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As a preprocessing step of many applications, such as object recognition, image retrieval and scene analysis, saliency detection plays an important role and remains a challenging and significant problem in computer vision. Most existing bottom-up methods utilize local or global contrast information to compute the saliency maps, whereas a few methods generate saliency maps with the use of background...
This paper discusses the performance evaluation of the Content Based Image Retrieval (CBIR) system using the optimality in selection of feature vector elements. The performance of the CBIR system may be improved by appropriate analysis of the image. Image analysis is still facing problems related to the detection of the objects. In spite of the noticeable achievements using the part based model, the...
Recently, the several applications of the probabilistic model based on two of the main concepts in quantum physics - a density matrix and the Born rule, have been introduced. It was shown that the model can be suitable for the modeling of learning algorithms in biologically plausible artificial neural networks framework, like it is the case of on-line learning algorithms for Independent /Principal/Minor...
This paper contains short description of cluster analysis algorithm for the mineral rock recognition in the mining industry. In this paper it describes the algorithm for automatic segmentation of color images of rocks, using the methods of cluster analysis. There are results of studies different color spaces for clustering k-means. Some realizations of this algorithm for computing the grading of mineral...
The conventional approaches for habitats mapping based on supervised algorithms need a seabed ground truth classes to know the entire seabed types before the training phase. These approaches give satisfying results only when a comprehensive training set is available. If the training set lacks a particular kind of seabed, it will be unknown for the classifier and the classification will be reduced...
In this paper, we propose a faster and more efficient color image segmentation technique, which is called local window K_means (LWK_means), consisting of three modules: window presetting, local window clustering, windows merging. LWK_means divides the color image into many windows, and then parallelly processes each window using the proposed local window K_means clustering algorithm, which is adaptive...
An effective PSO fuzzy clustering edge detection algorithm is proposed. PSO algorithm and Fuzzy C-Mean algorithm are combined to overcome two shortcomings, namely the initialization sensitivity and the local minimum of standard FCM algorithm in image edge detection. At first, a vector is constructed to describe edge point information, which includes neighborhood homogeneity information measure, orientation...
Image segmentation is a fundamental problem in computer vision. Normalized Cut (Ncut) scheme uses second smallest eigenvector for solving this problem, while such eigenvectors may be sensitive to undesired changes in image. In this paper, firstly, we point out that optimization of Ncut is equivalent to optimization of Fisher-Rao criterion in classification. Then we look at the classification experience...
Reliable extraction/segmentation of text lines, words and characters is one of the very important steps for development of automated systems for understanding the text in low resolution display board images. In this paper, a new approach for segmentation of text lines, words and characters from Kannada text in low resolution display board images is presented. The proposed method uses projection profile...
In this work we propose a translational, rotational and scaling invariant scheme for possible detection of tumors in Brain-Magnetic Resonance (MR) images. The method incorporates the features like shape, position and texture to accurately diagnose from the infected images. The geometric transformation invariant nature of the method helps in detecting the tumor in various scales, positions and orientations,...
In this article, the particle swarm optimization and differential evolution algorithms inspired by the intrinsic principles of quantum mechanics are presented. These quantum versions of meta-heuristic algorithms, namely quantum inspired particle swarm optimization and quantum inspired differential evolution for multi-level thresholding have been designed to find optimal thresholds of colour images...
This paper presents an operator of fuzzy clustering method of image segmentation based on Local Binary Pattern (LBP). Semi-supervised learning and fuzzy clustering method are introduced in order to overcome the problem of initial clustering sensitive. Also, local binary pattern operator is introduced to construct the space feature vectors of pixels, which makes full use of the space characteristics...
In this paper, a new method for heart vessel extraction based on heart area segmentation in angiogram image sequences is presented. One of the difficulties in vessel extraction in angiogram images is the detection of ribs and spins as vessels. Therefore, to cope with this problem, we utilize motion information to segment heart area. Then a two-stage vessel extraction algorithm is utilized to extract...
A new hybrid algorithm for image segmentation based on k-mean and particle swarm optimization algorithm is proposed in the paper. K-mean clustering algorithm is a local search algorithm because it is easily to be trapped in local optimum and is sensitive to initial value effectively. On the other hand, particle swarm optimization algorithm is a global optimization algorithm. Because of taking the...
Lanna script is an archaic script not commonly used in today's world. People trying to read these archaic Lanna manuscripts have to find some form of translation help to understand what they said. Unfortunately, few people nowadays know how to read or write this language. Therefore, character recognition system must be put to use in order to translate the Lanna script to the commonly used script....
Image segmentation as the processing of partitioning a digital image into multiple segments has wide applications, such as image retrieval, medical inspection, and computer forensics. Clustering methods as one solution are applied on a single or multiple feature spaces of an image, such as color, intensity, or texture, in order to group similar pixels that share certain visual characteristics. Given...
The traditional Canny edge detection method is widely used in gray image processing. However, this traditional algorithm is unable to deal with color images and the parameters in the algorithm are difficult to be determined adaptively. In this paper, an improved Canny algorithm is proposed to detect edges in color image. The proposed algorithm is composed of the following steps: quaternion weighted...
Automatic detection of human cell is one of the most common investigation methods that may be used as part of a computer aided medical decision making system. In this paper we present an efficient algorithm, based on the cluster analysis and the vector quantization techniques for human cell image detection. First, we perform the edge detection methods to specify the desired region of any object in...
In this work, we present an automatic branch and stenoses detection method that is capable of detecting all types of plaques in Computed Tomography Angiography (CTA) modality. Our method is based on the vessel extraction algorithm we proposed in [1], and detects branches and stenoses in a very fast way. We demonstrate the performance of our branch detection method on 3 complex tubular structured synthetic...
This paper proposes a segmentation method based on information theory. The entropy of the image is regarded as the objective function to be optimized. It is maximized during the segmenting process. At first, the kernel self-organizing map is applied to cluster the input vectors of the image into groups according to their attributes, and it keeps entropy maximization meanwhile. Then the clustering...
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