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Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...
The performance of face verification systems has steadily improved over the last few years, mainly focusing on models rather than on feature processing. State-of-the-art methods often use the gray-scale face image as input. In this paper, we propose to use ...
We define multi-scale moments that are estimated locally by analyzing the image through a sliding window at multiple scales. When the analysis window satisfies a two-scale relation, we prove that these moments can be computed very efficiently using a multi ...
We present a framework for feature detection in 3-D using steerable filters. These filters can be designed to optimally respond to a particular type of feature by maximizing several Canny-like criteria. The detection process involves the analytical computa ...
We present a new technique for extracting line-type features on point-sampled geometry. Given an unstructured point cloud as input, our method first applies principal component analysis on local neighborhoods to classify points according to the likelihood ...
The performance of face verification systems has steadily improved over the last few years, mainly focusing on models rather than on feature processing. State-of-the-art methods often use the gray-scale face image as input. In this paper, we propose to use ...
We introduce local weighted geometric moments that are computed from an image within a sliding window at multiple scales. When the window function satisfies a two-scale relation, we prove that lower order moments can be computed efficiently at dyadic scale ...
Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithm ...
In this paper we present a text independent on-line writer identification system based on Gaussian Mixture Models (GMMs). This system has been developed in the context of research on Smart Meeting Rooms. The GMMs in our system are trained using two sets of ...
In this paper we present a text independent on-line writer identification system based on Gaussian Mixture Models (GMMs). This system has been developed in the context of research on Smart Meeting Rooms. The GMMs in our system are trained using two sets of ...
