|
"Challenges in Object Detection on Embedded Devices," a Presentation from CEVA
As more products ship with integrated cameras, says Adar Paz, Imaging and Computer Vision Team Leader at CEVA, there is an increased potential for computer vision (CV) to enable innovation. For instance, CV can tackle the "scene understanding" problem by first figuring out what the various objects in the scene are. Such "object detection" capability holds big promise for embedded devices in mobile, automotive, and surveillance markets. However, performing real-time object detection while meeting a strict power budget remains a challenge on existing processors. In this session, Paz analyzes the trade-offs of various object detection, feature extraction and feature matching algorithms, their suitability for embedded vision processing, and recommends methods for efficient implementation in a power- and budget-constrained embedded device.
Basler's Thies Moeller Explains Image Quality
Image quality is a complex issue that goes far beyond brightness and sharpness. There are a number of other factors that contribute significantly to the image quality a camera delivers. In Basler's latest Vision Campus video, expert Thies Moeller uses practical examples to explain the effects of image noise, quantum efficiency, dynamic range and the like on an image.
More Videos
|
|
Optimizing Computer Vision Applications Using OpenCL and GPUs
The substantial parallel processing resources available in modern graphics processors makes them a natural choice for implementing vision-processing functions. The rapidly maturing OpenCL framework enables the rapid and efficient development of programs that execute across GPUs and other heterogeneous processing elements within a system. In this collaborative article, Alliance member companies AMD, ARM, and Intel review parallelism in computer vision applications, provide an overview the OpenCL programming language, and then dive into optimization techniques for computer vision applications based on OpenCL and leveraging GPU acceleration. More
Can FPGAs Challenge GPUs as a Platform for Deep Learning?
Over the past several years, GPUs have become the de facto standard for implementing deep learning algorithms in computer vision and other applications, notes Anand Joshi, Principal Analyst at Tractica. GPUs offer a large number of processing elements, a stable and expanding ecosystem, support for standards such as OpenCL, and a wide range of intellectual property to develop applications rapidly. However, as the industry matures, FPGAs are now starting to emerge as credible competition to GPUs for implementing deep learning algorithms. More
More Articles
|
|
Deep Learning for Vision Using CNNs and Caffe: A Hands-on Tutorial: September 22, 2016, Cambridge, Massachusetts
IEEE International Conference on Image Processing (ICIP): September 25-28, 2016, Phoenix, Arizona
SoftKinetic DepthSense Workshop: September 26-27, 2016, San Jose, California
Embedded Vision Summit: May 1-3, 2017, Santa Clara, California
More Events
|
