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New fast image edge-detection algorithm based on composite self-adaption predictor

机译:基于复合自适应预测器的新快速图像边缘检测算法

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Using advantages of gradient adjusted predictor (GAP) and gradient edge detection (GED) predictors of lossless image coding for reference, and the image was cut into four equal parts with the application of Graphics Processor Unit (GPU) parallel technology operation. In four sub-images, the composite self-adaption predictor was employed for predicting error image, threshold classification error image edge and thinning edge. Results showed that with the application of the parallel technology which avoided errors multiply, not only the complexity of the time was reduced significantly, but also the distinct, holistic and detail-rich edge image was obtained.
机译:利用梯度调整的预测器(间隙)和梯度边缘检测(GED)预测器的优点,无损图像编码用于参考,并且通过应用图形处理器单元(GPU)并行技术操作将图像切割成四个相等的部分。在四个子图像中,使用复合自适应预测器用于预测误差图像,阈值分类误差图像边缘和变薄边缘。结果表明,随着避免误差的并行技术乘以乘以,不仅显着降低了时间的复杂性,而且还获得了不同的,整体和细胞细胞的边缘图像。

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