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Microcalcifications are the earliest sign of breast carcinoma. Their typical size is about 1 mm, which is why it is difficult to detect for an expert. Therefore, a tool that eases their visualization becomes relevant. Segmentation gives the candidate areas that could contain microcalcifications. A preprocessing step can improve segmentation performance but the algorithm becomes database dependent...
Depth estimation, which is mostly performed by stereo vision, is a remarkable task in vision and scene understanding. In this paper, depth map estimation from a single image is investigated and applied in pedestrian candidate generation. To recover accurate depth map from a single image, a Markov Random Field (MRF) model that incorporates both image depth cues and the relationships between different...
In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing...
Intracranial hemorrhage (ICH) is a vital disease which occurs due to leakage or rupture of blood vessels within the brain tissue. In this paper, we propose a novel three-dimensional (3D) method for segmenting hemorrhage regions from a series of brain computed tomography (CT) images. This method combines a supervoxel approach for rough segmentation and three-dimensional graph cuts for refined segmentation...
This paper presents a fast algorithm to segment moving objects in video sequences, as the first step of a fast object tracking system. It is based on the detection of level lines to detect closed objects contours in a scene. The detected objects are clustered using a combination of mean shift and ensemble clustering. The proposed method produces a temporal video segmentation in a fraction of the processing...
This paper presents a multi-label automatic GrabCut technique for the problem of image segmentation. GrabCut is considered as one of the binary-label segmentation techniques because it is based on the famous s/t graph cut minimization technique for image segmentation. This paper extends the automatic binary-label GrabCut to a multi-label technique that can segment a given image into its natural segments...
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...
Image segmentation is a fundamental process in computer vision applications. This paper presents a novel method to deal with the issue of image segmentation. Each image is first segmented coarsely, and represented as a graph model. Then, a semi-supervised algorithm is utilized to estimate the relevance between labeled nodes and unlabeled nodes to construct a relevance matrix. Finally, a normalized...
This paper proposes a clustering based image segmentation approach for elephant recognition. Appreciable recognition rate was achieved by k-means clustering technique followed by feature extraction and K nearest neighbour (K-NN) classifier. The k-means algorithm employs the concept of fitness and belongingness to provide a more adaptive andbetterclustering process as compared to several conventional...
An effective graph-based image segmentation using superpixel-based graph representation is introduced. The techniques of SLIC superpixels, 5-D spectral clustering, and boundary-focused region merging are adopted in the proposed algorithm. With SLIC superpixels, the original image segmentation problem is transformed into the superpixel labeling problem. It makes the proposed algorithm more efficient...
Spectral clustering has represented a good alternative in digital signal processing and pattern recognition; however a decision concerning the affinity functions among data is still an issue. In this work it is presented an extended version of a traditional multiclass spectral clustering method which employs prior information about the classified data into the affinity matrixes aiming to maintain...
The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based both on heuristic rules and AdaBoost classification...
Logo spotting is of a great interest because it enables to categorize the document images of a digital library of scanned documents according to their sources, without any costly semantic analysis of their textual transcript. In this paper, we present an approach for logo spotting, based on the matching of keypoints extracted both from the query document images and a given set of logos (gallery) using...
In this study, novel image clustering algorithm is investigated to improve the clustering performance. We have investigated this model and have achieved improved clustering performance by fine tuning the related model parameters. Yi Yang (2010) proposed clustering algorithm namely local discriminant model and global integration (LDMGI). Clustering parameters are number of nearest neighbours (k) and...
This paper designs and realizes a bagged tablets checking system. First, according to tablet characteristics, color feature library and shape feature library are created. Second, the tablet image is clustered by K-means algorithm. And threshold segmentation for the tablet image use Ostu method. Finally, the recognition result of sorts of tablets in bag will compare with prescription in database written...
This paper presents an image segmentation algorithm, called ISIB, based on the Information Bottleneck (IB) method. ISIB extracts the image patterns by maximally preserving the mutual information between the segments and the gray scale values. There are two stages in our algorithm, partitioning the image and merging the segmentations. In the partition process, we segment an image by maximizing the...
An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which...
A video copy detection system that is based on content fingerprinting and can be used for video indexing and copyright applications is proposed. The system relies on a fingerprint extraction algorithm followed by a fast approximate search algorithm. The fingerprint extraction algorithm extracts compact content-based signatures from special images constructed from the video. Each such image represents...
Image segmentation is a hard task and many methods have been developed to alleviate its difficulties. A common preprocessing step designed for this purpose is to compute an over-segmentation of the image, often referred to as superpixels. In this paper, we propose a new approach to superpixels computation. In a first step, a hypergraph-based representation of the image is built. Then, a coarsening...
Clustering is a useful approach in data mining, image segmentation, and other problems of pattern recognition. Fuzzy clustering process can be quite slow when there are many objects or pattern to be clustered. This article discusses about an algorithm, ckMeans, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting partition quality. The reduction...
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