Low-Level computer vision algorithms: Performance evaluation on parallel and distributed architectures

Author(s):  
G. Destri ◽  
P. Marenzoni
1989 ◽  
pp. 255-269
Author(s):  
Vipin Chaudhary ◽  
J. K. Aggarwal
Keyword(s):  

Author(s):  
Mahmoud Afifi ◽  
Abdelrahman Abdelhamed ◽  
Abdullah Abuolaim ◽  
Abhijith Punnappurath ◽  
Michael S Brown
Keyword(s):  

Author(s):  
Lalit B. Damahe ◽  
Nileshsingh V. Thakur

Image representation and compression is one of the important fields of computer vision that contribute to the reduction of size of an image and other types of application areas such as image restoration, retrieval, etc. Image representation is important with respect to storage of image information, and it further extends to the compression, which may be lossy or lossless. Image compression can be applied to various applications which mainly include medical imaging, traffic monitoring, military, multimedia transmission, smart cell devices, and almost in all the domains that require less transmission and storage cost, specifically image retrieval processing. This chapter presents the various image representation compression and retrieval approaches. The retrieval approaches on personal computer and smart cell devices are discussed. Finally, the key issues are identified for image representation compression and retrieval on the basis of performance evaluation parameters like encoding time, decoding time, compression ratio, precision, recall, and elapsed time.


1994 ◽  
Vol 03 (01) ◽  
pp. 97-125 ◽  
Author(s):  
ARVIND K. BANSAL

Associative Computation is characterized by intertwining of search by content and data parallel computation. An algebra for associative computation is described. A compilation based model and a novel abstract machine for associative logic programming are presented. The model uses loose coupling of left hand side of the program, treated as data, and right hand side of the program, treated as low level code. This representation achieves efficiency by associative computation and data alignment during goal reduction and during execution of low level abstract instructions. Data alignment reduces the overhead of data movement. Novel schemes for associative manipulation of aliased uninstantiated variables, data parallel goal reduction in the presence multiple occurrences of the same variables in a goal. The architecture, behavior, and performance evaluation of the model are presented.


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