Stereo Matching through Squeeze Deep Neural Networks

Visual depth recognition through Stereo Matching is an active field of research due to the numerous applications in robotics, autonomous driving, user interfaces, etc. Multiple techniques have been developed in the last two decades to achieve accurate disparity maps in short time. With the arrival of Deep Leaning architectures, different fields of Artificial Vision, but mainly on image recognition, have achieved a great progress due to their easier training capabilities and reduction of parameters. This type of networks brought the attention of the Stereo Matching researchers who successfully applied the same concept to generate disparity maps. Even though multiple approaches have been taken towards the minimization of the execution time and errors in the results, most of the time the number of parameters of the networks is neither taken into consideration nor optimized. Inspired on the Squeeze-Nets developed for image recognition, we developed a Stereo Matching Squeeze neural network architecture capable of providing disparity maps with a highly reduced network size without a significant impact on quality and execution time compared with state of the art architectures. In addition, with the purpose of improving the quality of the solution and get solutions closer to real time, an extra refinement module is proposed and several tests are performed using different input size reductions

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

XXV Congreso Argentino de Ciencias de la Computación - CACIC 2019: libro de actas

Trabajos presentados en el XXV Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Río Cuarto los días 14 al 18 de octubre de 2019 organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y Facultad de Ciencias Exactas, Físico-Químicas y Naturales - Universidad Nacional de Río CuartoRed de Universidades con Carreras en Informátic