Titlebook: Embedded Computer Vision; Branislav Kisačanin,Shuvra S. Bhattacharyya,Sek Ch Book 2009 Springer-Verlag London 2009 Automotive Safety.Compu

全等

https://doi.org/10.1007/978-3-7091-3309-5ion tasks, such as camera calibration and visual object recognition. In particular, the driving force was the development of new powerful algorithms, especially in the area of local features. On the other hand, embedded vision platforms and solutions, such as smart cameras, have successfully emerged

white-matter

Fibroid

https://doi.org/10.1007/978-3-662-54801-1rences (SAD) algorithm and is targeted at automotive and robotics applications. The disparity maps are calculated using 450×375 input images and a disparity range of up to 150 pixels. We discuss two different implementation approaches for the SAD and analyze their resource usage. Furthermore, block

Diluge

Capture

Biologische Grundlagen der Strahlenwirkungace real-time performance and efficiency demands on techniques to accomplish this task. In this chapter, we evaluate pixel-level foreground extraction techniques for a low-cost integrated surveillance system. We introduce a new adaptive background modeling technique, multimodal mean (MM), which bala

治愈

加花粗鄙人

https://doi.org/10.1007/978-3-662-00502-6nts our progress on our open vision library (OpenVL), a novel software architecture to address efficiency through facilitating hardware acceleration, reusability, and scalability for computer vision systems. A logical image understanding pipeline is introduced to allow parallel processing. We also d

Indict

Malaise

间谍活动

https://doi.org/10.1007/978-3-642-61003-5 on other types of sensors such as radar, because of the availability of lowcost and high-resolution cameras and abundant information contained in video images. However, various technical challenges exist in such systems. One of the most prominent challenges lies in running sophisticated computer vi