11,125 research outputs found
Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification
[Abriged] Astronomical Wide Field Imaging performed with new large format CCD
detectors poses data reduction problems of unprecedented scale which are
difficult to deal with traditional interactive tools. We present here NExt
(Neural Extractor): a new Neural Network (NN) based package capable to detect
objects and to perform both deblending and star/galaxy classification in an
automatic way. Traditionally, in astronomical images, objects are first
discriminated from the noisy background by searching for sets of connected
pixels having brightnesses above a given threshold and then they are classified
as stars or as galaxies through diagnostic diagrams having variables choosen
accordingly to the astronomer's taste and experience. In the extraction step,
assuming that images are well sampled, NExt requires only the simplest a priori
definition of "what an object is" (id est, it keeps all structures composed by
more than one pixels) and performs the detection via an unsupervised NN
approaching detection as a clustering problem which has been thoroughly studied
in the artificial intelligence literature. In order to obtain an objective and
reliable classification, instead of using an arbitrarily defined set of
features, we use a NN to select the most significant features among the large
number of measured ones, and then we use their selected features to perform the
classification task. In order to optimise the performances of the system we
implemented and tested several different models of NN. The comparison of the
NExt performances with those of the best detection and classification package
known to the authors (SExtractor) shows that NExt is at least as effective as
the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at
http://www.na.astro.it/~andreon/listapub.htm
Increasing the Accuracy of Detection and Recognition in Visual Surveillance
Visual surveillance has two major steps of detecting and recognizing moving objects. In the detection stage, moving objects must be detected as quickly and accurately as possible and the influence of environmental light changes and waving trees should be reduced. In this research a block-based method is introduced in HSV color space in the detection stage. This method did not scan all the pixels of the frame and acted well in situations like sudden light changes. A powerful pattern recognition system should have powerful feature extraction and classification. Note that, feature extraction in gray level or RGB color space has problems such as environmental light changes, adding noise or changes in contrast and sharpness of images, which lead to weak classification. So the HSV color space was used. Here, Block-based Improved Center Symmetric Local Binary Pattern is introduced for feature extraction. In each component of the HSV color space, information of highlight areas in the image such as edge, shape and some texture was extracted. The histogram was calculated in two-level blocks and Support Vector Machine was used for classifying into vehicles, motorcycles and pedestrians. The obtained results in increasing the detection accuracy and decreasing the spent time were satisfactory.DOI:http://dx.doi.org/10.11591/ijece.v2i3.33
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