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This paper proposes an iterative unsupervised Markov Random Field (MRF) based segmentation technique for polarimetric Synthetic Aperture Radar (SAR) image using the optimized scattering mechanism similarity parameters. Parameter estimation for the MRF model is generally performed from the available training data in order to perform tasks including semantic image segmentation. Since the current scenario...
The text line segmentation process is a key step in an optical character recognition (OCR) system. Several common approaches, such as projection-based methods and stochastic methods, have been put forward to fulfill this task. However, most of existing methods cannot be directly applied to process the palm leaf manuscripts of Dai which the images have poor quality and include smudges, creases, stroke...
Due to the coupling of model parameters, most mixture models based on Markov random field for image segmentation cannot be computed directly by EM algorithm. The paper introduces spatial correlation into mixture models by two ways: First, visual description and spatial position of pixels are considered as co-occurrence data and a joint Gaussian mixture models are built to make use of spatial constraint...
Visual tracking is a significant and challenging task in computer vision. In this paper, we consider visual tracking as random walks on ergodic Markov chain, where nodes are represented as superpixels and edges represent their relationships. The graph model and Markov theory are integrated to construct ergodic Markov chain. Based on the random walks and introduction of positive and negative template...
In this study a supervised classification and dimensionality reduction method for hyperspectral images is proposed. For this purpose, using probabilistic principal component analysis (PPCA), dimensionality reduction is performed and a Gaussian mixture model (GMM) is built. Alongside this mixture model, spatial information is also included into the classification process by taking advantage of pixel...
Urdu Nastaleeq is a highly cursive, context sensitive language, written diagonally from top right to bottom left that makes it difficult to segment the partial word or a compete word into characters. Further due to stacking of characters, the segmentation at the character level is hard to perform. Some researchers have performed the ligature level segmentation and have succeeded to a great extent,...
The image segmentation is a fundamental tool to analyze and detect objects of interest that can be applied in many fields (medicine, satellite). In this work, we present a classical Markov model for unsupervised image segmentation: "Hidden Markov Chain with Independent Noise" (HMC-IN) for segmenting both gray and color images. Then, we compare five iterative algorithms EM, GEM, SEM, MCEM...
A brain tumour is a growth of cells in the brain that multiplies in an abnormal and uncontrollable way. The estimation of brain tumour volume is important for diagnosis and treatment process. The computed tomography is one of the most important devices used for detection, diagnosis, and volume estimation of the brain tumour. The most common disadvantage of this device is the high radiation dose that...
Brain tumors have been created by abnormal and uncontrolled cell division inside the brain. A crucial and lengthy task is the segmentation of brain tumors, which can be gained manually with the help of Computed Tomography (CT). Treatment, diagnosis, signs and symptoms of the brain tumors mainly depend on the volume, shapes and location of the tumors. The accuracy and time of detecting brain tumor...
Biomedical image processing is an emerging and most challenging field in recent years. Large sets of similar images are produced by medical imaging applications. Manual image interpretation and analysis becomes a fastidious task due to the huge amount of data. In medical image processing, to classify an image on the basis of statistical inference, MRF classification is a well established method. In...
In the recent times various malicious attacks can be performed over images for duplicating the images which introduces huge amount of challenges in the area of forgery detection and feature based image authentication. Researchers have focused on the development of efficient image forgery detection techniques which can be applicable to optimize image retouching attacks. The current research trends...
Knowledge of vertebra location, shape and orientation is crucial in many medical applications such as orthopedics or interventional procedures. The wide range of shapes, joint alterations and pathological cases encountered in an aging population makes automatic segmentation sometimes challenging. This paper presents a new automated vertebra segmentation method for 3D CT data which tackles these problems...
In this paper we propose a Hidden Markov Model for modeling and extracting vine structure from images. We built up from previous research to infer connectivity of cane segments extracted from binary images. We use skeletonisation and polylines to model cane segments and we use simulated annealing to optimize an energy function defined in terms of attributes observed for each connection. We formulate...
In this paper we present a line based word spotting system based on Hidden Markov Model for offline Indic scripts such as Bangla (Bengali) and Devanagari. We propose a novel approach of combining foreground and background information of text line images for keyword-spotting by character filler models. The candidate keywords are searched from a line without segmenting character or words. A significant...
This paper describes a comparison between online handwritten cursive word recognition using segmentation-free method and that using segmentation-based method. To search the optimal segmentation and recognition path as the recognition result, we attempt two methods: segmentation-free and segmentation-based, where we expand the search space using a character-synchronous beam search strategy. The probable...
Complexities in the facial recognition increases because of real time image acquisition, which is generally not performed by an expert. Because of the inappropriate focus, the positional aspect of the input image can be different. In this work, a directional aspect based structural analysis is provided for generating the Local binary pattern. For a single face about 60 different binary patterns are...
In this paper, a method for building a 3D map of some objects detected in an indoor environment is presented. The pecularity of the proposed algorithm is that it works with a simple consumer-grade webcam. With the webcam, pictures of the environment are taken. The proposed method first extracts the regions which may contain an object from the pictures. The regions are then classified to identify the...
This paper presents a novel method for facial expression recognition using only sequence level labeling. With facial image sequences containing multiple peaks of expression, our method aims to label these sequences and identifies expressional peaks automatically. We formulate this weakly labeled expression recognition as a multi-instance learning (MIL) problem. First, image sequences are clustered...
Identification of a human face in a crowded flux plays an important role in the context of surveillance. Considerable amount of research has been carried out on face identification in different applications. Accordingly, different researchers propose new algorithms. This paper attempts to showcase a novel methodology through which any face may be identified in a large crowd of human face. This proposed...
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