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Embedded Vision Insights: July 7, 2016 Edition

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"Challenges in Object Detection on Embedded Devices," a Presentation from CEVACEVA
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 QualityBasler
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,...