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. Computer vision is the use of computers to extract useful meaning from images, such as those that arise from photographs, video and real-time camera feeds. Thanks to […]
This article was originally published at ARM's website. It is reprinted here with the permission of ARM. For more information, please see ARM's developer site, which includes a variety of GPU Compute, OpenCL and RenderScript tutorials. In this third and last part of this blog series we are going to extend the mixed-radix FFT OpenCL™
From drones to handheld devices, the rising demand for video cameras has made them ubiquitous, constantly driving down size and cost while pushing up resolution and overall quality. One of the main challenges in this field is stabilizing the image to generate clear, smooth footage. In this post, I would like to discuss the challenges
Digital Video Stabilization: Smooth Footage Without Expensive Mechanics Read More +
This article was originally published at ARM’s website. It is reprinted here with the permission of ARM. For more information, please see ARM’s developer site, which includes a variety of GPU Compute, OpenCL and RenderScript tutorials. Here we are for the second part of our blog series about the OpenCL™ implementation of Complex to Complex
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. In a previously published article, I offered a quick guide to writing OpenCL kernels for PowerVR Rogue GPUs; this sets the scene for what follows next: a
Heterogeneous Compute Case Study: Image Convolution Filtering Read More +
This article was originally published at ARM's website. It is reprinted here with the permission of ARM. For more information, please see ARM's developer site, which includes a variety of GPU Compute, OpenCL and RenderScript tutorials. This is the first article of three that will focus on the implementation of Fast Fourier Transform (FFT) using
Spend any amount of time researching the topic of deep learning and you'll inevitably come across the term Caffe. This convolutional neural network (CNN) framework, originally named DeCAF, was initially developed by Yangqing Jia (now a research scientist at Google), during his Ph.D. program at the University of California, Berkeley. It is now maintained by
The Caffe Deep Learning Framework: An Interview with the Core Developers Read More +
This article was originally published at Basler's website. It is reprinted here with the permission of Basler. Ever have an inescapable need for a little something sweet? The phrase "good things come in small packages" applies not just to fine confectioneries, but also to the world of industrial machine vision. Sugarcube is a term that
Time for Something Sweet? Read More +
Powerful vision processors have existed for some time now, as exemplified by supercomputers and the longstanding academic research on computer vision. What's recently changed are the "low-cost" and "energy-efficient" aspects of vision processing, along with evolutionary (and sometimes revolutionary) accuracy and other improvements in the algorithms running on the processors. Well-known image classification competitions like
New Competition Aims to Spur Energy-efficient Computer Vision Innovation 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. In a previous article about heterogeneous architectures, I identified memory bandwidth as the main bottleneck for implementing power-efficient algorithms for computer vision. Luckily, Imagination has created an
The PowerVR Imaging Framework for Android Read More +
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