Add to ORKGAlgorithm k-means is useful for grouping operations; however, when is applied to large amounts of data, its computational cost is high. This research propose an optimization of k-means algorithm by using parallelization techniques and synchronization, which is applied to image segmentation. In the results obtained, the par-allel k-means algorithm, improvement 50 % to the algorithm se-quential k-means. General Terms: speedu
Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm fo...
The use of sequential programming is slowly getting replaced by distributed and parallel computing w...
In this paper, we propose a parallel algorithm for data classification, and its application for Magn...
Algorithm k-means is useful for grouping operations; however, when is applied to large amounts of da...
The paper presents speed up of the k-means algorithm for image segmentation. This speed up is achiev...
Various computer methods are sourced in parallel programming. Advances in methods and techniques wit...
The authors propose a parallel algorithm for data classification, and its application for Magnetic R...
Image processing technology is now widely used in the health area, one example is to help the radiol...
In this paper we discuss a classic clustering algorithm that can be used to segment images and a rec...
The design of parallel architectures to perform image segmentation processing is given. In addition,...
In mapping the k-means algorithm to FPGA hardware, we examined algorithm level transforms that drama...
Since many kinds of parallel architectures for video processing (1) are developed, the real-time pro...
Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm fo...
Image processing technology is now widely used in the health area, one example is to help the radiol...
Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm fo...
Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm fo...
The use of sequential programming is slowly getting replaced by distributed and parallel computing w...
In this paper, we propose a parallel algorithm for data classification, and its application for Magn...
Algorithm k-means is useful for grouping operations; however, when is applied to large amounts of da...
The paper presents speed up of the k-means algorithm for image segmentation. This speed up is achiev...
Various computer methods are sourced in parallel programming. Advances in methods and techniques wit...
The authors propose a parallel algorithm for data classification, and its application for Magnetic R...
Image processing technology is now widely used in the health area, one example is to help the radiol...
In this paper we discuss a classic clustering algorithm that can be used to segment images and a rec...
The design of parallel architectures to perform image segmentation processing is given. In addition,...
In mapping the k-means algorithm to FPGA hardware, we examined algorithm level transforms that drama...
Since many kinds of parallel architectures for video processing (1) are developed, the real-time pro...
Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm fo...
Image processing technology is now widely used in the health area, one example is to help the radiol...
Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm fo...
Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm fo...
The use of sequential programming is slowly getting replaced by distributed and parallel computing w...
In this paper, we propose a parallel algorithm for data classification, and its application for Magn...