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Histopathological examination is the gold standard for many kinds of cancer diagnosis as the histopathology slides provide a cellular level view of the disease. Computer-aided automated diagnosis techniques have recently been developed based on the analysis of skin histopathological images to diagnose skin cancer. Due to the very high resolution characteristics of the skin whole slide images, the...
In the present work, Particle swarm optimisation (PSO) based Tsallis entropy method is employed to segment the buried object SONAR images. This SONAR detects the objects present beneath the seabed in ocean. Objects may be pipelines, and unexploded ordinances buried beneath the seabed. Computer vision for object detection is required when SONAR is equipped in autonomous underwater vehicle. The vehicle...
Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers...
In this paper we propose a system for the problem of facade segmentation. Building facades are highly structured images and consequently most methods that have been proposed for this problem, aim to make use of this strong prior information. We are describing a system that is almost domain independent and consists of standard segmentation methods. A sequence of boosted decision trees is stacked using...
This paper presents a fast segmentation of iris portion from an eye image in an iris recognition system. In iris segmentation, we have to find the inner boundary (between pupil and iris) and outer boundary (between sclera and iris). To find the inner boundary a restricted circular Hough transform based method is applied. To locate the outer boundary image is first passes through inversion transform...
PASCAL VOC Segmentation Challenge [10] is currently considered as one of the datasets that reflect the image segmentation difficulties for real world scenarios [29]. However, current evaluation is simply based on a single Inter-section Over Union (IOU) score. In this paper, we try to discover the error factors under the IOU, which makes the results more informative to understand rather than a black...
In this paper, a building facade detection algorithm is proposed. Proposed algorithm first detects the edges present in an image and connects them into line segments. Then a polar histogram mapping is used to find the vanishing points in the scene. An approach with low complexity is introduced to cluster the line segments according to their vanishing points. Line segments and their corresponding vanishing...
Segmentation of mathematical equations from document images is already a major research area for improved performance of OCR systems. Though chemical equations are also sharing similar spatial properties as that of non-chemical equations (for example, mathematical equations), efforts to segment those are still to be explored. This paper presents a novel method for segmenting and identifying chemical...
Due to an increased need for efficient and objective evaluation of large amounts of data, MRI-based medical image analysis is gaining attention in recent times. The goal is to simplify an image into something that is more meaningful and making it easier to analyze. The aim of medical image segmentation in brain MRI is to separate the region of interest from the background after denoising and skull...
Diabetic Retinopathy (DR) is an eye filled illness caused by the complication of polygenic disease and that is to be detected accurately for timely treatment. As polygenic disease progresses, the vision of a patient could begin to deteriorate and leads to blindness. In this proposed work, the presence or absence of retinal exudates are detected using machine learning (ML) techniques. To detect the...
The paper presents a pornographic image recognition using fusion of scale invariant descriptor. The pornographic image means the image contains and shows genital elements of human body having large variability due to poses, lighting, and backgrounds variations. The fusion of scale invariant descriptor that is holistic feature is employed to handle those variability problems. This holistic feature...
In the paper we have proposed a two level k-means segmentation technique for eczema skin lesion segmentation. Two class criteria is used for classifying the normal skin and eczema skin lesions using Mahalanobis distance. In order to further improve the segmentation performance normalized color spaces are used. Our experiments include RGB and CIElab color models; and their color space normalized-I...
We propose a novel contrast enhancement method for dark images using the value gap expansion force (VGEF) and the sorted histogram equalization. Based on the observation that the inter-pixel relationship is analogous to the electrostatic force, we define the pixel field spread around each pixel and the pixel mass at each pixel position. We compute the VGEF exerted to a pixel by multiplying the pixel...
In this paper, we present multiple active contours for image segmentation that use scalable local regional information on expandable kernel. It includes using a strategy inside the variational level set method to adapt the size of a local window in order to avoid being stuck locally in a homogeneous region during the segmentation process. It also provides a multiple level set framework to deal with...
Radiologists are known to suffer from fatigue and drop in diagnostic accuracy due to large number of slices to read and long working hours. A computer aided diagnosis (CAD) system could help lighten the workload. Segmentation is the first step in a CAD system. This study aims to propose an accurate automatic segmentation. This study deals with High Resolution Computed Tomography (HRCT) scans of the...
In this paper, we address the problem of interactive image segmentation which segments an image based on user-supplied scribbles. For this purpose, we propose a novel framework that provides consistent performance robust to the location of input seeds. Most of the existing methods, especially random walk-based approaches, strongly depend on initial seed positions, which differ from one user to another...
Accurate liver segmentation from computed tomography (CT) images is problematic due to non-uniform density, weak boundaries and because there may be multiple liver tumors that have heterogeneous intensities in region(s) of interest (ROIs). So we propose a generalized energy framework that harnesses the statistical intensity approximation with image data on graphs. Our statistical energy term takes...
The authors propose a novel pre-processing phase that can be integrated into conventional methods to detect and recognize planar visual objects in printed materials with low computational cost and higher accuracy. A simple yet efficient visual saliency estimation technique based on regional contrast is developed to quickly filter out low informative regions in printed materials. By eliminating noisy...
This paper proposes a semi-supervised spectral clustering algorithm combined with Bayes decision, for Low stability and accuracy of spectral clustering algorithm. This method is clustered according to color, texture and spatial characteristics of the image. It first adjusts the similarity matrix by distance learning methods based on Bayes decision to improve clustering distribution of feature vectors,...
Steel is a widely used material in the industry and household. The presence of defects on the surface of steel strip has serious implications and limit its use for quality purpose significantly. The primary objective of this paper is to develop a method for classification of steel surface defects such as Blister, Scratch and pseudo defect like Water droplet. This paper proposes artificial neural network...
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