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Gabor Wavelet Based Modular PCA Approach for Expression and Illumination Invariant Face Recognition

Gabor Wavelet Based Modular PCA Approach for Expression and ...

引用

Applied imagery Pattern Recognition Workshop (AIPR)

作者： Neeharika Gudur Vijayan Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A Gabor wavelet based modular PCA approach for face recognition is proposed in this paper. The proposed technique improves the efficiency of face recognition, under varying illumination and expression conditions for face images when compared to traditional PCA techniques. In this algorithm the face images are divided into smaller sub-images called modules and a series of Gabor wavelets at different scales and orientations are applied on these localized modules for feature extraction. A modified PCA approach is then applied for dimensionality reduction. Due to the extraction of localized features using Gabor wavelets, the proposed algorithm is expected to give improved recognition rate when compared to other traditional techniques. The performance of the proposed technique is evaluated under conditions of varying illumination, expression and variation in pose up to a certain range using standard face databases.

关键词： Principal component analysis Lighting Face recognition image recognition Feature extraction Vectors Statistical analysis image databases Linear discriminant analysis Application software

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An Adaptive and Non Linear Technique for Enhancement of Extremely High Contrast images

An Adaptive and Non Linear Technique for Enhancement of Extr...

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Applied imagery Pattern Recognition Workshop (AIPR)

作者： Saibabu Arigela K. Vijayan Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. A novel non linear image enhancement algorithm for digital images captured under such extremely non-uniform lighting conditions is proposed in this paper. The new technique constitutes three processes viz. adaptive intensity enhancement, contrast enhancement and color restoration. Adaptive intensity enhancement uses a specifically designed nonlinear transfer function which is capable of reducing the intensity of bright regions and at the same time enhancing the intensity of dark regions. Contrast enhancement tunes the intensity of each pixels magnitude based on its surrounding pixels. Finally, a linear color restoration process based on the chromatic information of the input image frame is applied to convert the enhanced intensity image back to a color image.

关键词： Layout Dynamic range image coding Color image restoration image enhancement Displays Histograms Computational intelligence Machine vision

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Learning Bayesian Networks for Cytogenetic image Classification

Learning Bayesian Networks for Cytogenetic Image Classificat...

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International Conference on Pattern Recognition

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

We experimentally learn structures of Bayesian networks classifying signals enabling genetic abnormality diagnosis. Structures learned based on the naive Bayesian classifier, expert knowledge or using the K2 algorithm are compared. Inferiority of the K2-based classifier has motivated an investigation of the algorithm initial ordering, search procedure and metric. Replacing the K2 search with hill-climbing search improves accuracy as does the inclusion of hidden variables into the structure. However, it is proved experimentally that this inferiority of the K2-based classifier is mainly due to the K2 metric soliciting structures having enhanced representability but limited classification accuracy

关键词： Bayesian methods image classification Marine animals Neural networks Probability distribution Genetics Pattern classification Niobium compounds Pattern analysis Machine learning

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Improving the efficiency and effectiveness of railcar safety appliance inspection using machine vision technology

Improving the efficiency and effectiveness of railcar safety...

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ASME/IEEE Joint Conference on Rail

作者： R. Edwards C.P.L. Barkan J.M. Hart S. Todorovic N. Ahuja Department of Civil and Environmental Engineering Newmark Civil Engineering Laboratory University of Illinois Urbana-Champaign Urbana IL USA Computer Vision and Robotics Laboratory Beckman Institute for Advanced Science and Technology Urbana IL USA Department of Electrical and Computer Engineering Computer Vision and Robotics Laboratory Beckman Institute for Advanced Science and Technology Urbana IL USA

Before a train departs a yard, many aspects of the freight cars and locomotives undergo inspection, including their safety appliances. Safety appliances are handholds, ladders and other objects that serve as the interface between humans and railcars during transportation. Federal safety rules govern the design and condition of safety appliances. The current car inspection process is primarily visual making it laborious, redundant, and generally lacking of memory. There exists a potential to increase both the effectiveness and efficiency of safety appliance inspections by utilizing machine vision technology to enhance the railcar inspection process. Machine vision consists of capturing digital video and using algorithms capable of detecting and analyzing the particular objects or patterns of interest. computer algorithms can objectively inspect railcars without tiring or becoming distracted and can also focus on certain parts of the railcar not easily seen by an inspector on the ground. Thus far, algorithms have been developed that can detect deformed ladders, handholds, and brake wheels on open-top gondolas and hoppers. Next, visual learning will be employed to teach the algorithm the differences between Federal Railroad Administration (FRA) safety appliance defects and other types of deformation not requiring a car to be bad ordered. The final product will be a wayside inspection system capable of detecting safety appliance defects on passing railcars.

关键词： Railway safety Home appliances Inspection Machine vision Vehicle safety US department of Transportation Humans Rail transportation Object detection Pattern analysis

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analysis of P300 Classifiers in Brain computer Interface Speller

Analysis of P300 Classifiers in Brain Computer Interface Spe...

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

作者： H. Mirghasemi R. Fazel-Rezai M. B. Shamsollahi BDP laboratory Department of Electrical Engineering Sharif University of Technology Tehran Iran Brain Image and Signal Processing BISP Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (GSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any optimization similar to other methods. In addition, in this paper, it is shown that the efficiency of using Principal Component analysis (PCA) for feature reduction results in decreasing the time for the classification and increasing the accuracy

关键词： Brain computer interfaces Electroencephalography Support vector machines Support vector machine classification Principal component analysis Band pass filters Digital filters computer interfaces Cities and towns USA Councils

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Assessment of Preprocessing on Classifiers Used in the P300 Speller Paradigm

Assessment of Preprocessing on Classifiers Used in the P300 ...

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

作者： H. Mirghasemi M. B. Shamsollahi R. Fazel-Rezai BDP laboratory Department of Electrical Engineering Sharif University of Technology Tehran Iran BDP laboratory Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering Brain Image and Signal Processing Laboratory University of Manitoba Winnipeg MAN Canada

Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper. We used data set lib from the BCI competition 2003 for training and testing phase. Our accuracy varied between 80% and 96%. In our work, we demonstrated that the problems of choosing the classifier and preprocessing methods are not independent of each other. Two of our approaches could achieve the 96% accuracy i.e. 31 of 32 characters were predicted correctly. These two approaches have different classifier and different preprocessing method. It means that the performance of each classifier can be enhanced with a specific preprocessing method. In our approach, we used only three electrodes of 64 applied electrodes. Therefore it can noticeably reduce the time and cost of EEG measurement

关键词： Electroencephalography Band pass filters Filtering Electrodes Testing Noise reduction Feature extraction Principal component analysis Digital filters Cities and towns

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A Reinforcement Learning Framework for Medical image Segmentation

A Reinforcement Learning Framework for Medical Image Segment...

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International Joint Conference on Neural Networks (IJCNN)

作者： F. Sahba H.R. Tizhoosh M.M.A. Salama Pattern Analysis and Machine Intelligence Laboratory Department of System Design Engineering University of Waterloo Waterloo ONT Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

This paper introduces a new method to medical image segmentation using a reinforcement learning scheme. We use this novel idea as an effective way to optimally find the appropriate local thresholding and structuring element values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal determined objectively. The agent uses these objective reward/punishment to explore/exploit the solution space. The values obtained using this way can be used as valuable knowledge to fill a Q-matrix. The reinforcement learning agent can use this knowledge for similar ultrasound images as well. The results demonstrate high potential for applying reinforcement learning in the field of medical image segmentation.

关键词： Learning Biomedical imaging image segmentation Ultrasonic imaging Medical diagnostic imaging Pattern analysis Machine intelligence Laboratories System analysis and design Design engineering

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Rule-based decision-making framework for knowledge-based anatomical landmark localization (K-BALL)

Rule-based decision-making framework for knowledge-based ana...

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IEEE International Symposium on Biomedical Imaging

作者： M.-R. Siadat H. Soltanian-Zadeh K. Elisevich Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

K-BALL is a general method for localization of anatomical phenomena of the same origin with natural discrepancies distributed over a reference space, e.g., human brain anatomical structures. In this paper, we focus on information analysis step (2nd step) of K-BALL during which landmarks extracted in its first step are evaluated. We provide a framework in which rules are automatically generated based on estimated and derived models. We show that the rules based on the derived models can improve the overall success rate of K-BALL. Each rule evaluates the extracted points by producing an intermediate confidence factor (ICNF). A total confidence factor is calculated using ICNF's to facilitate the acceptance or rejection of a set of points as landmarks of interest. Using the rules merely based on the estimated models, simulation study produced an overall success rate of 91.8%. Using the rules based on both of the estimated and derived models, this rate increased to 92.5%

关键词： Decision making Hippocampus Information analysis Data mining Humans Radiology image analysis Radio control Process control Control systems

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Determination of the Grey Level Ranks for the Segmentation of Textured images

Determination of the Grey Level Ranks for the Segmentation o...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Z. Ameur A. Adane S. Ameur Faculty of Electrical Engineering and Computer Science Department of Electronics Laboratory of Random Phenomena Analysis and Modeling LAMPA Mouloud Mammeri University Tizi Ouzou Algeria Faculty of Electronics and Computer Science Department of Telecommunications Laboratory of Image Processing and Radiation University of Sciences and Technology of Algiers U. S. T. H. B. Bab-Ezzouar Algeria

In many cases, an image is made of various types of texture. Such diversity can be usefully exploited to get a suitable decomposition of the image into typical classes and then, identify its constitutive elements. Among the approaches used to segment textured images, those based on the statistical analysis of the neighborhood of each pixel seem to be most efficient. In this paper, this kind of method is implemented, which consists in coding the pixels surrounding each point of the image by taking into account the path traveling through them and their grey levels. The rank vectors namely the codes obtained for all the possible paths, are then classified using the K-means algorithm. Considering the 24- rank vectors, this method is tested on different images of the Brodatz album and compared with Laws filters and GLCM. It yields a satisfactory reproduction of the image contours and a classification ratio exceeding 98 %. The 24 rank based-method is also applied to meteorological images collected by Meteosat over Europe and North Africa during December 1994. It is found that these images can be segmented into eight typical classes assignable to the soils, the seas and the clouds observed in the regions under study.

关键词： image segmentation Pixel Statistical analysis image coding Testing Filters Meteorology Europe Africa Soil

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An Over-Complete Independent Component analysis (ICA) Approach to Magnetic Resonance image analysis

An Over-Complete Independent Component Analysis (ICA) Approa...

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

作者： Jing Wang C.-I. Chang Hsiang Ming Chen C.C.-C. Chen Jyh Wen Chai Yen-Chieh Ouyang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan

This paper presents a new application of independent component analysis (ICA) in magnetic resonance (MR) image analysis. One of most successful applications for ICA-based approaches in MR imaging is functional MRI (fMRI) which basically deals with one-dimensional temporal signals. The ICA approach proposed in this paper is rather different and considers a set of MR images acquired by different pulse sequences as a 3-dimensional image cube and performs image analysis rather than signal analysis. One major difference between the fMRI-based ICA approaches and our proposed ICA-based image analysis is that the ICA used in the former is undercomplete as opposed to the latter which uses over-complete ICA. Such a fundamental difference results in completely different applications

关键词： Independent component analysis Magnetic resonance image analysis image sequence analysis Magnetic resonance imaging Signal processing image processing Principal component analysis Pixel Remote sensing

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