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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,...
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...
Large-scale 3D point clouds have been actively used in many applications with the advent of capturing devices. In this paper, we propose a novel saliency detection algorithm for large-scale colored 3D point clouds which capture real-world scenes. We first voxelize an input point cloud, and then partition voxels into a supervoxel which corresponds to a clusters at the lowest level. We construct the...
Video co-segmentation typically refers to the task to jointly segment common objects existing in a given group of videos. In practice, high-dimensional data such as videos are often conceptually thought of being drawn from a union of subspaces corresponding to multiple categories. Therefore, segmenting data into respective subspaces, known as subspace clustering, has widespread applications in computer...
Tongue diagnosis is one of the important topics in the field of Chinese traditional medicine (TCM), and color is the basic element of tongue image, it has important diagnostic value. This paper presents a novel approach to extract color feature of tongue images. First, we use iterative method to extract initial main color and initial number of main color, then we adopt GLA(Generalized Loyd Algorithm)...
Under the environment of big data, retrieval becomes a crucial technology and image retrieval is paid more attention and widely used. The paper proposes a second-order retrieval algorithm, of which can be used to retrieval the similar images. Firstly, extracting image sift features. Then, build frequency table of characteristic words by k-means clustering and bag of word algorithm. Finally, based...
In this paper we present a new method for content-based searching large image databases by comparing content of a query image and images stored in a database. The algorithm consists of three main steps: feature extraction, indexing and system learning. The feature extraction stage is based on two types of features (SURF keypoints and color). For indexing we use the k-means algorithm and for system...
The fire accident usually causes economical and ecological damage as well as cause danger to people's lives. Therefore, its early detection is must for controlling this damage. Also smoke is considered as main constituent of fire, thus an efficient smoke detection algorithm on sequences of frame obtained from static camera is proposed. It is based on computer vision based technology. This algorithm...
This paper proposes a graph-based Web video search reranking method through consistency analysis using spectral clustering. Graph-based reranking is effective for refining text-based video search results. Generally, this approach constructs a graph where the vertices are videos and the edges reflect their pairwise similarities. A lot of reranking methods are built based on a scheme which regularizes...
This work presents a method for identifying plant leaf disease and an approach for careful detection of diseases. The goal of proposed work is to diagnose the disease of brinjal leaf using image processing and artificial neural techniques. The diseases on the brinjal are critical issue which makes the sharp decrease in the production of brinjal. The study of interest is the leaf rather than whole...
This paper deals with the automatic detection of the region of interest (ROI) in Histopathologoical images by means of advanced segmentation techniques. The clustering techniques like fuzzy C-means (FCM) and K-means algorithms for the color image segmentation are implemented on a dataset which consisted of skin images. Euclidian distance is used as a distance metric in both the algorithms. The resultant...
To perform a semantic search on a large dataset of images, we need to be able to transform the visual content of images (colors, textures, shapes) into semantic information. This transformation, called image annotation, assigns a caption or keywords to the visual content in a digital image. In this paper we try to resolve partially the region homogeneity problem in image annotation, we propose an...
The exploration of under-ice environments has seen increased interest over the past few years due to advances in technological capabilities, such as autonomous underwater vehicles (AUVs), as well as interest in exploration of polar regions and Jupiter's ice-covered moon Europa. Searching for interesting features under the ice, including animals capable of sustaining life in such harsh environments,...
This paper describes different approaches for detection and identification of diseases in apples using computer vision. Our proposed algorithms analyze surface appearance of apple for defects using image features, viz. color and texture. For segmentation of Region Of Interest (ROI), K-means clustering is performed over the image pixels based on their intensity values. For creation of feature vector,...
Person identification is an important but still challenging problem in video surveillance. This work designs a completely automatic appearance-based person identification system, which has the ability to achieve new person discovery and classification. The proposed system consists of three modules: background and silhouette separation; feature extraction and selection; and online person identification...
Computer vision based road detection is an indispensable and challenging task in many real-world applications such as obstacle detection in autonomous driving. Low-level image features (e.g., color and texture) and pre-trained models are commonly used for this task. In this paper, we propose a simple yet effective approach to detect roads from a single image, which avoids the supervised model training...
This research is addresses to determine the dominant species that located in the overlapped clusters produced by the Kohonen Self-Organizing Map (KSOM). Before, KSOM algorithm able to cluster the tropical wood species data set effectively and accurately according to the wood features, which is wood pores sizes. Unfortunately, there are seven overlapped clusters in the clustering result and this is...
A High Definition visual attention based video summarization algorithm is proposed to extract feature frames and create a video summary. It uses colour histogram shot detection algorithm to separate the video into shots, then applies a novel high definition visual attention algorithm to construct a saliency map for each frame. A multivariate mutual information algorithm is applied to select a feature...
Understanding human activities is an essential capability for intelligent robots to help people in a variety of applications. Humans perform activities in a continuous fashion, and transitions between temporally adjacent activities are gradual. Our Fuzzy Segmentation and Recognition (FuzzySR) algorithm explicitly reasons about gradual transitions between continuous human activities. Our objective...
Finding an object in a 3D scene is an important problem in the robotics, especially in assistive systems for visually impaired people. In most systems, the first and most important step is how to detect an object in a complex environment. In this paper, we propose a method for finding an object using geometrical constraints on depth images from a Kinect. The main advantage of the approach is it is...
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