2 edition of Parallel processors for low-level image processing and motion detection found in the catalog.
Parallel processors for low-level image processing and motion detection
|Statement||B. Taheri ; supervised by R.C.Waterfall.|
|Contributions||Waterfall, R.C., Electrical Engineering and Electronics.|
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Simple low-level image processing tasks (early-vision), a smart sensor integrates analogue and/or d igital processing circuits in the pixel  or at the edge of the pixels array .
I am trying to implement an image analysis algorithm using parallel processing techniques. As usual, the implementation is comprised of a. Kong et al.
obtained a speed up equal to times for an image size ×and it is an implementation of the full algorithm of face detection.
As against, we just work on the cascade detection function and we found for the same image size a speed up equal to 16 times, so we can conclude that the results are by: Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction.
However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data.
In this paper, a novel resolution free Cited by: 2. Alan C. Bovik, in Handbook of Image and Video Processing (Second Edition), Digital Video.
A significant portion of this Handbook is devoted to the topic of digital video processing. In recent years, hardware technologies and standards activities have matured to the point that it is becoming feasible to transmit, store, process, and view video signals that are stored in digital formats.
Hence, to achieve some simple low-level image processing tasks (early-vision), a smart sensor integrates analogue and/or digital processing circuits in the pixel (Burns et al.,El Gamal et al.,Dudek, Hicks, ) or at the edge of the pixels array (Ni, Guan, ).
Most often, such circuits are dedicated for specific by: 1. Researchers in human and machine vision share the belief that massive parallel processing characterizes low-level vision. In this paper we review the various parallel algorithms used in Computer Vision. The problem of visual recognition is divided into three conceptual levels-low-level Cited by: Image Processing Fundamentals 2 We begin with certain basic definitions.
An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y).An. This book comprises selected papers of the International Conference on Signal Processing, Image Processing and Pattern Recognition, SIPheld as Part of the Future Generation Information Technology Conference, FGITin Conjunction with GDCin Conjunction with GDCJeju Island, Korea, in December Vision Systems: Segmentation and Pattern Recognition Ma Research in computer vision has exponentially increased in the last two decades due to the availability of cheap cameras and fast processors.