Robust color contour object detection invariant to shadows

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 contour-based boosted classifier. The method has been tested in a sequence of outdoor color images presenting varying shadows using two classifiers, one that learns contour object features from a simple gradient detector, and another that learns 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.Peer Reviewe

Research at the learning and vision mobile robotics group 2004-2005

Spanish Congress on Informatics (CEDI), 2005, Granada (España)This article presents the current trends on wheeled mobile robotics being pursued at the Learning and Vision Mobile Robotics Group (IRI). It includes an overview of recent results produced in our group in a wide range of areas, including robot localization, color invariance, segmentation, tracking, audio processing and object learning and recognition.This work was supported by projects: 'Supervised learning of industrial scenes by means of an active vision equipped mobile robot.' (J-00063), 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe

Discriminant and invariant color model for tracking under abrupt illumination changes

Trabajo presentado al ICPR 2010 celebrado en Estambul (Turquía) del 23 al 26 de agosto.The output from a color imaging sensor, or apparent color, can change considerably due to illumination conditions and scene geometry changes. In this work we take into account the dependence of apparent color with illumination an attempt to find appropriate color models for the typical conditions found in outdoor settings. We evaluate three color based trackers, one based on hue, another based on an intrinsic image representation and the last one based on a proposed combination of a chromaticity model with a physically reasoned adaptation of the target model. The evaluation is done on outdoor sequences with challenging illumination conditions, and shows that the proposed method improves the average track completeness by over 22% over the hue-based tracker and the closeness of track by over 7% over the tracker based on the intrinsic image representation.This work was supported by projects: 'CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision' (V-00069), 'Robotica ubicua para entornos urbanos' (J-01225).Peer Reviewe

Comparative Analysis for Detecting Objects Under Cast Shadows in Video Images

Trabajo presentado a la ICPR 2010 celebrada en Estambul del 23 al 26 de agosto.Cast shadows add additional difficulties on detecting objects because they locally modify image intensity and color. Shadows may appear or disappear in an image when the object, the camera, or both are free to move through a scene. This work evaluates the performance of an object detection method based on boosted HOG paired with three different image representations in outdoor video sequences. We follow and extend on the taxonomy from van de Sande with considerations on the constraints assumed by each descriptor on the spatial variation of the illumination. We show that the intrinsic image representation consistently gives the best results. This proves the usefulness of this representation for object detection in varying illumination conditions, and supports the idea that in practice local assumptions in the descriptors can be violated.This work has been partially funded by the Spanish Ministry of Science and Innovation under projects UbROB DPI2007-61452, and MIPRCV Consolider Ingenio 2010 CSD2007-00018. The first author is funded by the Technical University of Catalonia (UPC).Peer reviewe

Combining color-based invariant gradient detector with HoG descriptors for robust image detection in scenes under cast shadows

Trabajo presentado al ICRA 2009 celebrado en Kobe (Japón) del 12 al 17 de mayo.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.This work was supported by projects: 'Ubiquitous networking robotics in urban settings' (E-00938), 'CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision' (V-00069), 'Robotica ubicua para entornos urbanos' (J-01225), 'Percepción y acción ante incertidumbre' (4803). This research was partially supported by the Spanish Minist ry of Innovation and Science under projects Consolider Ingenio 2010 CSD2007-00018, and projects DPI 2007-614452 and DPI 2008-06022; by t he URUS project IST-045062 of the European Union; by the Generalita t of Catalonia’s Department of Innovation, University and Industry and the European Social Fund to JS; and by the Technical University of Catalonia (UPC) to MV.Peer Reviewe

Robust color contour object detection invariant to shadows

Presentado al 12th Iberoamerican Congress on Pattern Recognition (CIARP-2007) celebrado en Valparaiso (Chile) del 13 al 16 de noviembre.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 contour-based boosted classifier. The method has been tested in a sequence of outdoor color images presenting varying shadows using two classifiers, one that learns contour object features from a simple gradient detector, and another that learns 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.This work was supported by projects: 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929), 'Ubiquitous networking robotics in urban settings' (E-00938).This work is supported in part by the Spanish Ministry of Education and Science under project DPI 2004-5414 and the EU URUS project FP6-IST- 045062 to JS, MV, JAC and AS; by the Generealitat of Catalonia’s Department of Education and Universities and the European Social Fund to JS; and by the Technical University of Catalonia to MV. JAC is a Ramón y Cajal Postdoctoral Fellow.Peer Reviewe

Push-pull directly modulated laser diodes

SIGLEAvailable from British Library Document Supply Centre-DSC:D064231 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Discriminant and invariant color model for tracking under abrupt illumination changes

The output from a color imaging sensor, or apparent color, can change considerably due to illumination conditions and scene geometry changes. In this work we take into account the dependence of apparent color with illumination an attempt to find appropriate color models for the typical conditions found in outdoor settings. We evaluate three color based trackers, one based on hue, another based on an intrinsic image representation and the last one based on a proposed combination of a chromaticity model with a physically reasoned adaptation of the target model. The evaluation is done on outdoor sequences with challenging illumination conditions, and shows that the proposed method improves the average track completeness by over 22% over the hue-based tracker and the closeness of track by over 7% over the tracker based on the intrinsic image representation