In this work we present a robust detection method in outdoor scenes under cast shadows using color based invariant gradients in combination with HoG local features. The method achieves good detection rates in urban scene classification and person detection outperforming traditional methods based on intensity gradient detectors which are sensible to illumination variations but not to cast shadows. The method uses color based invariant gradients that emphasize material changes and extract relevant and invariant features for detection while neglecting shadow contours. This method allows to train and detect objects and scenes independently of scene illumination, cast and self shadows. Moreover, it allows to do training in one shot, that is, when the robot visits the scene for the first time.

Abstract. In this work a new robust color and contour based object detection method in images with varying shadows is presented. The method relies on a physics-based contour detector that emphasizes material changes and a contourbased boosted classifier. The method has been tested in a sequence of outdoor color images presenting varying shadows using two classifiers, one that learnt contour object features from a simple gradient detector, and another that learnt from the photometric invariant contour detector. It is shown that the detection performance of the classifier trained with the photometric invariant detector is significantly higher than that of the classifier trained with gradient detector.

In this article, a method is presented for automatic scaling of the F-layer from ionograms based on an image processing technique for the extraction of curvilinear structures. The algorithm obtains the ordinary and extraordinary traces and determines the F2 critical frequency. The performance was tested using a wide data set of ionograms recorded by the Advanced Ionospheric Sounder/Istituto Nazionale di Geofisica e Vulcanologia (AIS/INGV) ionosonde located at Universidad Nacional de Tucumán, Tucuman, Argentina, and the results are compared with manual scaling and also with Autoscala method.Results from these tests show that the method is feasible and can be the seed for the development of a robust automated scaling system.

The output from a color imaging sensor, or apparent

Abstract