In this paper, we present a fast parallel implementation of linear feature extraction on IBM SP-2. We first analyze the machine features and the problem characteristics to understand the overheads in parallel solutions to the problem. Based on these, we propose an asynchronous algorithm which enhances processor utilization and overlaps communication with computation by maintaining algorithmic threads in each processing node. Our implementation shows that, given a 512 \Theta 512 image, the linear feature extraction task can be performed in 0.065 seconds on a SP-2 having 64 processing nodes. A serial implementation takes 3.45 seconds on a single processing node of SP-2. A previous implementation on CM-5 takes 0.1 second on a partition of 512 ...
We present a new method for image feature-extraction for learning image classification. An image is...
The number of cores per cpu is predicted to double every second year. Therefore, the opportunity to ...
Abstract—The demand for real-time processing of robust feature detection is one of the major issues ...
In this paper, we present a fast parallel implementation of linear feature extraction on IBM SP-2. W...
In this paper, we present a fast parallel implementation of an image feature extraction task on Conn...
In this paper, we summarize our results in parallelizing the linear approximation step on current di...
Image feature extraction is instrumental for most of the best-performing algorithms in computer vis...
ISBN: 0780373049A joint algorithm-architecture analysis leads to a new version of picture segmentati...
Many image-processing applications require special-purpose hardware to run in real time. Others can ...
Many image-processing applications require special-purpose hardware to run in real time. Others can ...
AbstractImage feature extraction is widely used in content-based image retrieval(CBIR), computer ver...
Image feature extraction is instrumental for most of the best-performing algorithms in computer visi...
We propose a scalable and fexible hardware architecture for the extraction of image features, used i...
The Connection Machine is a fine-grained parallel computer having up to 64K processors. It support...
The main reason for the moderate success of parallel computing has been the lack of a bridging and u...
We present a new method for image feature-extraction for learning image classification. An image is...
The number of cores per cpu is predicted to double every second year. Therefore, the opportunity to ...
Abstract—The demand for real-time processing of robust feature detection is one of the major issues ...
In this paper, we present a fast parallel implementation of linear feature extraction on IBM SP-2. W...
In this paper, we present a fast parallel implementation of an image feature extraction task on Conn...
In this paper, we summarize our results in parallelizing the linear approximation step on current di...
Image feature extraction is instrumental for most of the best-performing algorithms in computer vis...
ISBN: 0780373049A joint algorithm-architecture analysis leads to a new version of picture segmentati...
Many image-processing applications require special-purpose hardware to run in real time. Others can ...
Many image-processing applications require special-purpose hardware to run in real time. Others can ...
AbstractImage feature extraction is widely used in content-based image retrieval(CBIR), computer ver...
Image feature extraction is instrumental for most of the best-performing algorithms in computer visi...
We propose a scalable and fexible hardware architecture for the extraction of image features, used i...
The Connection Machine is a fine-grained parallel computer having up to 64K processors. It support...
The main reason for the moderate success of parallel computing has been the lack of a bridging and u...
We present a new method for image feature-extraction for learning image classification. An image is...
The number of cores per cpu is predicted to double every second year. Therefore, the opportunity to ...
Abstract—The demand for real-time processing of robust feature detection is one of the major issues ...