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2011年无线通信与信号处理国际会议(WCSP 2011)

作者： Mengjie Yang Xiuchang Zhu Liang Li Guang Han Xiaofei Li Jiangsu Provincial Key Lab of Image Processing and ImageCommunication Nanjing Univ. of P&TNanjing Engineering Research Center of Wideband Wireless Communication and Display Technique Ministry of Ed Engineering Research Center of Wideband WirelessCommunication and Display Technique Ministry ofEduc

This paper introduces a novel moving vehicle color recognition method. By processing videos recorded by monocular camera on traffic, we get single color image of vehicles which are crossed the traffic in any direction. First, paper employed a H-S two dimension histogram method to detect the color of the vehicles, and distinguish red, yellow, green and blue four color vehicles. Due to deal with the vehicles that do not satisfy above 4 colors, Regional Color Judgment method is proposed. This method consists of the following five steps: Firstly, position the color detection region by identifying the travelling direction of the vehicle. Then pick out the candidate color extraction region used to vehicle recognition. At last extract the color of the candidate region and match the color with the templates we predefined, the color of the best fit template is the color of the vehicle.

关键词： vehicle color recognition region choice HSV space vehicle detection

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Robust Multimodal Biometric Identification System Using Finger-Knuckle-Print features

Robust Multimodal Biometric Identification System Using Fing...

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International Conference on Control, Engineering & Information Technology

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Univ Ouargla Fac. des nouvelles technologies de l'information et de la communication Lab. de Genie Electrique Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB School of Computing Engineering and Information Sciences Northumbria University

ISBN: (纸本)9781479982134

Automatic personal identification is becoming an increasingly important requirement in a variety of applications like access control, surveillance systems and physical buildings. In recent years, biometric systems are used in these fields, offering greater convenience and several advantages over traditional security. In this paper, we propose an efficient online personal identification system based on Finger-Knuckle-Print (FKP) images. In this study, the finger image is characterized by the two dimensional Bloc based Discrete Cosine Transform (2D-BDCT) coefficients. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same finger is transformed by the two dimensional Discrete Fourier Transform (2D-DFT). The response of this transformation is directly used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. The results on a medium-size database, 150 users, show good identification performance based on individual modalities as well as after fusing multiple finger types.

关键词： Biometrics Identification FKP 2D-BDCT 2D-DFT HMM Data fusion

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Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models

arXiv

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arXiv 2023年

作者： Sun, Wei Wen, Wen Min, Xiongkuo Lan, Long Zhai, Guangtao Ma, Kede The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Institute of Image Communication and Information Processing The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science The Shenzhen Research Institute City University of Hong Kong Kowloon Hong Kong

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users’ viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models. Code is available at https://***/sunwei925/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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A motion estimation processor for MPEG2 video real time encoding at wide search range

A motion estimation processor for MPEG2 video real time enco...

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Custom Integrated Circuits Conference (CICC)

作者： A. Ohtani Y. Matsumoto M. Gion H. Yoshida T. Araki A. Ubukata M. Serizawa K. Aoki A. Sota A. Nagata K. Aono Semiconductor Research Center Matsushita Elecrric Indusrrial Company Limited USA Information and Communication Technology Lab Matsushita Elecrric Indusrrial Company Limited USA Image Processing Research Laboratories Matsushita Research Institute Tokyo Inc. Japan Audio Video Information Technology Lab Matsushita Elecrric Indusrrial Company Limited USA

We have developed a motion estimation processor, named COMET (COmpact Motion Estimation processor for real-Time MPEG2 encoding) for real-time MPEG2 encoding. COMET contains a full pel resolution estimation processor made up of 256 processing elements, a half pel resolution estimation processor made up of 8 processing elements and an SDRAM interface. This chip searches motion vectors through a full search method in a horizontal and vertical range of -16 pel to +15.5 pel. COMET is implemented by using 0.5 /spl mu/m-CMOS technology and contains 1.5 million transistors on an area of 12.9 mm/spl times/11.1 mm. COMET has a computation power of 42GOPS at 80 MHz and performs real-time motion vector estimation at a speed of 54 MHz for 720 /spl times/480 pel. An MPEG2 real-time encoding system with a high quality image for MP@ML can be realized using COMET.

关键词： Motion estimation Encoding image coding communication industry Real time systems Transform coding Search methods Video compression Large scale integration Circuits

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Eye Movement Pattern Modeling and Visual Comfort Viewing S3D images

Eye Movement Pattern Modeling and Visual Comfort Viewing S3D...

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IEEE Visual communications and image processing (VCIP)

作者： Chi Zhang Jun Zhou Xiao Gu Shouchen Zhu Alan. C. Bovik Institute of Image Communication & Network Enginerring Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing & Transmissions Shanghai Jiao Tong University China Shanghai Yanan High School Shanghai China Department of Electrical and Computer Engineering The University of Texas at Austin USA

ISBN: (纸本)9781538644591;9781538644584

Stereoscopic-3D (S3D) displays are widely used but present problems related to experiences of visual discomfort for human vision. One aspect of this issue is the movement of the gaze point within different depth fields. Here we aim to analyze the relationship between eye movement patterns and visual comfort experienced when viewing S3D images. Rather than simply labeling eye movement data according to categories such as gaze, saccade and so on, we depoly nonparametric Bayesian method to analyze and cluster several eye movement patterns, and to relate them to visual comfort. The results are relevant to the prediction of visual comfort assessment in S3D images by automatic algorithms.

关键词： Visualization Hidden Markov models Bayes methods Brain modeling Graphical models Three-dimensional displays

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Robust human identity identification system by using hand biometric traits 26

Robust human identity identification system by using hand bi...

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2014 26th International Conference on Microelectronics, ICM 2014

作者： Meraoumia, Abdallah Chitroub, Salim Bouridane, Ahmed Univ Ouargla Fac. des Nouvelles Technologies de L'information et de la Communication Lab. de Génie Électrique Ouargla30 000 Algeria Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB P.O. box 32 El Alia Bab Ezzouar Algiers16111 Algeria Department of Computer Science and Digital Technologies Northumbria University Newcastle Pandon Building Newcastle-upon-TyneNE2 1XE United Kingdom

ISBN: (纸本)9781479981533

Automatic personal identification has become an important issue in several applications, such as physical buildings and information systems. Nowadays, biometric techniques are an important and effective solution for automatic personal identification. One of the most popular biometric systems is based on the hand due to its ease of use. Hand has several modalities to be extracted, among them, Finger-Knuckle-Print (FKP) and PaLMprint (PLM), has attracted an increasing amount of attention. This paper investigates these modalities for elaborating an efficient multimodal biometric identification system. For that, the texture information of FKP and PLM is characterized by the Local Binary Pattern (LBP). During the matching phase, an Euclidian distance score is employed to measure the similarity between templates. The proposed system is tested and evaluated using a database of 165 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate. © 2014 IEEE.

关键词： Biometrics

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Blind Quality Assessment for in-the-Wild images via Hierarchical Feature Fusion and Iterative Mixed Database Training

arXiv

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arXiv 2021年

作者： Sun, Wei Min, Xiongkuo Tu, Danyang Zhai, Guangtao Ma, Siwei The Institute of Image Communication and Information Processing Shanghai Jiao Tong University China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China

image quality assessment (IQA) is very important for both end-users and service providers since a high-quality image can significantly improve the user’s quality of experience (QoE) and also benefit lots of computer vision algorithms. Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this paper, we propose a novel BIQA model for in-the-wild images by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity. Considering that perceptual visual quality is affected by both low-level visual features (e.g. distortions) and high-level semantic information (e.g. content), we first propose a staircase structure to hierarchically integrate the features from intermediate layers into the final feature representation, which enables the model to make full use of visual information from low-level to high-level. Then an iterative mixed database training (IMDT) strategy is proposed to train the BIQA model on multiple databases simultaneously, so the model can benefit from the increase in both training samples and image content and distortion diversity and can learn a more general feature representation. Experimental results show that the proposed model outperforms other state-of-the-art BIQA models on six in-the-wild IQA databases by a large margin. Moreover, the proposed model shows an excellent performance in the cross-database evaluation experiments, which further demonstrates that the learned feature representation is robust to images with diverse distortions and content. The code is available at https://***/sunwei925/StairIQA. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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A reduced-reference quality assessment scheme for blurred images

A reduced-reference quality assessment scheme for blurred im...

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IEEE Visual communications and image processing (VCIP)

作者： Zongxi Han Guangtao Zhai Yutao Liu Ke Gu Xinfeng Zhang Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Department of Computer Science Harbin Institute of Technology Harbin China School of Computer Science and Engineering Nanyang Technological University Singapore Rapid-Rich Object Search (ROSE) Lab Nanyang Technological University Singapore

ISBN: (纸本)9781509053179

In this paper, we propose a reduced-reference scheme for evaluating the quality of blurred images under the theory of free-energy principle. Specifically, the free-energy principle indicates that the brain tries to account for the input image with an internal generative model and the discrepancy between the image and its model-explained version, which can be measured by free energy, is related to the image's perceptual quality. Accordingly, we define a visual distance between the blurred image and its original image in free energy to evaluate the quality of the blurred image. Therefore, the proposed quality scheme belongs to reduced-reference methods, which needs some information from the original image for quality assessment. Experimental results on public databases, LIVE, TID2013 and C-SIQ, demonstrate the proposed method works in high consistency with subjective assessment results and outperforms representative image quality assessment approaches.

关键词： Brain modeling image quality Mathematical model Quality assessment Dictionaries Energy measurement

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image Fusion Algorithm Based on Features Motivated Multi-Channel Pulse Coupled Neural Networks

Image Fusion Algorithm Based on Features Motivated Multi-Cha...

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International Conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Xiaobo Qu Jingwen Yan 1Dept. of Communication Engineering Xiamen University.Fujian P.R.China 361005 1Dept. of Communication Engineering Xiamen University.Fujian P.R.China 3610052Key Lab of Digital Signal and Image Processing ofGuangdong Province Shantou University.Guangdong P.R.China 515063.

Pulse coupled neural networks (PCNN) is a mammal visual cortex-inspired artificial neural networks. Owing to the coupling links in neurons, PCNN is successful to utilize the local information, thus it has been successfully employed in image fusion. However, in traditional PCNN for image fusion, value of per pixel is used to motivate per neuron. In this paper, image feature of per pixel, e.g. gradient and local energy, is used to motivate per neuron and generate firing maps. Each firing map is corresponding to one type feature. Furthermore, a new multi-channel PCNN is presented to combine these firing maps via a weighting function which measures the contribution of these features to the fused image quality. Finally, pixels with maximum firing times, when firing times of source images are compared, are selected as the pixels of the fused image. Experimental results demonstrate that the proposed algorithm outperforms Wavelet- based and Wavelet-PCNN-based fusion algorithms.

关键词： image fusion Neural networks Neurons Pixel Discrete wavelet transforms image processing Artificial neural networks Fusion power generation Humans Pulse modulation

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Decoding Sleep: Microphone-Based Snoring Analysis using Embedded Machine Learning for Obstructive Sleep Apnea Detection

Decoding Sleep: Microphone-Based Snoring Analysis using Embe...

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International Conference on Biosignals, images and Instrumentation (ICBSII)

作者： Delpha Jacob Priyanka Kokil Subramanian S Jayanthi Thiruvengadam Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Chennai Department of Respiratory Medicine SRM Medical College Hospital and Research Centre Kattankulathur Tamil Nadu India

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Snoring, a recurring habit often disregarded within the Indian community, can signal a grave underlying issue of Obstructive Sleep Apnea (OSA). OSA is a severe sleep disorder characterized by recurrent interruptions in breathing for more than 10 seconds during sleep, typically due to partial or complete airway obstructions. Neglecting OSA can lead to a range of significant health risks, including increased likelihood of occupational accidents, motor vehicle accidents, heightened susceptibility to severe depression, cardiac and cerebrovascular diseases, and reduced life expectancy. The main objective of the study is to detect snoring while at sleep and also to classify it as normal snoring and OSA snoring. Arduino nano 33 BLE sense is used to capture the snore signal, it houses a built-in MP34DT05 sensor. The sensor has a signal-to-noise ratio of 64dB and sensitivity of - 26dBFS ± 3dB. This captures the sound signal of the individual, it is further processed to extract the Mel-filter bank energy features, Mel Frequency Cepstral Coefficients and Spectrogram features. The features are further used to build a model and the same is trained using edge impulse to classify the signal. The dataset is divided into training, testing, and validation sets, with 80% of the data allocated to training, 20% to testing, and an additional 20% within the training data set aside for validation purposes. The results for the two class classification (snoring and non snoring) indicate that the spectrogram-based approach achieved an accuracy rate of 96.9%, while the other two methods yielded accuracy rates of 93.8%. The accuracy for three class classification (normal, snoring and OSA snoring) using the Embedded Machine Learning (EML) approach is 88%. The proposed study demonstrates enhanced accuracy in identifying OSA by snoring compared to previous research. This autonomous system can facilitate the detection of OSA through the analysis of snoring patterns, subsequently alerting the subjec

关键词： Training Performance evaluation Accuracy Embedded systems Training data Machine learning Feature extraction

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