Artificial Neural Network algorithm for image Compression and Edge Detection

Asma Asma Abdulelah Abdulrahman, Fouad Shaker Tahir Al-azawi
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Abstract

In recent years, detection of things measured in the PASCAL VOC dataset has stabilized. Low-level image features and high-level context. There are many ways to perform complex grouping systems that combine image features in this work. An algorithm has been proposed to reveal the edge of the image in the proposed way Fully Connected Network (FC) by Convolution Neural Network (CNN) in short time using CNN method to reduce the space occupied by the original information of the image. To highlight the role of Artificial Neural Network (ANN) in the field of images to detect the edges using modern techniques in many areas of science, engineering and medicine, where the images of the cancerous cells algorithm that was created in this paper are revealed showing the importance of Artificial Neural Network (ANN) and Fully Connected Network (FC) by Convolution Neural Network (CNN) to detect cancer cells and tumors.
用于图像压缩和边缘检测的人工神经网络算法
近年来,在PASCAL VOC数据集中测量的事物的检测已经稳定下来。低级图像特征和高级上下文。在这项工作中,有许多方法可以执行结合图像特征的复杂分组系统。提出了一种利用卷积神经网络(CNN)的全连接网络(FC)在短时间内显示图像边缘的算法,利用CNN的方法减少图像原始信息占用的空间。为了突出人工神经网络(ANN)在图像领域的作用,利用现代技术在许多科学、工程和医学领域检测边缘,本文揭示了创建的癌细胞图像算法,显示了人工神经网络(ANN)和卷积神经网络(CNN)的全连接网络(FC)在检测癌细胞和肿瘤中的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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