We provide theoretical and algorithmic tools for nding new features which enable better classiication of new cases. Such features are proposed to be searched for as linear combinations of continuously valued conditions. Regardless of the choice of classiication algorithm itself, such an approach provides the compression of information concerning dependencies between conditional and decision fea...

This paper proposes an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified linear discriminant analysis (LDA). The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data is registered into the training data set. The holistic features of face im...

With recent advances in sensor technology and digital image processing techniques, automatic image mosaicking has received increased attention in a variety of geospatial applications, ranging from panorama generation and video surveillance to image based rendering. The geometric transformation used to link images in a mosaic is the subject of image orientation, a fundamental photogrammetric tas...

Pose estimation, which determines the position and the attitude of the camera, is a prerequisite for a variety of photogrammetric tasks such as image matching, surface reconstruction, ortho-photo generation, and object recognition. As digital imagery is becoming more popular in photogrammetric applications, features other than points are increasingly employed in pose estimation. For frame image...

In this paper we introduce two ideas for phoneme classification: First, we derive the necessary steps to integrate linear transform into the computation of reproducing kernels. This concept not restricted to phoneme classification and can be applied in a wider range of research subjects. Second, in the context of support vector machine (SVM) classification, correlation features based on MFCC-ve...

This paper discusses a trainable system to extract features for image segmentation based on non-linear mapping of local features. Supervised training methods are presented, for artificial neural diabolo networks, which produce a mapping comparable to Fisher’s linear discriminant mapping. This mapping can be used to decrease dimensionality whilst preserving class separability. It is shown that t...

The automated extraction of linear features from remotely sensed imagery has been the subject of extensive research over several decades. Recent studies show promise for extraction of feature information for applications such as updating geographic information systems (GIS). Research has been stimulated by the increase in available imagery in recent years following the launch of several airborn...

The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the paradigm of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize t...

This paper presents a multi-robot simultaneous localization and map building (SLAM) algorithm, suitable for environments which can be represented in terms of lines and segments. Linear features are described by adopting the recently introduced M-Space representation, which provides a unified framework for the parameterization of different kinds of features. The proposed solution to the cooperat...

Computer vision systems that attempt to extract cultural features from aerial imagery are often forced to interpret segmentations where the actual features are broken into numerous segments or fragments. For example, reads and road-like features are difficult to completely segment due to occlusions, poor contrast with their surroundings, and changes in surface material. Often the nature of the ...