Add to ORKGThis work first reviews an already-developed, existing deterministic parallel algorithm to compute the complete histogram of an image in optimal number of steps (log n) on a hypercube architecture and utilizing memory space on the order of O(x1/2 log x), where x is the number of gray levels in the image, at each processing element. The paper then introduces our improvement to this algorithm’s memory requirements by introducing the concept of randomization into the algorithm
Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement....
We present a compact histogram computation algorithm which considerably reduces, without loss, the a...
[[abstract]]The real-time parallel computation of histograms using an array of pipelined cells is pr...
This work first reviews an already-developed, existing deterministic parallel algorithm [1] to compu...
Computing a complete histogram of an image in Log(n) steps and minimum expected memory requirements ...
In this paper, a new method to compute the image histogram is presented, along with the image maximu...
We develop efficient reconfigurable mesh (RMESH) algorithms to compute the histogram of an image and...
Parallel algorithms for programming low-level vision mechanisms on the JPL-Caltech hypercube are rep...
Proposed is a unique cell histogram architecture which will process k data items in parallel to comp...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
Histogramming is a technique by which input datasets are mined to extract features and patterns. His...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
This paper presents efficient and portable implementations of two useful primitives in image pro...
Several commercial hypercube parallel processors with the potential to deliver massive parallelism c...
AbstractA novel histogram generation hardware architecture, which can develop histogram for all type...
Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement....
We present a compact histogram computation algorithm which considerably reduces, without loss, the a...
[[abstract]]The real-time parallel computation of histograms using an array of pipelined cells is pr...
This work first reviews an already-developed, existing deterministic parallel algorithm [1] to compu...
Computing a complete histogram of an image in Log(n) steps and minimum expected memory requirements ...
In this paper, a new method to compute the image histogram is presented, along with the image maximu...
We develop efficient reconfigurable mesh (RMESH) algorithms to compute the histogram of an image and...
Parallel algorithms for programming low-level vision mechanisms on the JPL-Caltech hypercube are rep...
Proposed is a unique cell histogram architecture which will process k data items in parallel to comp...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
Histogramming is a technique by which input datasets are mined to extract features and patterns. His...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
This paper presents efficient and portable implementations of two useful primitives in image pro...
Several commercial hypercube parallel processors with the potential to deliver massive parallelism c...
AbstractA novel histogram generation hardware architecture, which can develop histogram for all type...
Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement....
We present a compact histogram computation algorithm which considerably reduces, without loss, the a...
[[abstract]]The real-time parallel computation of histograms using an array of pipelined cells is pr...