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Ultrasound is one of the imaging modalities commonly used for detecting mass abnormalities of nodule. The observation of ultrasound images is conducted by the radiologists, which tend to be subjective. Therefore, the use of computer aided diagnosis (CADx) system based on image processing can assist the radiologists to give more objective decision-making for detecting the mass abnormalities of nodule...
This paper addresses the problem of handwritten and printed text separation in Arabic document images. The objective is to extract handwritten text from other parts of the document. This allows the application, in a second time, of a specialized processing on the extracted handwritten part or even on the printed one. Documents are first preprocessed in order to remove eventual noise and correct document...
This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression...
This paper presents a process of knowledge Discovery from Data (KDD) applied on 2D Inverse Synthetic Aperture Radar (ISAR) images. This process is based on four crucial steps which are data acquisition, data pre-processing, data representation and data classification to make the final decision. We propose a new method for data representation based on combining mathematical morphology top-Hat, Thresholding...
Figure-Ground Segmentation simply means separating foreground from it's background. It has many applications in day to day life. Object recognition is one of the main applications of it. Extracting foreground from it's background is not an easy task. Various techniques are available for figure-ground segmentation. In this paper, a new approach is proposed to extract foreground from background. A high...
Moving Object detection is a challenging tool because shape and size of the object in a video vary significantly according to camera direction, partial occlusion and poses. Significant research has been carried out for detecting people in videos. Traditional methods for detecting human used sliding window approach which involved scanning various sizes of windows across an image. Hence in this work...
In recent years, biometric-based authentication systems have been widely used in many applications which require reliable identification scheme. Among others, handwritten signature is one of the most interesting biometric means, that is being considered with renewed interest. This paper presents some of the most relevant advances in the field of offline signature identification and highlights some...
Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods...
Algorithms for extracting information about the structures present in an image are known as segmentation algorithms and play an important role in numerous biomedical applications where the images are the primary source of information. Image segmentation is a fundamental process in the area of medicine, although many alternatives have been proposed to solve the problem properly segment the objects...
This paper discusses novel texture features that allow providing enhanced classification accuracy for focal hepatic lesions. The proposed texture features takes advantage of the rotation and scale invariant nature of Gabor wavelets, as well as the gray-level co-occurrence matrix (GLCM) for analyzing the spatial distribution of the pixel intensity in the lesion. To verify the effectiveness of the proposed...
A new method for supervised hyperspectral data classification is proposed. In particular, the notion of Stochastic Minimum Spanning Forests (MSFs) is introduced. For a given hyper-spectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step...
This paper presents a new, efficient technique for supervised texture segmentation based on a set of specifically designed filters and a multi-level pixel-based classifier. Filter design is carried out by means of a neural network, which is trained to maximize the filters' discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed...
In tennis match highlights mainly take place in shots containing full court (Court view shots), therefore successful court view shots detection is useful for highlights extraction. This paper proposes a court view shots detection algorithm, in which shot detection that is the precondition of usual shot classification is given up for shot detection not only costs more time, but also its detection performance...
A medical image retrieval system combined of the low-level image feature and high-level semantic is used in the paper witch includes two main parts: image preprocessing and the machine learning. In the first part, feature tree structure is presented to reduce the semantic gap and in the latter part, a novel machine learning method based on SVM is presented to optimize the Network parameters by which...
Associative classification (AC) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high accuracy. The traditional AC algorithm discovers the association rules with the frequency count (minimum support) and ranking threshold (minimum confidence) while restricted to the concepts (class labels). In this paper, we propose a novel framework with...
Text and non-text segmentation and classification is very important in document layout analysis system before it is presented to an OCR system. Heuristic rules have been used in segmenting and classifying the text and non-text blocks. This research focuses on the classification of non-text block in technical documents into table, graph, and figure. A comparative study is conducted between backpropagation...
An effective algorithm for number and letter character recognition is proposed in this paper. Our algorithm employs template matching, but it unlike traditional template matching method using the original pixel value to match. Our algorithm draws some features from the original image, and then obtains an eigenvector of 192 dimensions. Before drawing features, the image is disposed using math morphologic...
Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on support vector machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse...
Structured diagrams are very prevalent in many document types. Most people who need to create such diagrams use structured graphics editors such as Microsoft Visio. Structured graphics editors are extremely powerful and expressive but they can be cumbersome to use. We have shown through extensive timing experiments that structured diagrams drawn by hand will take only about 10% of the time it takes...
We describe a method to automatically extract important video objects for object-based indexing. Most of the existing salient object detection approaches detect visually conspicuous structures in images, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in...
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