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Inspired by recent successful deep learning methods, this paper presents a new approach for polarimetric synthetic aperture radar (PolSAR) image classification. It combines both advantages of pixel-based and object-based methods. An improved simple linear iterative clustering (SLIC) superpixel segmentation algorithm is used to obtain spatial information in the PolSAR image. Then, a Deep Belief Network...
Detection of repetitive patterns in images is subject of several research papers. The majority of them deals with detection of lattice patterns of repetitive elements. However, there are many situations, when element's repetition doesn't follow any particular pattern. In this paper we focus on the following two objectives. Firstly, our algorithm detects repetitive elements regardless of their relative...
This paper studies the segmentation methods by analyzing the threshold method of rubbings. Firstly, OTSU and fast 2 dimensional OTSU threshold segmentation algorithms are presented, and the segmentation effect of a given image is analyzed. The limitation of OTSU, and fast 2 dimensional OTSU segmentation algorithm are explained. Then the segmentation algorithm is given in conjunction with the histogram,...
Detection and segmentation of cells is an important step for classifying the cells as cancerous or non-cancerous. Pathologists use microscopic images for analysis and further diagnosis of cancer. These images contain the microscopic structure of tissues and are stained using some staining components to facilitate the process. Staining process varies due to different stain manufacturers, staining practices...
Oil spills is a major threat to ocean ecosystems. The capability of synthetic aperture radar (SAR) sensors to detect oil spills over the sea surface is established and proven. Oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena (e.g., manmade actions, geological conditions, and meteorological and hydrological effects). The current researches...
Robust obstacle detection is an important task for unmanned ground vehide(UGV). Vegetation in off-road environments poses great challenges to this task. Usually, vegetation should not be considered as obstacles for off-road UGVs since they are soft and drivable. On the other hand, there are also possibilities that real obstacles exist in the vegetation, which makes the problem difficult. In this paper,...
In this paper, we propose a new method to automatically extract the main structure (fuselage and wings) of straight wing aircraft in photographic images using line clustering. We exploit the location, length and orientation features (parallel constraint) of line segments to group them into line clusters. The different line clusters correspond to the different structure of straight wing aircraft, and...
Skin cancer is one of the most deadly cancers in the world. If not diagnosed in early stages it might be hard to cure. This paper suggests a new approach for automatic segmentation and classification of skin lesion for dermoscopic images. The segmentation is based on a pre-processing; using the color structure-texture image decomposition to decompose a textured image into texture and geometrical components...
Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems able to classify a set group of species known a priori. This greatly limits deployability as classification systems must be retrained for any field with a different...
While accurate tumor delineation in FDG-PET is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. An automatic segmentation method based on clustering is developed in 3-D, where, different from available...
Detection and counting of white blood cells (WBC) in blood samples provides valuable information to medical specialists, helping them to evaluate a wide range of important hematic pathologies such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and time consuming. An automatic detection and classification of WBC images can enhance the accuracy and speed up the detection...
Vision based environmental monitoring using fixed cameras generates large image collections, creating a bottleneck in data analysis. In areas with limited background knowledge of the monitored habitat, this bottleneck can often not be overcome by traditional pattern recognition methods. A new change detection method to identify interesting events such as presence and behavior of different species...
Since many sky-survey observations were performed, as well as appreciable amount of data were obtained, study on large-scale evolution of our Universe has become a field of interest. In this work, we concentrate on the X-ray astronomical samples from NASA's Chandra observatory, and propose an approach to classify galaxy clusters (GCs) based on their central gas profiles' morphological features. Firstly,...
Several segmentation methods have been presented for breast ultrasound (BUS) images. Unfortunately most of them are supervised and semi-automatic in nature. In this paper, a complete unsupervised algorithm for BUS image segmentation algorithm using local intensity and texture histograms features has been proposed. The texture and intensity features are combined in the clustering process. Initially...
Breast cancer is the second leading cause of cancer death in women according to World Health Organization (WHO). Development of computer aided diagnostic (CAD) systems has great importance as a secondary reader systems for a correct diagnosis and treatment process. In this paper, a deep learning based feature extraction method by convolutional neural network (CNN) is proposed for automated mitosis...
In this article, a Combination of Fuzzy logic and Texture features based segmentation approach for retinal blood vessel segmentation is proposed. The proposed approach employs a new methodology for segmenting the blood vessels from the retinal images more effectively. The overall process is carried out in five steps. The first step is to pre-process the image in order to remove the presence of noise...
Glossoscopy is an important part of Traditional Chinese Medicine (TCM). To analyze the tongue properties objectively, we need extract the tongue region from images. This paper presents a method to segment the tongue images based on kernel FCM (Fuzzy Cluster means). Firstly we pre-processed the tongue images by gray-level integral projection. Secondly the features were extracted to form a feature vector...
In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (PCA) is used for better representation of features about observation screens. The optimal number of component related with observation screen is determined by Horn's Parallel Analysis...
Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images...
This paper introduces an automatic classification of mammogram images by categorizing malignant or normal after segmenting the suspected region. Fuzzy and fuzzy soft set approaches have been used successfully to deal with diverse uncertainties, imprecision and vagueness in data. We have advocated a method of fuzzy soft set using fuzzy soft aggregation operator for solving the problem. The proposed...
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