For automotive assistance systems, on-road vehicle detection is a key challenge to forward collision warning. Along with detecting existence, determining a vehicle's orientation plays an important role in correctly predicting maneuvers. In this paper, an approach to remotely estimate vehicle orientations from monocular images is presented. The proposed system operates on a per frame basis and does not require any depth cues. Orientation estimation is performed by analyzing the position of the vehicle's rear section relative to the overall vehicle outline. Both position types are determined using a newly devised tree-structured classifier. Based on the cascaded structure by Viola and Jones, the pro posed classifier adapts itself to the problem's structure, dividing the overall problem into parts that require fewer weak learners to solve. To find partitions that simplify the classification task, a quality criterion measuring class separability is optimized using the Simulated Annealing algorithm. To further increase processing speed, the number of tree nodes to be traversed is drastically reduced by a two-staged boosting procedure, training a classifier that decides which branch to take. Experiments show the relevance and effectiveness of the proposed concepts.

The authors deal with the recognition of writing style (whether a data field is hand or machine printed) in the context of form reading applications. Due to the form reader's hardware restrictions, the approach had to be based only on the knowledge of the surrounding rectangles of the black connected components of the data field. Different statistical classifiers were developed which were adapted to different feature vectors calculated separately for each data field. The output of these classifiers was combined, allowing a much higher performance than each single classifier. The combination was carried out by another polynomial (statistical) classifier using the estimations, not decisions, of these classifiers as the new feature vector. The improvement by combination was significant. Meanwhile the approach has proven its practical viability while running successfully in commercially distributed form readers.>

The authors present a conceptual framework for solving the task of document analysis, which, in essence, consists in the conversion of the document's pixel representation into an equivalent knowledge network representation holding the document's content and layout. Starting on the pixel level, the formation of elementary geometric objects on which layout analysis as well as the definition of character objects is based is described. Character recognition accomplishes the mapping from geometric object to character meaning in ASCII representation. On the next level of abstraction words are formed and verified by contextual processing. Modeled knowledge about complete documents and about how their constituents are related to the application forms the highest level of abstraction. The various problems arising at each stage are discussed. The dependencies between the different levels are exemplified and technical solutions put forward.>

A method of representing connected components in multivalued images is proposed, and an efficient, sequential, one-pass algorithm for generating the border-line chain code for each component is introduced. Simultaneously, the algorithm produces for each component a list of all neighboring components that are encountered during a walk around the border line, thus providing full topological information. The number of different pixel values does not affect performance.>