Abstract:The paper presents a simple, original method, using supervised learning and pattern matching, for frontal-view face detection. The raw method and algorithms, their refinements and optimizations, the experimental system, and the obtained results are described incrementally. A pyramid of several simplified representations for faces, with gradual complexity and dimensionality from coarser to more detailed ones, has been defined. These representations were used as a basis for structuring and organizing the knowledge base as a kind of hash tree, as well as to optimize the developed algorithms and their processing time, by applying a sequence of filters with gradual computational complexity in cascade at detection. Appropriate metrics have been defined and used to evaluate the similarity between two finite sets of binary values, of the same dimension, with a minimum of computational effort in these filters. Various thresholds of passage were empirically chosen and adjusted. An experimental system was developed and used for learning and detection tests. Public image databases and private images have been employed, and quite promising results have been obtained. Finally, comparative parallels with some reference methods are discussed.

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ALPR based applications are more and more used today. Besides the OCR part, the vehicle registration plate detection in real world images represents the main challenge in LPR. This paper presents a simple, yet quite general, fast and effective method for license plate (LP) segmentation. It is based on the evaluation of a local contrast (high gradient) measure at the level of image blocks, binarization of the downscaled contrast map obtained with these values, and analysis of connectivity between its runs, requiring modest CPU and memory resources. It provides as output not only the locations of detected LPs, but also associated bitmaps, black on white, containing only their constituent alphanumeric characters, aligned horizontally, with no slope, slant or tilt, and free of other parasitic noise. Such black on white bitmaps are directly suitable for further OCR, the correctness and completeness of final LPR strongly depending on the quality of the bitmap provided. Extended experiments carried out on own image set, as well as on other (public) data sets, showed good performance and results of the implemented method in the vast majority of situations, even on certain difficult, poor quality images.Comparison with state-of-the-art (based on deep neural networks, and high-end GPU parallel computing), also proved average good performance on public data sets complying with the minimal requirements of our method.

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