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Background reconstruction is very important in many video-based tracking systems. The principle difficulties are the quality and velocity of reconstruction. To cope with these problems, a novel method is proposed. Firstly, the sequence images are decomposed into low frequency sub-images using DWT (discrete wavelet transform). Then, the improved grayscale classification is introduced to reconstruct...
A Myocardial Ischemia Detection Algorithm (MIDA) is presented that analyses echocardiography sequences automatically in order to detect the presence of heart muscle damage. MIDA involves an image enhancement, fuzzy multi resolution edge detection to obtain the heart wall boundaries, composite motion image creation using the heart wall boundaries, followed by statistical pattern recognition and classification...
This paper proposes a system for people trajectory shape analysis by exploiting a statistical approach which accounts for sequences of both directional (the directions of the trajectory) and linear (the speeds) data. A semi-directional distribution (AWLG - Approximated Wrapped and Linear Gaussian) is used with a mixture to find main directions and speeds. A variational version of the mutual information...
Mass segmentation plays an important role in many computer-aided diagnosis (CAD) system. It is usually used as the previous step of mass classification. In this paper, we propose one novel scheme for segmentation of breast mass in digitized mammograms, which is based on gradient vector flow (GVF) snake and multi-scale analysis using Gaussian pyramid. In the proposed method, mammogram is decomposed...
Motion analysis is an important component of surveillance, video annotation and many other applications. Current work focuses on the tracking of moving entities, the representation of their actions and the classification of sequences. A wide range of methods are available for the characterization and analysis of human activity. This work presents an original approach for the detailed characterization...
We propose an activity recognition algorithm that utilizes a unified spatial-frequency model of motion to recognize large-scale differences in action using global statistics, and subsequently distinguishes between motions with similar global statistics by spatially localizing the moving objects. We model the Fourier transforms of translating rigid objects in a video, since the Fourier domain inherently...
A single-mode background model based on blob analysis is proposed to segment foreground from image sequences in complex environment. Firstly, the symmetric difference is used to extract a rough moving object. Secondly, blob analysis is utilized to update background model .Finally, classification strategy (block-level and frame-level) is used to extract foreground accurately and avoid the affect of...
With the increase of vehicles, the work load and the difficulties of the traffic management and road tolling become harder and harder, and day by day, so the automatic recognition of automobile type has very important real value in the traffic management system and road auto-tolling system. An approach is presented to detect and classify the moving vehicle in static scenes, which is based on GVF-Snake...
In this paper we propose a solution to the problem of body part segmentation in noisy silhouette images. In developing this solution we revisit the issue of insufficient labeled training data, by investigating how synthetically generated data can be used to train general statistical models for shape classification. In our proposed solution we produce sequences of synthetically generated images, using...
We address the problem of performing decision tasks and, in particular, classification and recognition in the space of dynamical models in order to compare time series of data. Motivated by the application of recognition of human motion in image sequences, we consider a class of models that include linear dynamics, both stable and marginally stable (periodic), both minimum and nonminimum phases, driven...
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