for text recognition

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International Conference on Pattern Recognition

作者： Bolan Su Shijian Lu Trung Quy Phan Chew Lim Tan Department of Computer Vision and Image Understanding Institute for Infocomm Research Singapore Department of Computer Science School of Computing National University of Singapore Singapore

ISBN: (纸本)9781467322164

images with text are frequently used on Internet for different purposes. Automatic recognition of text from web images plays an important role on extraction and retrieval of web information. However, the web images are usually in low resolution with artifacts and special effects, which makes word recognition a challenge task even after the text has been localized. In this paper, we propose a robust text recognition technique to efficiently convert the web images into text format. The proposed technique first makes use of the L0 norm smoothing to increase the edge contrast of the input web images. The images are then binarized on each color channel. A connected component analysis is followed to identify the possible character components. Finally the character candidates are recognized by the OCR engine after skew correction. Extensive experiments have been conducted on the latest ICDAR 2011 robust reading competition dataset for born-digital text. The experimental results show the superior performance of our proposed technique.

关键词： Text recognition image recognition Smoothing methods Optical character recognition software Robustness image color analysis Testing

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