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A multi-view approach on modular PCA for illumination and pose invariant face recognition

A multi-view approach on modular PCA for illumination and po...

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Applied imagery Pattern Recognition Workshop (AIPR)

作者： P. Sankaran V. Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A modified approach on modular PCA for face recognition is presented in this paper. The proposed changes aim to improve the recognition rates for modular PCA for face images with large variation in light and facial expression. The eyes form one of the most invariant regions on the face. A sub-image from this region is considered. Weight vectors from this region are appended to the existing weight vector for modular PCA. The accuracies for the modified method, the original method and PCA method are evaluated under conditions of varying pose, illumination and expressions using standard face databases.

关键词： Principal component analysis Lighting Face recognition Vectors image recognition Eyes Facial features image databases Computational intelligence Machine vision

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Belief networks for cytogenetic image categorization

Belief networks for cytogenetic image categorization

引用

IEEE Convention of the electrical and ELectronic Engineers in Israel

作者： B. Lerner R. Malka Pattern Analysis and Machine Learning Laboratory Department of Electrical & Computer Engineering Ben-Gurion University of the Negev Beersheba Israel

The structure and parameters of a belief network are learned in order to categorize cytogenetic images enabling the detection of genetic syndromes. We compare a structure learned from the data to another obtained utilizing expert knowledge and to the naive Bayesian classifier. We also study feature quantization needed for parameter learning in comparison to density estimation. Both networks achieve comparable accuracy for the cytogenetic database with a slight advantage to that based on expert knowledge.

关键词： Marine animals Neural networks Probability distribution Pattern classification Pattern analysis Machine learning Laboratories computer networks Genetic engineering Bayesian methods

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Clustering-based framework for comparing fMRI analysis methods

Clustering-based framework for comparing fMRI analysis metho...

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IEEE International Symposium on Biomedical Imaging

作者： G.-A. Hossein-Zadeh A.-M. Golestani H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering method in the new space. As a result, different methods can be compared by means of a cluster validity measure. The feature space is computed using a non-parametric method (principal component analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach.

关键词： Clustering algorithms Algorithm design and analysis image analysis Clustering methods Magnetic analysis Testing Accuracy Reproducibility of results Radiology Magnetic resonance

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A new application of texture unit coding to mass classification for mammograms

A new application of texture unit coding to mass classificat...

引用

IEEE International Conference on image Processing

作者： Yuan Chen Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780385543

Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as texture unit coding (TUC) has shown promise in texture classification. This paper presents a new application of the TUC to mass classification in mammograms. The TUC generates a texture spectrum for a texture image that can be used to describe the characteristics of masses. It also develops an information divergence (ID)-based discrimination criterion to measure the discrepancy between two texture spectra, a concept yet to explore in texture analysis. The TUC along with ID are further applied to mass classification in mammograms where the minimammographic database provided by the Mammographic image analysis Society (MIAS) is used for experiments.

关键词： Pixel Breast cancer Application software image coding image texture analysis Spatial databases Helium image generation Histograms Shape

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New texture shape feature coding-based computer aided diagnostic methods for classification of masses on mammograms

New texture shape feature coding-based computer aided diagno...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Y. Chen C.-I. Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This work presents new texture shape feature coding (TSFC)-based computer aided diagnostic (CAD) classification methods for classification of masses on mammograms. It introduces a new concept of 1.5-order 3-neighbor 3/spl times/3 connectivity to extract texture shape features that can describe multiples of 22.5/spl deg/. In order to effectively utilize these shape features, two new methods of implementing TFSC are further proposed to convert these features to texture feature numbers (TFNs), TFNs in quaternary (TFNq) which expresses a TFN in quaternary expansion and TFNs in product (TFNx ) that represents a TFN in terms of a product. Both TFNq and TFNx can then produce texture shape histograms in the same way that a gray-level histogram is generated for an image. Such a texture shape histogram is further used to generate various shape features of masses on mammograms for classification. In order to demonstrate the promise of our TSFC-based CAD methods, the MiniMammographic database provided by the Mammographic image analysis Society (MIAS) is used for experiments.

关键词： Shape Feature extraction Histograms image coding Character recognition Remote sensing Signal processing image processing Laboratories computer science

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Multiresolution automatic segmentation of T1-weighted brain MR images

Multiresolution automatic segmentation of T1-weighted brain ...

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IEEE International Symposium on Biomedical Imaging

作者： M. Zeydabadi R.A. Zoroofi H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.

关键词： image resolution image segmentation Biomedical imaging Multiresolution analysis image analysis Magnetic analysis Algorithm design and analysis Brain modeling Magnetic liquids Magnetic resonance

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MRSI brain tumor characterization using wavelet and wavelet packets feature spaces and artificial neural networks

MRSI brain tumor characterization using wavelet and wavelet ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： A. Yazdan-Shahmorad H. Soltanian-Zadeh R.A. Zoroofi Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique for assessing biochemical fingerprint of tissue composition. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of MRSI. There are several technical reasons that make the brain easier than other organs to be examined with MRSI. This work presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types (malignant glioma, astrocytoma, and oligodendroglioma). After extracting features from MRSI data using wavelet and wavelet packets, we use artificial neural networks to determine the abnormal spectra and the type of abnormality. We evaluated the proposed methods using clinical and simulated MRSI data and biopsy results. The MRSI analysis results were correct 97% of the time when classifying the spectra of the clinical MRSI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determining tumor types using the clinical and simulated MRSI data, respectively.

关键词： Wavelet packets Neoplasms Artificial neural networks Biopsy Magnetic resonance Spectroscopy Magnetic resonance imaging Fingerprint recognition Surgery Cancer

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A uniform projection-based unsupervised classification for hyperspectral imagery

A uniform projection-based unsupervised classification for h...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jing Wang Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

ISBN: (纸本)0780387422

Orthogonal subspace projection (OSP) has received considerable interest in hyperspectral image classification. It performs well when the complete knowledge of image endmembers is available. In reality, obtaining such prior knowledge is very difficult, if not impossible. This work investigates a variant of OSP, called uniform projection (UP) and presents a UP-based unsupervised algorithm for hyperspectral image classification. It first implements a uniform target detector (UTD) to find a set of interfering signatures, I, then another uniform I-annihilated detector (UIAD) to find desired target signatures. The signatures in I are combined with the UIAD found desired signatures to form a target signatures matrix M which can be used for supervised image classification. In this case, OSP can be used for this purpose. In order to demonstrate the utility of the proposed unsupervised classification technique for hyperspectral imagery, a series of computer simulations and experiments are conducted for analysis.

关键词： Hyperspectral imaging Data mining Hyperspectral sensors image classification Detectors Histograms Remote sensing Signal processing image processing Laboratories

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The characterization of scanning noise and quantization on texture feature analysis

The characterization of scanning noise and quantization on t...

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IEEE Southwest Symposium on image analysis and Interpretation

作者： G. Martin M.S. Pattichis Image and video Processing and Communications Laboratory (ivPCL) Department of Electrical and Computes Engineering University of New Mexico Albuquerque NM USA Department of Computer Science University of Cyprus Cyprus

The study of the effects of scanning on texture features is of great interest to computer-based screening systems. A mathematical model is developed for understanding how the original image gets distorted due to the contrast variability and geometric distortion inherent in the scanning process. Both quantitative and qualitative results (for sixty common texture features) are given.

关键词： Quantization Testing Optical films Optical distortion Mathematical model Charge coupled devices Optical sensors Sensor arrays Quality control Biomedical optical imaging

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Discrimination and identification for subpixel targets in hyperspectral imagery

Discrimination and identification for subpixel targets in hy...

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IEEE International Conference on image Processing

作者： Chein-I Chang Weimin Liu Chein-Chi Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Civil and Environmental Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780385543

Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications, specifically, for mixed pixels and subpixel targets. This paper investigates the issue of discrimination and identification for subpixel targets and further develops sample spectral covariance/correlation matrix-based hyperspectral measures to account for spectral variability within subpixel targets. Two types of measures are of interest and studied, Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures. In order to substantiate the proposed measures, a real data-based comparative analysis is conducted and compared to two spectral similarity measures, spectral angle mapper (SAM) and spectral information divergence (SID) for performance evaluation. The experiments show that both Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures work very effectively and outperformed the SAM and the SID in discrimination and identification for subpixel targets.

关键词： Hyperspectral imaging Pollution measurement Hyperspectral sensors Covariance matrix Matched filters Performance analysis Performance evaluation Remote sensing Signal processing image processing

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