The key weakness in modern computer vision systems identified
Computer vision algorithms have come back a protracted manner within the past decade. they have been shown to be nearly as good or higher than individuals at tasks like categorizing dog or cat breeds, and that they have the exceptional ability to spot specific faces out of an ocean of millions.
But an analysis by Brown University scientists shows that computers fail miserably at a category of tasks that even young kids don’t have any drawback with: determinative whether or not 2 objects in a picture ar constant or completely different. 
Computer Vision Model for Traffic Sign Recognition and Detection—A Survey
Computer vision is an associate knowledge domain field that deals with a high-level understanding of digital videos or pictures. The results of pc vision are within the type of a call or knowledge. This additionally includes ways to cherish gaining, processing, analyzing, understanding, and extracting high spatiality knowledge. visual perception is employed for distinctive the objects in an image or video. the looks of objects could very thanks to lighting or colors, viewing direction, and size or form. the matter we have a tendency to determine here is accuracy at night and insure atmospheric condition is a smaller amount that compared to daytime and additionally, we have a tendency to change to notice some signs at the dark. during this paper, we have a tendency to gift a close study of pc vision, visual perception, and additionally a study of traffic sign detection and recognition alongside its applications, advantages, and downsides. 
Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV
A sensible guide designed to induce you from basics to current state of art in laptop vision systems. Key options Master the various tasks related to laptop Vision and develop your own laptop Vision applications with easeLeverage the ability of Python, Tensorflow, Keras, and OpenCV to perform image process, object detection, feature detection and more with real-world datasets and totally useful code, this book is your one-stop guide to understanding laptop VisionBook DescriptionIn this book, you may notice many recently projected strategies in numerous domains of laptop vision. you may begin by fixing the correct Python surroundings to figure on sensible applications. This includes fixing libraries cherish OpenCV, TensorFlow, and Keras victimization Eunectes murinus. victimization these libraries, you may begin to know the ideas of image transformation and filtering. you may notice an in-depth clarification of feature detectors cherish quick and ORB; you may use them to seek out similar-looking objects. With an associate degree introduction to convolutional neural nets, you may find out how to make a deep neural web victimization Keras and the way to use it to classify the Fashion-MNIST dataset. 
Computer vision based corn kernel quality evaluation: traditional versus machine learning
The corn kernel quality analysis could be a trivial task for knowledgeable farmers and agriculture researchers, however, it becomes tough if we have a tendency to attempt to develop a laptop vision primarily based automatic resolution. during this thesis, we have a tendency to gift 2 approaches for this downside, concisely introduce the info sets resembling every methodology and compare the accuracy between them. we have a tendency to attack the corn kernel quality analysis downside by 2 completely different methods: (1) measure the standard supported the share of excellent corn kernels inside the scope by a “percentage” classifier trained with multi-class support vector machine (SVM). (2) measure the standard by a decent corn kernel detector trained with multiple progressive detectors, specifically quicker R-CNN and Retinanet. 
Computer Vision Syndrome Survey among the Medical Students in Sohag University Hospital, Egypt
Purpose: to research and describe the quantitative information of a specially designed well-structured form to survey the pc Vision Syndrome (CVS) among the medical students in Sohag University, Egypt.
Design: A prospective descriptive non-comparative cross-sectional study.
Methods: a hundred medical students enclosed during this study (50 males and fifty females). when rationalization of the character of the CVS to the medical students, they consummated a specially designed kind for CVS survey including twenty queries of high validity and dependability. 
 Key weakness in modern computer vision systems identified
Date: July 30, 2018, Source: Brown University (web link)
 Computer Vision Model for Traffic Sign Recognition and Detection—A Survey
Sindhu OS, Paul PV. Computer Vision Model for Traffic Sign Recognition and Detection—A Survey. InInternational Conference on Communications and Cyber-Physical Engineering 2018 2018 Jan 24 (pp. 679-689). Springer, Singapore. (web link)
 Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV
Dadhich A. Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV. (web link)
 Computer vision based corn kernel quality evaluation: traditional versus machine learning
Li X. Computer vision based corn kernel quality evaluation: traditional versus machine learning (Doctoral dissertation). (web link)
 Computer Vision Syndrome Survey among the Medical Students in Sohag University Hospital, Egypt
Mohammed Iqbal1*, Ahmed El-Massry2, Mohammed Elagouz1, and Hosam Elzembely3
1Department of Ophthalmology, Faculty of Medicine, Sohag University, Egypt.
2Department of Ophthalmology, Faculty of Medicine, Alexandria University, Egypt.
3Department of Ophthalmology, Faculty of Medicine, Minia University, Egypt. (web link)