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.
– Download framework from http://www.cmsoft.com.br/download/OpenCLTemplate.zip
– Include OpenCLTemplate and Cloo in references
– Initialize SuperPixel class and pass data using SetBmp for performance
– Analyze concentric regions and checkerboards
– Detection distance – CAM quality
– Download this example from http://www.cmsoft.com.br/download/RobustOMRDemo.zip (Visual Studio 2017)
For more details about the technique: please visit the text of my Thesis:
The Department of Computer Science of Federal University of Minas Gerais – DCC/UFMG and Khronos Chapter Brazil have been working 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 GPGPU through OpenCL to accelerate CV algorithms.
Machine learning and computer vision have become a reality in people’s daily life through smart wearables (smart glasses, 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.
One very important issue with OpenCL code at the moment is that it needs to either be compiled at runtime by vendor’s compilers or be precompiled but also restricted to the hardware it has been precompiled to. This is a problem because programmers cannot protect sensitive parallel code unless it is delivered precompiled to each specific platform, a very tedious task that completely challenges the goals of having an open specification.
Khronos’ SPIR (Standard Portable Intermediate Representation) specification is an important step towards protecting sensitive source code while still maintaining cross-platform capabilities.
This may very well be the last step that gaming industry and multimedia processing companies were waiting to fully incorporate heterogeneous computing into their applications.