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Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels...
In this work, we employ a pair wise Markov Random Field (MRF) and a Conditional Random Field (CRF) for bi-level image segmentation and denoising. For both tasks, the Ising pair wise model and the Iterative Conditional Mode (ICM) inference method are implemented, assuming the parameters of the unary and pair wise potentials are known. Experimental results demonstrate the effectiveness of the proposed...
CAPTCHAs exploit the gap in the ability between a human and a machine to understand the semantics of specific multimedia content, with vast applications in computer security. In this paper we compare two techniques in automated CAPTCHA solving for text-based CAPTCHA schemes, i.e., Classification based on the Vector Space Model (VSM) versus a popular Optical Character Recognition (OCR) engine. For...
This paper proposed a robust segmentation method which can adopt varied conditions. The local binarization algorithm and global binarization are used as well as the blob analysis algorithm. Based on the long line fitting algorithm, the bottom frame connected with the characters is removed; based on the characters' gradient lines and average width and height information, the left and right frames are...
We consider the use of graph cuts technique to efficiently segment the full tissue volume of the entire heart or important parts of it such as ventricles in Magnetic Resonance Imaging (MRI) scans for different species. With the segmented 3-D volume of the heart, simulations of electrical waves propagating through the tissue can be done. The modeled wave results can then be compared directly to the...
According to the problems that there are low detection rate and high false reject rate based on traditional image processing algorithms, this article proposes a method that combines SSDA and character contour searching algorithms to recognize the digital segment codes' large flaws and extract characters in the VFD image, and by image subtraction, maximum between-cluster variance (Otsu) and mathematical...
Infrared (IR) ship image segmentation is a challenging task due to defects of IR images, such as low-contrast, sea clutters, noises and etc. Aiming to solve this problem, we propose a multiple features based IR ship image segmentation method using fuzzy inference system (FIS). Because of complexness of the low-contrast IR image, the ship target cannot be segmented by only one kind of feature. Thus...
Accurate liver segmentation is an essential and crucial step for computer-aided liver disease diagnosis and surgical planning. In this paper, a new coarse-to-fine method is proposed to segment liver for abdominal computed tomography (CT) images. This hierarchical framework consists of rough segmentation and refined segmentation. The rough segmentation is implemented based on a kernel fuzzy C-means...
Interest on anomaly detection for hyperspectral images is increasingly growing the last decades due to the diversity of applications that aims for detecting small distinctive objects dispersed in a large geographic zone, without any prior knowledge about the scene. In addition to the absence of prior knowledge, many problems are particularly challenging for the anomaly detection such as the differentiation...
Extracting the structures of interest accurately is one of the main challenges in medical imaging segmentation. Statistical models of shape are a promising approach for robust and automatic segmentation of medical image data. This work describes the construction of a statistical shape model of the Radius bone. For 3-D model-based approaches, however, building the 3-D shape model from a training data...
Magnetic Resonance Image is one of the technologies used for diagnosing brain cancer. Radiographers use the information obtained from MRI images to diagnose the disease and plan further treatment for the patient. MRI images are always corrupted with noise. Removing noise from images is crucial but it is not an easy task. Filtering algorithm is the most common method used to remove noise. A segmentation...
Studies of medical image segmentation have long been done as a mean to distinguish object region from one to another for further image analysis. The segmentation of lung region in chest X-ray (CXR) based on object edge detection is one of the popular method applied. Early edge detection algorithms like Sobel, Prewitt and Laplacian have been used to segment the lung however, none of them can successfully...
Segmentation of line, word and character are one of the critical phases of optical character recognition (OCR). Due to the imperfection in segmentation, most of the recognition system produce poor recognition rate. In this paper we are discussing some novel approach for line, word and character segmentation of printed Manipuri document. Few works has been done for optical character recognition on...
Image segmentation is mostly used as a fundamental step in medical image processing, especially for clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) algorithm is one of the well known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MR images to high number of clusters with different noise levels and real images because...
Optical coherence tomography (OCT) has become a famous ophthalmic diagnostic technique recent years. It is a non-invasive imaging method and causes no damage to the eyes. Among the information OCT is able to provide, the thickness of choroid, especially epichoroidal space can reflect many diseases of the retina, which makes it a very important basis for diagnosing. However, up to now, there are few...
Fuzzy clustering techniques, especially Fuzzy C-Means clustering method (FCM), is a popular algorithm widely used in the images segmentation. However, as the conventional FCM doesn't optimize data in feature space and doesn't involve any spatial information, it is sensitive to the noise. In the paper, we presented a novel FCM clustering algorithm based on kernel spatial information to segment the...
We describe a novel appearance model with optimal combined features to produce the accurate vessel segmentation. It starts with investigating a set of multi-scale vessel features, followed by a weighed approach to optimally combine different features. Then the optimally combined features advantage the appearance model to reveal more detailed information of vessel. The novelty of the work lies in the...
The precise segmentation of bone regions is important in applications where measurements are taken from the extracted tissues. In high quality biomedical images, these extracted regions can be processed to study the underlying biology, like the relationship between bone structure and genetics. In studying the relationship between trabecular tissue growth patterns and the genes governing that particular...
Image segmentation is an important task in computer vision. The task of image segmentation is to portion image into segments, thus provide more meaningful information of the image contents. Many methods have been developed for numerous application. The common problems of most of the segmentation techniques are scattered segmentation lines, too much details, small or thin segments, and noisy segmentation...
In many medical imaging applications, a clear delineation and segmentation of areas of interest from low resolution images is crucial. It is one of the most difficult and challenging tasks in image processing and directiy determines the quality of final result of the image analysis. In preparation for segmentation, we first use preprocessing methods to remove noise and blur and then we use super-resolution...
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