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Boundary Refined Texture Segmentation Based on K-Views and Datagram Methods

Boundary Refined Texture Segmentation Based on K-Views and D...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Enmin Song Renchao Jin Chih-Cheng Hung Yu Luo Xiangyang Xu School of Computer Science and Technology Huazhong University of Science and Technology Wuhan Hubei China School of Computing and Software Engineering Southern Polytechnic State University Marietta GA USA

We propose a new texture segmentation algorithm to improve the segmentation of boundary areas in the image. In some applications such as medical image segmentation, an exact segmentation on the boundary areas is needed. But satisfactory segmentation results cannot be obtained on the boundary areas among different texture classes with some existing texture segmentation algorithms in our preliminary experiments. The proposed algorithm consists of three steps. The first step is to apply the K-view-datagram segmentation method to the image to obtain an initial segmentation; the second step is to find a boundary set which includes the pixels with high probabilities to be misclassified by the initial K-view-datagram segmentation; the third step is to apply a modified K-views template method with a small scanning window to the boundary set to refine the segmentation. The evaluation of the proposed algorithm was carried out with the benchmark images randomly taken from Brodatz Gallery and the ultrasonic prostate images provided by the hospitals. Initial experimental results show that the concept of boundary set defined in this paper can catch most of misclassified pixels of the output of the initial K-View-datagram segmentation. The new segmentation algorithm gives high segmentation accuracy and classifies the boundary areas better than the existing algorithms

关键词： image segmentation signal processing algorithms Biomedical imaging Pixel image texture Feature extraction computational intelligence signal processing Computer science Software engineering

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New Advances in Intelligent signal processing 2011

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丛书名： Studies in computational intelligence

2011年

作者： Antonio Ruano Annamária R. Várkonyi-Kóczy

ISBN: (数字)9783642117398

ISBN: (纸本)9783642117381

The current volume New Advances in Intelligent signal processing contains extended works based on a careful selection of papers presented originally at the jubilee sixth ieee International symposium on Intelligent signal processing (WISP2009), held in Budapest Hungary, August 26-28, 2009 - celebrating the 10 years anniversary of the WISP event series. The present book does not intent to be an overall survey on the fields of interest of the area, but tries to find topics which represent new, hot, and challenging problems. The book begins with papers investigating selected problems of Modeling, Identification, and Clustering such as fuzzy random variables, evolutionary multi-objective neural network models, a structural learning model of neural networks within a Boltzmann machine, a robust DNA-based clustering techniques, and the advances of combining multi-criteria analysis of signals and pattern recognition using machine learning principles. In the second part of the book image processing is treated. The carefully edited chapters deal with fuzzy relation based image enhancement, image contrast control technique based on the application of ukasiewicz algebra operators, low complexity situational models of image quality improvement, flexible representation of map images to quantum computers, and object recognition in images. The last chapter presents an image processing application for elderly care, performing real-time 3D tracking based on a new evaluative multi-modal algorithm.

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A Novel Method to Recognize Complex Dynamic Gesture by Combining HMM and FNN Models

A Novel Method to Recognize Complex Dynamic Gesture by Combi...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Xiying Wang Guozhong Dai Institute of Software Chinese Academy and Sciences Beijing China

Recognition of dynamic gesture is an important task for gesture-based human-computer interaction. A novel HMM-FNN model is proposed in this paper for the modeling and recognition of complex dynamic gesture. It combines temporal modeling capability of hidden Markov model, and ability of fuzzy neural network for fuzzy rule modeling and fuzzy inference. Complex dynamic gesture has two important properties: its motion can be decomposed and usually being defined in a fuzzy way. By HMM-FNN model, dynamic gesture is firstly decomposed into three independent parts: posture changing, 2D motion trajectory and movement in Z-axis direction, and each of part is modeled by a group of HMM models which represent all fuzzy classes it possibly belongs to. The likelihood probability of HMM model to observation sequence is considered as fuzzy membership for FNN model. In our method, high dimensional gesture feature is transformed into several low dimensional features, which leads to the reduction of model complexity. By means of fuzzy inference, it achieves a higher recognition rate than conventional HMM model. Besides, human's experience can be taken advantaged to build and optimize model structure. Experiments show that the proposed approach is an effective method for the modeling and recognition of complex dynamic gesture

关键词： Hidden Markov models Fuzzy neural networks Fingers Fuzzy set theory computational intelligence signal processing image recognition Character recognition computational efficiency Optimization methods

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image Indexing and Retrieval in Compressed Domain Using Color Clusters

Image Indexing and Retrieval in Compressed Domain Using Colo...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Meekal Bajaj Jose A. lay Electronics and Communication Engineering Department Netaji Subhas Institute of Technology New Delhi India School of Electrical and Information Engineering University of Sydney NSW Australia

A compressed domain color-based image indexing method that avoids overheads associated with full decompression and color space transformation by operating in the YCbCr space has been presented. The proposed method performs a hard segmentation of the color space into predefined clusters based on perceptual similarity. A three dimensional color descriptor is generated by extracting the average intensity value of the Y, Cb and Cr components for each 8times8 DCT block. Each blocks descriptor is subsequently allocated to its corresponding cluster, whose centroid is updated using a weighted mean. The similarity measure is evaluated using a compact four dimensional feature vector from each cluster, which consists of the quantized centroid coordinates and the percentage contribution to the image composition by the cluster. Retrieval is performed by comparing the feature attributes of the clusters having significant membership in the query image, to those with the database images; the obtained matches are then integrated to evaluate the final ranking. The experimental results of the proposed model on a database of 7380 images are reported

关键词： Indexing image retrieval image coding image databases Spatial databases image segmentation Chromium Discrete cosine transforms Coordinate measuring machines Information retrieval

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Robust Object Extraction and Change Detection in Retinal images for Diabetic Clinical Studies

Robust Object Extraction and Change Detection in Retinal Ima...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Qin Li Jane You Lei Zhang Prabir Bhattacharya Department of Computing Hong Kong Polytechnic University Hong Kong China Institute for Information Systems Engineering Concordia University QUE Canada

With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis system can help eye specialists to screen larger populations and produce better evaluation of treatment and more effective clinical study. This paper is focused on the immediate needs for clinical studies on diabetic patients. Our system includes multiple feature extraction, robust retinal vessel segmentation, hierarchical change detection and classification. The output throughout this system will assist doctors to speed up screening large populations for abnormal cases, and facilitate evaluation of treatment for clinical study

关键词： Robustness Object detection Retina Diabetes Medical diagnosis Medical services Pathology Hypertension Arteriosclerosis image analysis

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Collective network of evolutionary binary classifiers for content-based image retrieval

Collective network of evolutionary binary classifiers for co...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011

作者： Kiranyaz, Serkan Uhlmann, Stefan Pulkkinen, Jenni Gabbouj, Moncef Ince, Turker Dept. of Signal Processing Tampere University of Technology Tampere Finland Faculty of Computer Science Izmir University of Economics Izmir Turkey

ISBN: (纸本)9781424499793

The content-based image retrieval (CBIR) has been an active research field for which several feature extraction, classification and retrieval techniques have been proposed up to date. However, when the database size grows larger, it is a common fact that the overall retrieval performance significantly deteriorates. In this paper, we propose collective network of (evolutionary) binary classifiers (CNBC) framework to achieve a high retrieval performance even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be trained incrementally. The CNBC framework basically adopts a "Divide and Conquer" type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. In such an evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale re-training or re-configuration. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its scalability;and a significant performance improvement is achieved over traditional retrieval techniques. © 2011 ieee.

关键词： Benchmarking

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X-ray image segmentation using active contour model with global constraints

X-ray image segmentation using active contour model with glo...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Y Jiang J Meng P Babyn Department of Computer Science Ryerson University Toronto ONT Canada Hospital for Sick Children Toronto ONT Canada

This paper presents an image segmentation method that outlines fractured bones in an X-ray image of a patient's arm within cast materials, and displays the alignment between the fractured bones. The cast material overlaying on the fractured bones creates extra noises to the X-ray image and provides challenges to the segmentation method. Our segmentation method aims on outlining the objects from a low contrast and high noise ratio of the X-ray images. A geodesic active contour model with global constraints is applied to this segmentation task. A prior shape is collected and embedded into the active contour model as a global constraint. A maximum-likelihood function is derived and used as a feedback system for each evolving process to a decision making procedure. Mutual Information is employed to measure the difference or the likelihood between the prior shape and the evolving curve. Experimental results show that the method produces the outlines of the fractured bones on the low contrast X-ray images robustly and accurately. The computation of our segmentation method is fast and efficient

关键词： X-ray imaging image segmentation Active contours Bones Noise shaping Shape measurement Displays signal to noise ratio Active shape model Feedback

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An Extendable Multi-Purpose 3D Neuromorphic Fabric using Nanoscale Memristors

An Extendable Multi-Purpose 3D Neuromorphic Fabric using Nan...

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ieee symposium on computational intelligence for Security and Defense Applications (CISDA)

作者： Manem, Harika Beckmann, Karsten Xu, Min Carroll, Robert Geer, Robert Cady, Nathaniel C. SUNY Polytech Inst Coll Nanoscale Sci & Engn Albany NY USA

ISBN: (纸本)9781467375573

Neuromorphic computing offers an attractive means for processing and learning complex real-world data. With the emergence of the memristor, the physical realization of cost-effective artificial neural networks is becoming viable, due to reduced area and increased performance metrics than strictly CMOS implementations. In the work presented here, memristors are utilized as synapses in the realization of a multi-purpose heterogeneous 3D neuromorphic fabric. This paper details our in-house memristor and 3D technologies in the design of a fabric that can perform real-world signal processing (i.e., image/video etc.) as well as everyday Boolean logic applications. The applicability of this fabric is therefore diverse with applications ranging from general-purpose and high performance logic computing to power-conservative image detection for mobile and defense applications. The proposed system is an area-effective heterogeneous 3D integration of memristive neural networks, that consumes significantly less power and allows for high speeds (3D ultra-high bandwidth connectivity) in comparison to a purely CMOS 2D implementation. images and results provided will illustrate our state of the art 3D and memristor technology capabilities for the realization of the proposed 3D memristive neural fabric. Simulation results also show the results for mapping Boolean logic functions and images onto perceptron based neural networks. Results demonstrate the proof of concept of this system, which is the first step in the physical realization of the multi-purpose heterogeneous 3D memristive neuromorphic fabric.

关键词： 3D integrated circuits memristor Neuromorphics neural networks image processing nanoelectronics

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An Automatic Wavelet-Based Approach for Lung Segmentation and Density Analysis in Dynamic CT

An Automatic Wavelet-Based Approach for Lung Segmentation an...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Omid Talakoub Emma Helm Javad Alirezaie Paul Babyn Brian Kavanagh Francesco Grasso Doreen Engelberts Department of Electrical and Computer Engineering Ryerson University Canada Department of Medical Imaging University of Toronto Canada Department of Medical Imaging Hospital for Sick Children Toronto Canada Department of Systems Design Engineering University of Waterloo Canada Department of Critical Care Medicine Hospital for Sick Children Toronto Canada

Acute respiratory distress syndrome (ARDS) can occur in people with or without previous lung disease. Analysis of aeration in artificial ventilation for ARDS is one of the major applications of computed tomography (CT) lung density examination. A movie of an affected rabbit lung over the respiratory cycle was produced by dynamic CT with a cine loop technique. This technique can produce thousands of CT images for analysis with a single experiment. A fully automated algorithm based on the capability of wavelet transformation to detect edges in the image is proposed. This method accurately and consistently segments the lung in pulmonary CT images. The speed and accuracy of this technique allows it to outperform other methods when dealing with the large number of images created by dynamic computed tomography

关键词： Wavelet analysis Lungs Computed tomography image edge detection Diseases Ventilation Motion pictures Rabbits image analysis image segmentation

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An Efficient RLS Algorithm For Output-Error Adaptive IIR Filtering And Its Application To Acoustic Echo Cancellation

An Efficient RLS Algorithm For Output-Error Adaptive IIR Fil...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Jafar Ramadhan Mohammed Gurnam Singh Electronics Engineering College Communication Engineering University of Mosul Mosul Iraq Electrical Engineering Department Punjab Engineering College Chandigarh India

In this letter, an efficient recursive least squares (RLS) algorithm using infinite impulse response (IIR) filter for acoustic echo cancellation (AEC) is proposed. The RLS adaptive filter is naturally extended from the finite impulse response (FIR) structure to the IIR structure. One of the main advantages of an IIR RLS filter is that a long-delay echo can be synthesized by a relatively small number of filter coefficients leading to lesser computational complexity. In addition it is more suitable for modeling physical systems, due to its pole-zero structure, over their FIR counterparts. To investigate the tracking performance and the stability of the proposed IIR RLS filter in a practical implementation, real data for far end speech signal and its echo were used in car environment. The proposed algorithm is shown to have global convergence. To demonstrate the effectiveness of the proposed IIR RLS filter, it is compared to the usual FIR RLS filter. The good performance of the proposed IIR RLS algorithm for AEC have been verified via computer simulations

关键词： Resonance light scattering Filtering algorithms Adaptive filters IIR filters Acoustic applications Echo cancellers Finite impulse response filter Least squares methods computational complexity Stability

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