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For any autonomous system it is very important to acquire the knowledge of the surrounding environment. Images and videos acquired by the vision based sensors can provide meaningful information about the environment, which can be very useful for the navigation of autonomous system like mobile robots. To extract road information from image frames for navigation purpose they have to be classified. Classification...
Image classification is an important task for many aspects of global change studies and environmental applications. This paper emphasizes on the analysis and usage of different advanced image classification techniques like Cloud Basis Functions (CBFs) Neural Networks, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for object based classification to get better accuracy. For comparison,...
Background subtraction in highly dynamic scenes has been a critical challenge for traditional pixel-wise background models which perform poorly when the background has dynamic textures. In this paper, we consider background modelling in a spatial perspective and make an attempt to exploit more information from the outputs of pixel-wise model. We propose a background subtraction scheme using adaptive...
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...
Moving objects often contain the most important information in surveillance videos. The detection and segmentation of moving objects are the basis for object recognition and intrusion analysis. Gaussian mixture model (GMM) is an effective way to extract moving objects from a video background. However, the conventional mixture Gaussian method suffers from false motion detection in complex backgrounds...
Multispectral images provide detailed data with information in both the spatial and spectral domains. Many clustering methods for multispectral images are based on a per-pixel classification, while uses only spectral information and ignores spatial information. In this work, a new clustering algorithm for multispectral images, based on both spectral and spatial information, is presented. This algorithm...
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