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Sharing computational and mathematics software solutions Menu and widgets In this video, I will explain how to detect optical markers (checkerboards and concentric circles) in outdoor environments using OpenCLTemplate OMR framework. This sample shows how to use images and videos. My screen shows an image obtained with lighting conditions that are not controlled. What the code allows you to do is detect the checkerboard structure, with all checkerboard centers, and targets, with their colors and centers. – Include OpenCLTemplate and Cloo in references – Initialize SuperPixel clas* and pas* data using SetBmp for – An*lyze concentric regions and checkerboards – Detection distance – CAM quality For more details about the technique: please visit the text of my Thesis: together to spread and strengthen industrial and scientific use of Khronos’ standards, most notably OpenCL and OpenGL. On March 12th a talk was held with students and researchers of the Computer Vision laboratory – VeRLAB to present benefits of using Machine learning and computer vision have become a reality in people’s daily life through smart wearables (smart glas*es, watches phones), vehicles capable of recognizing traffic signs, biometric systems and many others. These technologies increase safety and comfort when using such machines and are currently object of active research. However, development of better algorithms and the possibility of executing them in mobile devices under acceptable time frames and lower energy consumption still remain as open challenges. This presentation covers topics on how GPU parallel processing allows performance and energy efficiency increases when executing algorithms whose inputs are images. Accelerations up to 800x may be obtained by intelligent use of appropriate parallel algorithms suited to SIMD architectures, explicit cache management and use of texture samplers. We have had some requests from users to post a simple OpenCL OpenGL Interoperation example using vertexes and we decided to post a fresh