2011 ieee projects matlab


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2011 ieee projects matlab

  1. 1. MATLAB 2011<br /><ul><li>Face Recognition by Exploring Information Jointly in Space, Scale and OrientationInformation jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multi-orientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.Image ProcessingDetection of Architectural Distortion in Prior MammogramsWe present methods for the detection of sites of architectural distortion in prior mammograms of interval-cancer cases. We hypothesize that screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. The methods are based upon Gabor filters, phase portrait analysis, a novel method for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase portrait analysis, 4224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' measures, and Haralick's 14 features were computed. The areas under the receiver operating characteristic curves obtained using the features selected by stepwise logistic regression and the leave-one-ROI-out method are 0.76 with the Bayesian classifier, 0.75 with Fisher linear discriminant analysis, and 0.78 with a single-layer feed-forward neural network. Free-response receiver operating characteristics indicated sensitivities of 0.80 and 0.90 at 5.8 and 8.1 false positives per image, respectively, with the Bayesian classifier and the leave-one-image-out method.Image ProcessingEnhanced Assessment of the Wound-Healing Process by Accurate Multiview Tissue ClassificationWith the widespread use of digital cameras, freehand wound imaging has become common practice in clinical settings. There is however still a demand for a practical tool for accurate wound healing assessment, combining dimensional measurements and tissue classification in a single user-friendly system. We achieved the first part of this objective by computing a 3-D model for wound measurements using uncalibrated vision techniques. We focus here on tissue classification from color and texture region descriptors computed after unsupervised segmentation. Due to perspective distortions, uncontrolled lighting conditions and view points, wound assessments vary significantly between patient examinations. The main contribution of this paper is to overcome this drawback with a multiview strategy for tissue classification, relying on a 3-D model onto which tissue labels are mapped and classification results merged. The experimental classification tests demonstrate that enhanced repeatability and robustness are obtained and that metric assessment is achieved through real area and volume measurements and wound outline extraction. This innovative tool is intended for use not only in therapeutic follow-up in hospitals but also for telemedicine purposes and clinical research, where repeatability and accuracy of wound assessment are critical.Image ProcessingA New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based FeaturesThis paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.Image ProcessingGraph Run-Length Matrices for Histopathological Image SegmentationThe histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from “graph run-length matrices” lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.Image ProcessingX-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual WordsIn this study we present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology is based on local patch representation of the image content, using a “bag of visual words” approach. We explore the effects of various parameters on system performance, and show best results using dense sampling of simple features with spatial content, and a nonlinear kernel-based support vector machine (SVM) classifier. In a recent international competition the system was ranked first in discriminating orientation and body regions in X-ray images. In addition to organ-level discrimination, we show an application to pathology-level categorization of chest X-ray data, the most popular examination in radiology. The system discriminates between healthy and pathological cases, and is also shown to successfully identify specific pathologies in a set of chest radiographs taken from a routine hospital examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnosticsImage ProcessingStandard Deviation for Obtaining the Optimal Direction in the Removal of Impulse NoiseThis letter proposes a new technique of restoring images distorted by random-valued impulse noise. The detection process is based on finding the optimum direction, by calculating the standard deviation in different directions in the filtering window. The tested pixel is deemed original if it is similar to the pixels in the optimum direction. Extensive simulations prove that the proposed technique has superior performance, when compared to other existing methods, especially at high noise rates.Image ProcessingRemoval of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median FilterA modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).Image ProcessingIMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet DecompositionIn this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.Image ProcessingAutomatic Optic Disc Detection From Retinal Images by a Line OperatorUnder the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.Image ProcessingWavelet-Based Image Texture Classification Using Local Energy HistogramsIn this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback-Leibler divergences between the input and sample local energy histograms on all the wavelet subbands. It is demonstrated by various experiments that our proposed method obtains a satisfactory texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.Image ProcessingA Ringing-Artifact Reduction Method for Block-DCT-Based Image ResizingThis paper proposes a new ringing-artifact reduction method for image resizing in a block discrete cosine transform (DCT) domain. The proposed method reduces ringing artifacts without further blurring, whereas previous approaches must find a compromise between blurring and ringing artifacts. The proposed method consists of DCT-domain filtering and image-domain post-processing, which reduces ripples on smooth regions as well as overshoot near strong edges. By generating a mask map of the overshoot regions, we combine a ripple-reduced image and an overshoot-reduced image according to the mask map in the image domain to obtain a ringing-artifact reduced image. The experimental results show that the proposed method is computationally faster and produces visually finer images than previous ringing-artifact reduction approaches.Image ProcessingAutomatic Exact Histogram Specification forContrast Enhancement and Visual SystemBased Quantitative EvaluationHistogram equalization, which aims at informationmaximization, is widely used in different ways to perform contrastenhancement in images. In this paper, an automatic exacthistogram specification technique is proposed and used for globaland local contrast enhancement of images. The desired histogramis obtained by first subjecting the image histogram to a modificationprocess and then by maximizing a measure that represents increasein information and decrease in ambiguity. A new method ofmeasuring image contrast based upon local band-limited approachand center-surround retinal receptive field model is also devised inthis paper. This method works at multiple scales (frequency bands)and combines the contrast measures obtained at different scalesusing 􀀀􀀀-norm. In comparison to a few existing methods, the effectivenessof the proposed automatic exact histogram specificationtechnique in enhancing contrasts of images is demonstratedthrough qualitative analysis and the proposed image contrast measurebased quantitative analysis.Image ProcessingFast Sparse Image Reconstruction UsingAdaptive Nonlinear FilteringCompressed sensing is a new paradigm for signalrecovery and sampling. It states that a relatively small numberof linear measurements of a sparse signal can contain most ofits salient information and that the signal can be exactly reconstructedfrom these highly incomplete observations. The majorchallenge in practical applications of compressed sensing consistsin providing efficient, stable and fast recovery algorithms which,in a few seconds, evaluate a good approximation of a compressibleimage from highly incomplete and noisy samples. In this paper,we propose to approach the compressed sensing image recoveryproblem using adaptive nonlinear filtering strategies in an iterativeframework, and we prove the convergence of the resultingtwo-steps iterative scheme. The results of several numerical experimentsconfirm that the corresponding algorithm possesses therequired properties of efficiency, stability and low computationalcost and that its performance is competitive with those of the stateof the art algorithms.Image ProcessingBinary Tissue Classification on Wound Images WithNeural Networks and Bayesian ClassifiersA pressure ulcer is a clinical pathology of localizeddamage to the skin and underlying tissue caused by pressure,shear, or friction. Diagnosis, treatment, and care of pressureulcers are costly for health services. Accurate wound evaluationis a critical task for optimizing the efficacy of treatment andcare. Clinicians usually evaluate each pressure ulcer by visualinspection of the damaged tissues, which is an imprecise mannerof assessing the wound state. Current computer vision approachesdo not offer a global solution to this particular problem. In thispaper, a hybrid approach based on neural networks and Bayesianclassifiers is used in the design of a computational system forautomatic tissue identification in wound images. A mean shiftprocedure and a region-growing strategy are implemented foreffective region segmentation. Color and texture features areextracted from these segmented regions. A set of multilayerperceptrons is trained with inputs consisting of color and texturepatterns, and outputs consisting of categorical tissue classes whichare determined by clinical experts. This training procedure isdriven by a -fold cross-validation method. Finally, a Bayesiancommittee machine is formed by training a Bayesian classifierto combine the classifications of the neural networks. Specificheuristics based on the wound topology are designed to significantlyimprove the results of the classification. We obtain highefficiency rates from a binary cascade approach for tissue identification.Results are compared with other similar machine-learningapproaches, including multiclass Bayesian committee machineclassifiers and support vector machines. The different techniquesanalyzed in this paper show high global classification accuracyrates. Our binary cascade approach gives high global performancerates (average sensitivity 􀀀 __ __, specificity 􀀀 __ __, andaccuracy 􀀀 ____) and shows the highest average sensitivityscore (􀀀86.3%) when detecting necrotic tissue in the woundMedical ImagingRemoval of Artifacts from JPEG Compressed DocumentImagesWe present a segmentation-based post-processing method to remove compression artifacts from JPEG compresseddocument images. JPEG compressed images typically exhibit ringing and blocking artifacts, which can beobjectionable to the viewer above certain compression levels. The ringing is more dominant around textualregions while the blocking is more visible in natural images. Despite extensive research, reducing these artifactsin an effective manner still remains challenging. Document images are often segmented for various reasons. As aresult, the segmentation information in many instances is available without requiring additional computation. Wehave developed a low computational cost method to reduce ringing and blocking artifacts for segmented documentimages. The method assumes the textual parts and pictorial regions in the document have been separated fromeach other by an automatic segmentation technique. It performs simple image processing techniques to cleanout ringing and blocking artifacts from these regions.A Low-Cost VLSI Implementation for EfficientRemoval of Impulse NoiseImage and video signals might be corrupted by impulsenoise in the process of signal acquisition and transmission.In this paper, an efficient VLSI implementation for removing impulsenoise is presented. Our extensive experimental results showthat the proposed technique preserves the edge features and obtainsexcellent performances in terms of quantitative evaluationand visual quality. The design requires only low computationalcomplexity and two line memory buffers. Its hardware cost is quitelow. Compared with previous VLSI implementations, our designachieves better image quality with less hardware cost. Synthesisresults show that the proposed design yields a processing rate ofabout 167 M samples/second by using TSMC 0.18 m technology.EVALUATION OF RETINAL VESSEL SEGMENTATION METHODS FORMICROANEURYSMS DETECTIONMicroaneurysms (MAs) are the earliest sign of diabeticretinopathy and manifest as small reddish spots on the retina.Generally, algorithm design for MAs detection starts byseparating the vascular system from the background for aposterior analysis of candidate MAs presence. Followingthis approach, this paper assesses three different methodsfor vessel segmentation and how they affect posterior MAsdetection. The robustness in developing automatic screeningsystems for MAs detection is discussed and a methodologyto detect candidate MAs in retinal images is introduced. Thealgorithm combines different vessel segmentation methodswith region growing to evaluate which is the best to providecandidate MAs detectionSecret Communication UsingJPEG Double CompressionProtecting privacy for exchanging informationthrough the media has been a topic researched by many people.Up to now, cryptography has always had its ultimate role inprotecting the secrecy between the sender and the intended receiver.However, nowadays steganography techniques are usedincreasingly besides cryptography to add more protective layerto the hidden data. In this letter, we show that the quality factorin a JPEG image can be an embedding space, and we discuss theability of embedding a message to a JPEG image by managingJPEG quantization tables (QTs). In combination with some permutationalgorithms, this scheme can be used as a tool for secretcommunication. The proposed method can achieve satisfactorydecoded results with this straightforward JPEG double compressionstrategy.Signal Processing