TY -的A2 Sarfraz,沙赫扎德盟博士——Nandhini Abirami, r . AU - Durai Raj文森特,p . m . AU - Srinivasan Kathiravan AU -塔里克,乌斯曼AU - Chang Chuan-Yu PY - 2021 DA - 2021/04/20 TI - CNN和深深甘在计算视觉Perception-Driven图像分析SP - 5541134六世- 2021 AB -计算视觉感知,也称为计算机视觉,人工智能领域,使计算机处理数字图像和视频和生物视觉以类似的方式。它涉及方法开发复制生物视觉的功能。计算机视觉的目标是超越生物视觉的功能从视觉数据中提取有用的信息。今天是生成的大量数据的驱动因素之一计算机视觉的巨大的增长。这个调查包含概述现有应用程序的计算视觉感知的深度学习。调查探讨了各种深度学习技术适应解决计算机视觉问题使用卷积神经网络和深深生成对抗的网络。深度学习的缺陷及其解决方案简要讨论。讨论的解决方案是辍学和扩充。结果表明,有一个显著的改善使用辍学和数据增加的准确性。深卷积神经网络的应用程序,即图像分类、定位和检测,文档分析、语音识别,进行了较为详细的试验研究。 In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides. SN - 1076-2787 UR - https://doi.org/10.1155/2021/5541134 DO - 10.1155/2021/5541134 JF - Complexity PB - Hindawi KW - ER -