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This paper presents an accurate object segmentation method using novel active shape and appearance models that evolve according to the output of a support vector machine as well as traditional appearance features at shape landmarks. The method consists of two main processes including the building of the shape and appearance models and support vector machine (SVM) classifier, and the segmentation of...
Home electrical power monitoring plays an important role in reducing energy usage, and non-intrusive appliance load monitoring (NIALM) techniques are the most effective approach for estimating the electrical power consumption of individual appliances. Power load events detection is one of the most important steps in these techniques. This paper presents an automatic power load event detection method:...
Monitoring household electrical consumption by employing appropriate techniques is of great significance to sustainable development of human society. This paper proposes one approach of nonintrusive appliance load monitoring (NIALM) for electrical consumption managing. This approach can automatically monitor the house power consumption of individual devices. It employs multiple-class support vector...
This paper presents an automatic liver parenchyma segmentation algorithm that can segment liver in abdominal CT images. There are three major steps in the proposed approach. Firstly, a texture analysis is applied to input abdominal CT images to extract pixel level features. In this step, wavelet coefficients are used as texture descriptors. Secondly, support vector machines (SVMs) are implemented...
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