一种先进的计算机辅助图像鉴别与分类工具的设计
Design an Advance computer-aided tool for Image Authentication and Classification
摘要：
多年来，数字图像处理和人工智能领域的进步已被应用于解决许多现实生活中的问题。这可以在安全系统的面部图像识别，身份注册中看到。因此，身份配准的瓶颈是图像处理。这些是以图像预处理、通过裁剪的图像区域提取、使用主成分分析（PCA）的特征提取和使用离散余弦变换（DCT）的图像压缩的形式进行的。其他处理包括使用对比度拉伸进行滤波和直方图均衡化，同时作为分析工具的一部分增强图像。因此，本研究工作提出了一个通用的集成图像伪造检测分析工具与图像人脸识别使用反向传播神经网络（BPNN）处理器。所设计的工具是一个多功能的智能工具，具有可编程的错误目标和光强度的新架构。此外，其先进的双数据库提高了高性能应用程序的效率。事实上，人脸图像识别将总是，给出一个匹配的输出或最接近的可能的输出图像的每一个输入图像的真实性无关，通用的智能GUI工具，提出并设计执行图像伪造检测与2%的错误率的高精度。同时，提出了一种新的结构，提供了有效的自动图像伪造检测的所有输入的测试图像的BPNN识别。因此，输入图像将在被馈送到识别工具之前被认证。
英文摘要：
Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing includes filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Back Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency of a high-performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of 2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
