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The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the...
Many skin detection approaches have been proposed in the image analysis literature. Some are simple and static; the others are dynamic and rely on complex machine learning algorithms and training data. Generally the simple approaches are preferred. We hypothesize that the developers' choice for the simple approaches are due to the reasonable quality of results and ease of implementation, since the...
Skin color segmentation is important for several image processing, and computer vision applications. But, the accuracy of a color-based skin detection method is affected by the presence of some skin-like colors in the background regions. So, probabilistic approaches are more suitable for the skin detection as compared to hard decision-based approaches. A Skin Probability Map (SPM) of an image provides...
Saliency detection is detecting the more attracted regions in an image. Like a loud noise in a quite environment. Saliency is the contrastic difference between the visually attracted items and their neighbourhood. Detecting such attracted areas in the image is termed to be saliency detection. Salient region detection can be broadly classified into contrast based method, learning based, rarity based...
Palm-leaf manuscripts have enormous information, very much essential to day-to-day life. Due to climatic changes and aging factors these manuscripts have started degrading. To preserve the information present on the palm-leaves, digital images of each leaves have been taken up and stored. These images can be viewed as and when required. However, the storage volume required for each leaf is tremendously...
In this paper, a region-based moving object detection based on hu moments is concerned. Firstly, the feasibility analysis of hu moments in moving object detection is performed. Then a model of hu moments in moving object detection is presented and the performances are evaluated. The experiment results show that the hu moments can eliminate the large amount of noise caused by traditional Single Gaussian...
Abstract: In this paper a segmentation algorithm is used to detect moving objects and to integrate it to a supervision and surveillance systems, in a parking lot, as a first step. One of the way to moving detection in image sequences is the moving object segmentation by background model, very well-known technique that it permit to know what objects are moving. This can be employed, in the second stage,...
This paper presents an efficient image exploration scheme for the unshaped object using semantic modelling. The local regions of an image have been classified with respect to the frequency of occurrences. The semantic concept is evaluated using RGB histogram dissimilarity factor, overall dissimilarity factor and regional dissimilarity factor. The dissimilarities determine the local concept with accuracy...
Segmentation of the foreground objects is the primary step in many video analysis applications. The accuracy of the segmentation is dependent on an accurate background image that is used for background subtraction. The Teknomo-Fernandez (TF) algorithm is an efficient algorithm that quickly generates a good background image. A previous study showed the extendibility of the TF algorithm to higher number...
In this paper, a reliable pixel-based foreground-background segmentation technique for detecting object(s) of interest (OOI) from video sequence captured by a fixed camera is proposed. OOIs, used for further tracking or positioning applications, should be detected accurately from those moving (or still) objects even under variable illumination and the corresponding background model need to update...
In this paper, we present a background subtraction (BS) technique based on the fusion of thermal and visible imagery using an adaptive Gaussian mixture models (GMM). We investigate how to effectively combine thermal and visible information to optimize the segmentation accuracy. Pixel-level fusion strategies combining different color spaces and image representations are addressed. The standard GMM...
Tree models for human pose estimation have been prevailed in the last decade, which are effective in human pose estimation. This paper aims to incorporate the appearance symmetry of human limb parts into tree model and address the problem of the wrong detection of human limbs. For a pair of symmetrical limbs, such as for legs and arms, their appearances are similar that can use a distance to represent...
This paper addresses the difficult problem of finding dense correspondence across images with large appearance variations. Our method uses multiple feature samples at each pixel to deal with the appearance variations based on our observation that pre-defined single feature sample provides poor results in nearest neighbor matching. We apply the idea in a flow-based matching framework and utilize the...
Modelling 3D stratum surface is difficult because the number of exploratory holes is usually too small. As an attempt to solve this problem, many interpolation methods were proposed to estimate the surface between the holes. In this paper, we propose an approach to interpolate 3D spatial points and model stratum surface. The curvature of the original surface mesh is embedded into kriging interpolation...
In this paper, we examine the suitability of correlogram for background subtraction, as a step towards moving object detection. Correlogram captures inter-pixel relationships in a region and is seen to be effective for modelling dynamic backgrounds. We propose herein a novel feature, termed fuzzy correlogram, composed by applying fuzzy c-means algorithm on correlogram. Fuzzy correlogram greatly reduces...
Many existing background subtraction approaches model background color only and detect foreground as outliers, and hence may confuse background changes or noises with true foreground. We present a novel algorithm that utilizes motion cues computed from an optical flow algorithm. The additional motion information allows aligning moving foreground objects over time so that models can be built for foreground...
In this paper, we propose a new Mean-shift algorithm to tackle some tracking difficulties, such as background clutter and partial occlusion. First, we compare all Mean-shift-like tracking algorithms, and indicate that the main difference among them is weight calculation. Then, a new fusion strategy is proposed to unify all weight calculation methods into a framework. Based on this framework, we propose...
Image saliency detection is very useful in many computer vision tasks while it still remains a challenging problem. In this paper, we propose a new computational saliency detection model which is implemented with a coarse to fine strategy under the Bayesian framework. First, saliency points are applied to get a coarse location of the saliency region. And then, based on the rough region, we compute...
Visual saliency is an extremely hot topic in image and video processing. Our paper addresses a novel method for saliency detection, which conveys the photographers' idea of a scene. It consists of analyzing the photo elements like color, depth-of-field (DOF) and composition. We use the color spatial distribution feature to calculate the initial saliency. A simple but efficient classifier is trained...
Background subtraction is a traditional method for detecting objects in stationary background. However, this traditional method is difficult to detect objects accurately in the real world, because the background is usually cluttered and not completely static. In this paper, we propose an object detection approach using Ant Colony System (ACS) in a MAP-MRF framework. For object segmentation, a MAP-MRF...
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