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International conference on Computational Intelligence for Modelling, Control and Automation, and International conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： D. van der Weken v. De Witte M. Nachtegael S. Schulte E. Kerre Fuzziness & Uncertainty Modelling Ghent University Ghent Belgium

We constructed several new fuzzy similarity measures for greyscale images that outperform the classical measures of comparison, like root mean square error or peak signal to noise ratio, in the sense of image quality evaluation. In this paper we investigate the usefulness of similarity measures for the comparison of colour images. Instead of applying the similarity measures for greyscale images component-wise, we extend the similarity measures to colour images by applying vector morphological operators. We restrict ourselves to an investigation in the RGB colour space

关键词： Fuzzy sets Digital images Colored noise Noise measurement PSNR Root mean square image processing Color image quality Particle measurements

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Automatic Segmentation and Classification of Diffused Liver Diseases using Wavelet Based Texture Analysis and Neural Network

Automatic Segmentation and Classification of Diffused Liver ...

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IEEE India conference (INDICON)

作者： K. Mala v. Sadasivam CSE Mepco Schlenk Engineering College Sivakasi India CSE M.S.University Tirunelveli India

In this paper a computer aided diagnostic system for classifying diffused liver diseases from Computerized Tomography (CT) images using wavelet based texture analysis and neural network is presented. Liver is extracted from CT abdominal images using adaptive threshold and morphological processing. Orthogonal wavelet transform is applied on the liver to get horizontal, vertical and diagonal details. The statistical texture features like Mean, Standard deviation, Contrast, Entropy, Homogeneity and Angular second moment are extracted from these details and hence the eighteen features are used to train the Probabilistic neural network to classify the liver as fatty or cirrhosis. The proposed system is tested for 100 images. It produces an accuracy of 95%. The performance of the proposed system is also evaluated by calculating specificity, sensitivity, positive prediction value and negative prediction value. The performance measures of the above system are compared with the results evaluated by radiologists.

关键词： Liver diseases Wavelet analysis Neural networks Computed tomography image texture analysis image segmentation Computer networks image analysis Abdomen Wavelet transforms

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Stereo vision-based approaches for Pedestrian Detection

Stereo Vision-based approaches for Pedestrian Detection

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IEEE Computer Society conference on Computer vision and Pattern Recognition Workshops (CvPRW)

作者： M. Bertozzi E. Binelli A. Broggi M.D. Rose Dip. Ingegneria dell'Informazione Università di Parma Parma ITALY

This paper describes a system for pedestrian detection in stereo infrared images. The system is based on three different underlying approaches: warm area detection, edgebased detection, and v-disparity computation. Stereo is also used for computing the distance and size of detected objects. A final validation process is performed using head morphological and thermal characteristics. Neither temporal correlation, nor motion cues are used in this processing. The developed system has been implemented on an experimental vehicle equipped with two infrared camera and preliminarily tested in different situations.

关键词： image edge detection Infrared detectors Infrared imaging Object detection Temperature Magnetic heads vehicles Cameras Shape Humans

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Analysis of Neurite Outgrowth for a Laser Patterned Neuronal Culture

Analysis of Neurite Outgrowth for a Laser Patterned Neuronal...

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International IEEE/EMBS conference on Neural Engineering, CNE

作者： S.K. Guduru S.v. Narasimhan S.T. Birchfield B.Z. Gao Department of Electrical and Computer Engineering Clemson University Clemson SC USA

We describe a system for patterning neuronal cultures using a weakly focused laser beam. Using this technique, individual cells can be placed at predetermined positions with submicron accuracy, enabling detailed study of neurite outgrowths regulated by various cell contact interactions. images of the cells are analyzed automatically by an algorithm to detect the soma and neurite outgrowth. The soma detection is based upon gray-level morphological processing, while the neurite outgrowth tracing is based upon the work of Al-Kohafi et al., which has been modified to improve the computational performance. Results on images of embryonic chick forebrain neurons demonstrate the effectiveness of the technique

关键词： Pattern analysis Neurons Laser beams Spatial resolution Focusing Algorithm design and analysis Biomedical engineering image analysis Biology computing Cells (biology)

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A spatiotemporal approach to tornado prediction

A spatiotemporal approach to tornado prediction

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International Joint conference on Neural Networks (IJCNN)

作者： v. Lakshmanan I. Adrianto T. Smith G. Stumpf University of Oklahoma and National Servere Storms Laboratory USA Sch. of Industrial Engineering University of Oklahama USA University of Oklahoma and National Weather Service USA

Automated tornado detection or prediction techniques in the literature have all been based on analyzing signatures of tornadoes that appear in Doppler radar velocity data. Attributes of these signatures are derived from radar data, as well as the near-storm environment, and associated with observed tornadoes. This associated database has been used to train neural networks and support vector machines to automatically classify radar signatures. In this paper, we formulate the tornado prediction problem differently. Instead of devising a machine intelligence approach to classify detections, we formulate the problem as a spatiotemporal one: of estimating the probability of a tornado event at a particular spatial location within a given time window. In this paper, we also describe our initial approach to addressing this differently formulated problem. We use a least-squares methodology to estimate shear, morphological image processing to estimate gradients, fuzzy logic to generate compact measures of tornado possibility and a classification neural network to generate the final spatio-temporal probability field.

关键词： Spatiotemporal phenomena Tornadoes Doppler radar Radar detection Neural networks Databases Support vector machines Support vector machine classification Machine intelligence Event detection

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Automatic Detection of Subsurface Defects Using Infrared Thermography

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AIP conference Proceedings 2005年第1期760卷 1469-1476页

作者： G. M. Lonkar P. Klinkhachorn Udaya B. Halabe H. v. S. GangaRao 1Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV 26505‐6109 2Department of Civil and Environmental Engineering Constructed Facilities Center West Virginia University Morgantown WV 26505‐6103

The popularity of FRP bridge decks has increased in recent times because of their high strength to weight ratio, fatigue resistance etc. Defects due to degradation of the bridge deck malign their properties and adversely affect the structural integrity. These defects need to be detected and continuously monitored using field techniques such as infrared thermography. The process of manually analyzing the infrared images is tedious and ambiguous. Instead, using software algorithms on the infrared images of FRP decks can increase the defect detection speed and accuracy. This paper proposes a software automated defect detection technique to detect subsurface anomalies in fiber reinforced polymer (FRP) bridge decks. Thermal images of the FRP decks were captured using a radiometric infrared camera. Software algorithms using morphological image processing and fuzzy clustering techniques were developed to analyze the images for detecting the defects automatically. They were tested on infrared images of FRP bridge decks prepared in the laboratory. In the tests conducted, simulated subsurface defects of varying size, thickness and wearing surfaces were fabricated in the laboratory. The tests include a performance analysis of detecting delaminations and debonds, and the effect of distance on the detecting ability of the algorithm. The algorithms were also tested with FRP deck specimens under solar radiation, to test the response under a passive heat source. The study showed that Infrared Thermography can be effectively used to detect subsurface defects and the process can be automated with substantial accuracy.

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morphological classification of blood leucocytes by microscope images

Morphological classification of blood leucocytes by microsco...

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IEEE International conference on Computational Intelligence for Measurement Systems and Applications

作者： Piuri, v Scotti, F Univ Milan Dept Informat Technol I-26013 Crema Italy

ISBN: (纸本)0780383419

The classification and the count of white blood cells in microscopy images allows the in vivo assessment of a wide range of important hematic pathologies (i.e., from presence of infections to leukemia). Nowadays, the morphological cell classification is typically made by experienced operators. Such a procedure presents undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operator's capabilities and tiredness. Only few attempts of partial/full automated systems based on image-processing systems are present in literature and they are still at prototype stage. This paper presents a methodology to achieve an automated detection and classification of leucocytes by microscope color images. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it extracts morphological indexes and finally it classifies the leucocytes by a neural classifier in Basophil, Eosinophil, Lympocyte, Monocyte and Neutrophil.

关键词： automatic cell classification morphological analysis classifiers leucocytes identification image processing

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The Iterative Object symmetry Transform

The Iterative Object symmetry Transform

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2004 International conference on image processing, ICIP 2004

作者： Zavidovique, B. Di Gesu, v. IEF University of Paris XI 91405-Orsay France DMA University of Palermo 90123-Palermo Italy

ISBN: (纸本)0780385543

This paper introduces a new operator named the Iterated Object Transform that is computed by combining the Object Symmetry Transform with the morphological operator erosion. This new operator has been applied on both binary and gray levels images showing the ability to grasp the internal structure of a digital object. We present some experiments on real images in face analysis. ©2004 IEEE.

关键词： image analysis

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morphological filtering of spectrograms for automatic speech recognition

Morphological filtering of spectrograms for automatic speech...

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Proceedings of the Fourth IASTED International conference on visualization, Imaging, and image processing

作者： Liu, W.M. Bastante, v. J. Rivas Rodriguez, F. Romero Evans, N.W.D. Mason, J.S.D. School of Engineering University of Wales Swansea United Kingdom

ISBN: (纸本)0889864152

This paper examines the separation of speech signals from additive noise using a recently proposed signal, noise segmentation approach based on statistical properties of the spectrogram [1,2], Competitive ASR results were reported in [3] despite using only crude spectrogram shape information suggesting that the approach offers high reliability in identifying regions of different signal dominance and might be robust down to negative SNRs. This paper extends these early results in two directions. First extension investigates the contribution of spectrogram shapes plus magnitudes versus shapes alone, the same ASR experiments as in [3] are repeated but this time with magnitude information recovered in regions deemed to contain speech. Results show consistent improvement for all SNRs down to -5dB. Second extension relates to computational efficiency, a modified one-pass version of the originally iterative process is proposed by deducing empirically an optimal final stopping condition for each SNR. This is found to reduce computational time significantly (factors ranging from 7 to 18) whilst improving ASR accuracy.

关键词： Speech recognition

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The iterative object symmetry transform

The iterative object symmetry transform

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IEEE International conference on image processing

作者： B. Zavidovique v. Di Gesu Universite Paris Sud Orsay France DMA University of Palermo Palermo Italy

关键词： Machine vision Discrete transforms Face detection image analysis Humans Object detection Avatars Data mining Gaussian processes Fourier transforms

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