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Content-based image retrieval (CBIR) scheme has gained popularity in the field of information retrieval for retrieving some relevant images from the image database based on the visual descriptors such as color, texture and/or shape of a given query image. In this paper, color features have been exploited from each color component of an RGB color image by using multiresolution approach since most of...
Medical data contain very valuable information which can save many lives if it is analyzed and utilized efficiently. Efficient analysis of this large volume of data demands the right choice of predictors and this in turn can impact the accuracy of the decision support system. Dimensionality reduction and feature subset selection are two techniques to reduce the number of features used in classification...
In the present work, a novel technique for detection and classification of transient phenomenon occurring in power systems is proposed based on high order statistical moments using empirical mode decomposition (EMD). Occurrence of transient disturbances in power systems are very common and require advanced detection techniques for their proper monitoring. In this study, transient electrical disturbances...
Grasping objects is one of the most important hand utilisation in everyday life. Due to neuromuscular ailments or injury, some people are unable to move their hands. Though myoelectrically controlled prostheses are widely available in the market, they require some muscle based control points which are hardly available for many. Motor Imagination (MI) controlled prostheses will surpass this shortcomings...
Object detection and recognition are crucial elements of any high level image analysis system. Convolutional Neural Networks (CNNs) or ConvNets have been applied for recognizing the category of the principal entity in an image for several years. One major benefit of convolutional networks is the use of shared weights in the intermediate convolutional layers, which reduces the required memory size...
Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and Machine Learning on clinically evaluated histopathological...
A novel multi-criteria optimization framework for matching of partially visible shapes in multiple images using joint geometric graph embedding is proposed. The proposed framework achieves matching of partial shapes in images that exhibit extreme variations in scale, orientation, viewpoint and illumination and also instances of occlusion; conditions which render impractical the use of global contour-based...
Many researches are going on in the field of optical character recognition (OCR) for the last few decades and a lot of articles have been published. Also a large number of OCR is available commercially. In this literature a review of the OCR history and the various techniques used for OCR development in the chronological order is being done.
The new framework proposed in this paper provides an insight into the problem of face authentication (verification) in unconstrained environment. This unconventional method extracts and represents the microstructures and local features of a given face image by greedy approach and sparse code respectively. This gives a stable and discriminative local descriptor for each patch that hinge on the local...
Computer Aided Diagnosis (CAD)systems provides computerized aid to medical practitioners that serves as a second opinion in the detection and diagnosis of diseases. Medical imaging modalities are the most effective non-invasive technique used for the classification of liver diseases. Imaging of abdominal organs for diagnosis of liver disease is usually carried out by Computed tomography. The computer...
Computer Aided Diagnosis (CAD) is one of the trusted methods in the field of medicine. CAD system assists the doctors for the diagnosis of diseases in higher degree of perfection within a short period of time. Now CAD is the most preferable method for the initial diagnosis of cancer using X-ray, CT, mammogram or MRI images. CAD works as an intermediate in between the radiologist and the input images...
This paper reviews the earlier period and current technologies for skin cancer detections. Malignant melanoma is one of the most common and the deadliest type of skin cancer. Skin cancer is commonly known as Melanoma. Skin Cancers are of two types- Benign and Malignant Melanoma. Melanoma can be cured completely if it is detected early. Both benign and malignant melanoma resembles similar in appearance...
Diabetic retinopathy is an open topic on which research has been ongoing for the past few decades. The damage caused by diabetic retinopathy can be prevented by the early detection of microaneurysm in the retina. This review focuses on microaneurysm detection, which is the commonly observed complication in diabetic patients. Some of algorithms used for microaneurysm detection from retinal fundus images...
Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. MRI (Magnetic Resonance Imaging) is a medical technique, mainly used by the radiologist for visualization of internal structure of the human body without any surgery. MRI provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumour....
In this paper, comparison between three classifiers for lung cancer diagnosis is proposed. Morphological Operations is used for preprocessing of the images and gray level cooccurrence matrix is used for the feature extraction process and SVM, Minimum distance and k-nearest neighbor classifiers are used for classification. Experimental analysis is made with data set to evaluate the performance of the...
This paper represents unsupervised method of color based segmentation using clustering to classify vegetated and urban area in Satellite images. Now a day due to the progresses in spatial resolution of satellite imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more important. In this work, one method proposed a segmentation of various...
An innovative approach based on local components called Optimal Random Image Component Selection is presented in this paper. Here, features are extracted from the Optimal Random Image Components by Gabor wavelets using greedy approach is proposed. These feature vectors are then down-sampled to some size which is then classified based on minimum distance measure. The design of Gabor filters for facial...
Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year. Computer-aided liver analysis can help in reducing the risk of liver surgery and design treatment strategies. This paper develops a novel Computer-Aided Diagnosis (CAD) system using Contourlet Transform based feature extraction for automatic diagnosis of tumors in the liver. Evaluation of the CAD...
Fast and accurate image classification is becoming one of the key requirements in content-based image retrieval (CBIR) system. The main idea of CBIR is to search similar image's based on user query. This paper proposes an improvised texture retrieval system using intuitionistic fuzzy set (IFS) theory. Tamura feature extraction technique is used to extract texture features of each image in the database...
We present a novel method to detect when a person is speaking using respiratory measurements collected in the natural environment. A speaker's respiration pattern is sampled from a respiratory inductive plethysmograph (RIP) band worn around the speaker's chest. Ratio of inhalation duration to exhalation duration (IE ratio) has traditionally been used to detect speaking in controlled lab environment...
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