This article was originally published at Basler's website. It is reprinted here with the permission of Basler. The past few years have seen the rise of a powerful generation of processing boards capable of replacing much of the work previously performed by PC-based systems. In the machine vision field, the components for industrial applications have […]
Processing Platform Concepts in Embedded Vision Read More +
This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction By September of 2013, Google’s self-driving car had completed over 500,000 miles of driving without a single accident under computer control[1]. Google’s disruptive driver- less car project was aimed to improve car safety and
Empowering Automotive Vision with TI’s Vision AccelerationPac Read More +
This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction Cars continue to become smarter and smarter, integrating new and cutting-edge technologies to make the driving experience safer and more enjoyable. With the goal of reducing roadway fatalities, enabling these new advanced driver assistance
TI Gives Sight to Vision-Enabled Automotive Technologies Read More +
This article was originally published at Basler's website. It is reprinted here with the permission of Basler. Almost everyone associates the concept of color with very personal experiences. The fact that our eyes can distinguish millions of color shades has brought us many advantages in the course of evolution – after all, thanks to our
What Role Does Color Play in Image Processing? Read More +
This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction There were 1.2 million global traffic deaths in 20101. 93 percent of traffic accidents in the US are due to human error, typically due to inattention2. ADAS (Advanced Driver Assistance Systems) applications such as
TI Vision SDK, Optimized Vision Libraries for ADAS Systems Read More +
This article was originally published as a two-part blog series at Aldec's website. It is reprinted here with the permission of Aldec. It's pretty hard to overestimate the role of heterogeneous embedded systems based on Xilinx® Zynq®-7000 All-Programmable devices in tasks like computer vision. Many consumer electronics and specialized devices are emerging to facilitate and
Leveraging the Power of VDMA Engines for Computer Vision Apps with TySOM Read More +
This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction Computer-vision algorithms used to be quite different from one another. For example, one algorithm would use Hough transforms to detect lines and circles, whereas detecting objects of interest in images would require another technique
Embedded Low-power Deep Learning with TIDL Read More +
Deep learning and other machine learning techniques have rapidly become a transformative force in computer vision. Compared to conventional computer vision techniques, machine learning algorithms deliver superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence,
Data Sets for Machine Learning Model Training Read More +
DNNs (deep neural networks) have transformed the field of computer vision, delivering superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence, which had been solved quite well with conventional techniques, are now finding even better
Implementing Vision with Deep Learning in Resource-constrained Designs Read More +
This article was originally published at Synopsys' website. It is reprinted here with the permission of Synopsys. Examples of applications abound where high-performance, low-power embedded vision processors are used: a mobile phone using face recognition to identify a user, an augmented or mixed reality headset identifying your hands and the layout of your living room
Implementing High-performance Deep Learning Without Breaking Your Power Budget Read More +
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