This article was originally published at Imagination Technologies' website, where it is one of a series of articles. It is reprinted here with the permission of Imagination Technologies. Writing and optimizing code for heterogeneous computing can be difficult, especially if you are starting from scratch. Imagination has set up a new page where developers can […]
The PowerVR Imaging Framework Camera Demo Read More +
This article was originally published at Imagination Technologies' website, where it is one of a series of articles. It is reprinted here with the permission of Imagination Technologies. Imagination’s R&D group has developed a face detection algorithm, which is based on a classifier cascade and is optimized to run on mobile devices comprising a CPU
Deep Dive: Implementing Computer Vision with PowerVR (Part 3: OpenCL Face Detection) Read More +
This article was originally published at PathPartner Technology's website. It is reprinted here with the permission of PathPartner Technology. If your realtime image processing applications such as a driver monitoring system are dependent on OpenCV, you have to develop an OpenCV build environment on the target board. This article will guide you through the steps
This column was originally published at Vision Systems Design's website. It is reprinted here with the permission of PennWell. For most of its history, computer vision was a topic of academic research, gaining its first sizable commercial success in factory automation applications, where it has become an essential technology. Nevertheless, vision has remained a niche
Computer Vision Evolves Towards Ubiquity Read More +
This article was originally published by Embedded Vision Alliance consultant Dave Tokic. It is reprinted here with Tokic's permission. How one non-profit is helping the poorest by enabling cutting edge image search and self-driving cars 1.2 billion people (22 percent) live in extreme poverty on less than $1.25 a day worldwide and about 50% of
Taking on Poverty with Jobs Created by Machine Learning and Computer Vision Read More +
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 +
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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