Embedded vision applications deal with a lot of data; a single 1080p60 (1920×1080 pixel per frame, 60 frames per second) 24-bit color video stream requires nearly a half GByte of storage per second, along with nearly 3 Gbps of bandwidth, and 8-bit alpha (transparency) or 3-D depth data further amplifies the payload by 33% in […]
Fundamentals of coolSRAM-1T Memory IP Read More +
VR (virtual reality) systems are beginning to incorporate practical computer vision techniques, dramatically improving the user experience as well as reducing system cost. This article provides an overview of embedded vision opportunities in virtual reality systems, such as environmental mapping, gesture interface, and eye tracking, along with implementation details. It also introduces an industry alliance
Vision Processing Opportunities in Virtual Reality Read More +
UAVs (unmanned aerial vehicles), commonly known as drones, are a rapidly growing market and increasingly leverage embedded vision technology for digital video stabilization, autonomous navigation, and terrain analysis, among other functions. This article reviews drone market sizes and trends, and then discusses embedded vision technology applications in drones, such as image quality optimization, autonomous navigation,
Vision Processing Opportunities in Drones Read More +
This article was originally published at PathPartner Technology's website. It is reprinted here with the permission of PathPartner Technology. Object detection/classification is a supervised learning process in machine vision to recognize patterns or objects from images or other data. It is a major component in Advanced Driver Assistance Systems (ADAS), for example, as it is
Embedded Vision Application: A Design Approach for Real Time Classifiers Read More +
FPGAs have proven to be a compelling solution for solving deep learning problems, particularly when applied to image recognition. The advantage of using FPGAs for deep learning is primarily derived from several factors: their massively parallel architectures, efficient DSP resources, and large amounts of on-chip memory and bandwidth. An illustration of a typical FPGA architecture
FPGAs for Deep Learning-based Vision Processing Read More +
Neural networks enable the identification of objects in still and video images with impressive speed and accuracy after an initial training phase. This so-called "deep learning" has been enabled by the combination of the evolution of traditional neural network techniques, with one latest-incarnation example known as a CNN (convolutional neural network), by the steadily increasing
Deep Learning for Object Recognition: DSP and Specialized Processor Optimizations Read More +
This article was originally published by Embedded Vision Alliance consultant Dave Tokic. It is reprinted here with Tokic's permission. It seems to me that hardly a day goes by without some mention of self-driving cars (mostly good, some tragic), drones that follow you to record your ski run, augmented and virtual reality goggles that immerse
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 article, we briefly review parallelism in computer vision applications,
Optimizing Computer Vision Applications Using OpenCL and GPUs Read More +
This article was originally published at Imagination Technologies' website. It is reprinted here with the permission of Imagination Technologies. It was clear last week at the annual Embedded Vision Summit in Santa Clara that the time of computer vision and deep learning on mobile had finally arrived. Interest in the area is growing noticeably –
Deep Learning on Mobile Devices at the Embedded Vision Summit 2016 Read More +
This article was originally published at CEVA's website. It is reprinted here with the permission of CEVA. Demand is on the rise for video cameras on moving platforms. Smartphones, wearable devices, cars, and drones are all increasingly employing video cameras with higher resolution and higher frames rates. In all of these cases, the captured video
Video Stabilization Using Computer Vision: Tips and Insights From CEVA’s Experts Read More +
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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