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Fuzzy rule based approach for face and facial feature extraction in biometric authentication

Fuzzy rule based approach for face and facial feature extrac...

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international conference on image and vision Computing New Zealand, IVCNZ

作者： Mozammel Chowdhury Junbin Gao Rafiqul Islam School of Computing & Mathematics Charles Sturt University Bathurst NSW Australia Discipline of Business Analytics Business School University of Sydney Sydney NSW Australia

ISBN: (纸本)9781509027491

Human biometrics are regarded as groundbreaking tools for preserving privacy and security in many computer vision applications such as, biometric authentication, secure access control, visa processing, and border checking. Biometric features such as, fingerprint, face, retina, iris patterns, voice waves, palm print and signatures are commonly used in biometric authentication. However, in recent years face and facial features have become as the key attributes in biometric authentication due to their uniqueness and robustness. This manuscript proposes a robust and efficient scheme for extracting facial biometric features from the face images using fuzzy rules. The proposed system estimates face and facial features by analyzing color components in the human face images. In this paper we have used a combined color space consisting of HSV and YCbCr models to extract the color components. The face skeleton is determined based on the largest connected skin color area. Facial features such as, eyes, nose, mouth are then localized from the face skeleton with the knowledge of the face geometry. The algorithm is characterized by its simplicity and inexpensive computational requirements. Experimental evaluation confirms that our proposed system provides better performance comparable to other methods.

关键词： Face Skin image color analysis Feature extraction Facial features Lighting

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NIGHT-TIME DEHAZING BY FUSION

NIGHT-TIME DEHAZING BY FUSION

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IEEE international conference on image processing

作者： Cosmin Ancuti Codruta O. Ancuti Christophe De Vleeschouwer Alan C. Bovik ICTEAM Universite Catholique de Louvain Computer Vision and Robotics Group University of Girona Department of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (纸本)9781467399623

We introduce an effective technique to enhance night-time hazy scenes. Our technique builds on multi-scale fusion approach that use several inputs derived from the original image. Inspired by the dark-channel [1] we estimate night-time haze computing the airlight component on image patch and not on the entire image. We do this since under night-time conditions, the lighting generally arises from multiple artificial sources, and is thus intrinsically non-uniform. Selecting the size of the patches is non-trivial, since small patches are desirable to achieve fine spatial adaptation to the atmospheric light, this might also induce poor light estimates and reduced chance of capturing hazy pixels. For this reason, we deploy multiple patch sizes, each generating one input to a multiscale fusion process. Moreover, to reduce the glowing effect and emphasize the finest details, we derive a third input. For each input, a set of weight maps are derived so as to assign higher weights to regions of high contrast, high saliency and small saturation. Finally the derived inputs and the normalized weight maps are blended in a multi-scale fashion using a Laplacian pyramid decomposition. The experimental results demonstrate the effectiveness of our approach compared with recent techniques both in terms of computational efficiency and quality of the outputs.

关键词： Night-time Hazy Dehazing Multi-scale fusion Dehazing Fusion reactions FUSION fusion (melting) Fusion, biological image patch original image multi-scale Computational efficiency

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Learning Camera Viewpoint Using CNN to Improve 3D Body Pose Estimation

Learning Camera Viewpoint Using CNN to Improve 3D Body Pose ...

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international conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Mona Fathollahi Ghezelghieh Rangachar Kasturi Sudeep Sarkar Department of Computer Science and Engineering University of South Florida Tampa

ISBN: (纸本)9781509054084

The objective of this work is to estimate 3D human pose from a single RGB image. Extracting image representations which incorporate both spatial relation of body parts and their relative depth plays an essential role in accurate3D pose reconstruction. In this paper, for the first time, we show that camera viewpoint in combination to 2D joint locations significantly improves 3D pose accuracy without the explicit use of perspective geometry mathematical models. To this end, we train a deep Convolutional Neural Net-work (CNN) to learn categorical camera viewpoint. To make the network robust against clothing and body shape of the subject in the image, we utilized 3D computer rendering to synthesize additional training images. We test our framework on the largest 3D pose estimation bench-mark, Human3.6m, and achieve up to 20% error reduction on standing-pose activities compared to the state-of-the-art approaches that do not use body part segmentation.

关键词： Three-dimensional displays Two dimensional displays Cameras Clothing Pose estimation Solid modeling Training

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Vessel segmentation in low contrast X-ray angiogram images

Vessel segmentation in low contrast X-ray angiogram images

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IEEE international conference on image processing

作者： B. Felfelian H. R. Fazlali N. Karimi S. M. R. Soroushmehr S. Samavi B. Nallamothu K. Najarían Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Emergency Medicine University of Michigan Ann Arbor U.S.A. Departruent of Internal Medicine University of Michigan Ann Arbor U.S.A. Deptartment of Computational Medicine and Bioinformatics University of Michigan Ann Arbor U.S.A.

Coronary artery disease is one of the major causes of death throughout the world. An effective method for diagnosing this disease is X-ray angiography. The images are usually of poor quality and low contrast. This is due to non-uniform illumination, appearance of other body organs and artifacts, low SNR values, etc. Accurate segmentation of arteries is a challenging and important task. In this paper we first extract coronary arteries region of interest (ROI) using Hessian filter. Then, we combine these results with the flux flow measurements for accurate identification of vessel pixels. Post processing is performed to eliminate falsely identified vessel pixels. Finally, we segment the coronary arteries by selecting the largest connected component. Qualitative and quantitative evaluations of our method show high effectiveness of the proposed method. In terms of capturing major vessels our method is successful in 96% of cases.

关键词： Decision support systems Biomedical imaging Indexes Q measurement computers Biology computer vision

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Machine vision processing System based on Embedded Linux

Machine Vision Processing System based on Embedded Linux

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international conference on Industrial Technology and Management Science(ITMS 2015)

作者： C.MA L.P.XU Z.D.WANG K.HE R.X.DU Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Yantai Vocational College Shenzhen Key Laboratory of Precision Engineering The Chinese University of Hong Kong

Machine vision is widely used in automatic equipments, but the vision system based on embedded Linux is seldom at present. The key points and difficulties are the image processing and its realtime on embedded system. This paper introduces the vision processing system based on ARM and Linux, mainly introduces the USB camera driver, image matching, camera calibration and object localization. Comparing the conventional method using PC with universal software, vision processing system based on ARM and Linux greatly reduces the system size and cost. Experimental results show that this system can accurately recognize and locate the target object. The efficiency can meet the requirement basically, and it still has room to improve. Combined with industrial robot, this vision system can be used to automatic carrying, assembly, etc.

关键词： machine vision template match camera calibration

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Automatic video surveillance for theft detection in ATM machines: An enhanced approach

Automatic video surveillance for theft detection in ATM mach...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Rupesh Mandal Nupur Choudhury Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim INDIA Department of Computer Science Engineering & IT Assam Don Bosco University Guwahati INDIA

ISBN: (纸本)9781467394178

This paper deals with the development of an application for automation of video surveillance in ATM machines and detect any type of potential criminal activities that might be arising with the system which would considerably decrease the inefficiency that are existing in the prevalent systems. An advanced digital image processing technique along with the combination of computer vision and unsupervised machine learning techniques would be utilized which would create phenomenal results in the detection of the activities and their categorization. The proposed system makes efficient utilization of vector graphics and lists out an effective algorithm which comprises of methodologies like background modelling, subtraction, identification of salient objects, tracking of those objects and finally ending up with the detection and identification of the necessary action for the prevention of such type of activities. The proposed system also indulges in semi-supervised learning techniques and uses matching techniques like pose clustering and consistency in order to train the system and develop it to be an automated system as a whole. Since the captured video is fragmented into smaller frames and then the vector graphics and image processing techniques are implemented, the entire mechanism takes place in real time decreasing the time complexity to a great extent making the system an efficient mechanism to prevent such anti-social activities.

关键词： Shape Feature extraction Online banking image edge detection Mathematical model image segmentation Video surveillance

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A Survey of Face Recognition Techniques and Comparative Study of Various Bi-Modal and Multi-Modal Techniques

A Survey of Face Recognition Techniques and Comparative Stud...

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IEEE international conference on Industrial and Information Systems

作者： Anand Handa Rashi Agarwal Narendra Kohli Department of CSE PSIT COE Kanpur (India) Department of IT UIET CSJMU Kanpur (India) Department of CSE HBTIU Kanpur (India)

Face Recognition is one of the most demanding problems in computer vision and image processing. Face recognition techniques can be divided into following categories: methods which are applicable on intensified images;images derived from video sequences;and images that require three-dimensional information and data. In this paper, we have compared the various Bi-Modal and Multi-Modal techniques. Speech recognition is an essential component for various applications such as building interfaces for natural human machines. Acoustic speech is used as the only input for various speech based automatic recognition system. Multimodal recognition is as an indigenous component of the next level speech-based systems. The main objective of this review paper is to compare the various components of bimodal recognition and it aims at some important ongoing research issues in the field of image and face recognition.

关键词： Hidden Markov models Speech recognition Face recognition Speech Face Classification algorithms Feature extraction Facial Recognition Face recognition techniques Sorting algorithm Speech recognition images Hidden Markov models Speech Feature extraction Face computer vision

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Edge detection of license plate using Sobel operator

Edge detection of license plate using Sobel operator

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international conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Sheetal Israni Swapnil Jain Department of E&Tc Engineering DMIETR Wardha

ISBN: (纸本)9781467399401

Edge detection is one of the important operations in image processing and computer vision. It is the process that is used to locate the boundaries of objects or textures depicted in an image. To know the positions of these boundaries is a critical task in the process of image enhancement, recognition, restoration and compression. The edges of image are considered to be the most important attributes of image that provide valuable information for human image perception. As the data of edge detection is very large, therefore the speed of image processing becomes a difficult problem. The sobel operator is used for edge detection. In the edge function, the Sobel method uses the derivative approximation to find edges of the image. So, it returns edges at those points where the gradient of the considered image is maximum. The horizontal and vertical gradient matrices are used for the Sobel method whose dimensions are 3×3 in the edge detection operations. It has been proven by the results we have obtained, that the edge detection mathematical method by simulation using MATLAB software is very good method for the analyzing the image. After reading the pixels of an image the algorithm is applied in VERILOG. The entire simulation of the above process is done VERILOG using “XILINX-14.1”. And to display input and output image MATLAB is used. This paper focuses on software used to detect edges of image employing mainly the MATLAB program for solving this problem.

关键词： image edge detection Detectors MATLAB Mathematical model Feature extraction

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TULIPP: Towards ubiquitous low-power image processing platforms

TULIPP: Towards ubiquitous low-power image processing platfo...

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international conference on Embedded computer Systems: Architectures, Modeling and Simulation (IC-SAMOS)

作者： Tobias Kalb Lester Kalms Diana Göhringer Carlota Pons Fabien Marty Ananya Muddukrishna Magnus Jahre Per Gunnar Kjeldsberg Boitumelo Ruf Tobias Schuchert Igor Tchouchenkov Carl Ehrenstrahle Magnus Peterson Flemming Christensen Antonio Paolillo Christian Lemer Ben Rodriguez Guillaume Bernard Francois Duhem Philippe Millet Ruhr-University Bochum Germany Efficient Innovation France Norwegian University of Science and Technology Norway Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Germany Synective Labs AB Sweden Sundance Multiprocessor Technology Ltd. UK HIPPEROS S.A. Belgium Thales TED-MIS Thales TRT France Thales TED-MIS Thales TRT France

Many industrial domains rely on vision-based applications which require to comply with severe performance and embedded requirements. Tulipp will develop a reference platform which consists of a hardware system, a tool chain and a real-time operating system. This platform defines implementation rules and interfaces to tackle power consumption issues while delivering high, energy efficient and guaranteed computing performance for image processing applications. Using this reference platform will enable designers to develop a complete solution at a reduced cost to meet the typical embedded systems requirements: Size, Weight and Power. Moreover, for less constrained systems which performance requirements cannot be fulfilled by one instance of the platform, the reference platform will also be scalable so that the resulting boards can be chained for higher processing power. The instance of the reference platform developed during the project will be use-case driven and split between the implementation of: a reference hardware architecture - a scalable low-power board; a low-power operating system and image processing libraries; a productivity-enhancing tool chain. It will lead to three proof-of-concept demonstrators across different application domains: real-time and low-power medical image processing product prototype of surgical X-ray system (mobile c-arm); embedded image processing systems within Unmanned Aerial Vehicles (UAVs); automotive real time embedded systems for driver assistance. Tulipp will set up an ecosystem and will closely work with standardization organizations to propose new standards derived from its reference platform to the industry.

关键词： image processing Hardware Operating systems Real-time systems Power demand computer architecture Field programmable gate arrays

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Approaches of artificial intelligence in biomedical image processing: A leading tool between computer vision & biological vision

Approaches of artificial intelligence in biomedical image pr...

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(Spring) international conference on Advances in Computing, Communication, & Automation (ICACCA)

作者： Shruti Shukla Ashish Lakhmani Ambuj Kumar Agarwal Department of Biomedical Engineering-B. Tech graduated (BME) Babu Banarsi Das-Northern India Engineering College Lucknow Uttar Pradesh Department of Computing Science Engineering-B. Tech Graduate (CSE) Babu Banarsi Das-National Institute Of Technology & Management Lucknow Uttar Pradesh College of Computing Science and Information Technology Teerthankar Mahaveer University Moradabad India

Artificial Intelligence or AI is a subfield of computer science, which can be defined as the intelligence exhibited by a machine or a software having a remarkable impact on the field of biology and medicine. Imaging, on the other hand has become an essential component of many fields in medicine, biomedical applications, biotechnology and laboratory research by which images are processed and analysed. Putting together AI and imaging, the tools and techniques of artificial intelligence are useful for solving many biomedical problems and using a computer based equipped hardware software application for understanding images, researchers and clinicians can enhance their ability to study, diagnose, monitor, understand and treat medical disorders. Therefore the main idea behind this research paper is to focus on understanding the artificial intelligence, its concepts and various models available for the segmentation(or classification) of medical images, its applications, advantages and disadvantages and results and more.

关键词： image segmentation Artificial intelligence Medical diagnostic imaging Artificial neural networks Computational modeling Feature extraction

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