This site uses cookies to deliver our services and to ensure you get the best experience. By continuing to use this site, you consent to our use of cookies and acknowledge that you have read and understand our Privacy Policy, Cookie Policy, and Terms
This paper presents a novel approach for detection and segmentation of generic shapes in cluttered images. The underlying assumption is that generic objects that are man made, fre...
In this paper we present a novel framework for generic object class detection by integrating Kernel PCA with AdaBoost. The classifier obtained in this way is invariant to changes...
— The automatic extraction and analysis of visual information is becoming generalised. The first step in this processing chain is usually separating or segmenting the captured v...
Abstract. We present a novel statistical-model-based segmentation algorithm that addresses a recurrent problem in appearance model fitting and model-based segmentation: the "s...
In recent years Kernel Principal Component Analysis (Kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly impor...