scholarly journals A Survey on Visionary Eye for Visual Impairment

2020 ◽  
pp. 33-39
Author(s):  
Ruchir Shah ◽  
Dhaval Tamboli ◽  
Ajay Makwana ◽  
Ravindra Baria ◽  
Kishori Shekokar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Vision Research ◽  
Vision System ◽  
Human Vision ◽  
Human Vision System ◽  
Blind Person ◽  
Common Goal ◽  
Survey Paper

In this survey paper, we have discussed a proposed system that can be a visionary eye for a blind person. A common goal in computer vision research is to build machines that can replicate the human vision system. For example, to recognize and describe objects/scenes. People who are blind to overcome their real daily visual challenges. To develop a machine that can work by the vocal and graphical assistive answer. A machine can work on voice assistant and take the image taken by a person and after an image processing and extract the result after neural networks.

scholarly journals A STUDY ON APPLICATION OF MACHINE LEARNING AND COMPUTER VISION FOR RETAIL PROJECTS

2017 ◽  
Vol 10 (13) ◽  
pp. 476
Author(s):  
Rima Borah ◽  
Rajarajeswari S
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Good Accuracy ◽  
Vision System ◽  
Human Vision ◽  
Retail Market ◽  
Human Vision System ◽  
Processing Information ◽  
Deep Cnn ◽  
The Brain

The motivation for developing computer vision is the human vision system which is the richest sense that we have. To us, vision seems an easy task ofjust seeing objects in daily life and identifying them, but in reality, our eyes along with the brain are processing information of around 50 images everysecond with millions of pixels in each image. Most of these images obtained are currently just looked at by people. The challenging task is to processimages from all these cameras and allow automation of tasks never before considered. Neural networks help us in making cameras intelligent enoughto understand the images it captures. Convolutional neural networks (CNN) are trained to give image classification results of good accuracy, with thechallenge to improve utilization of computing resources. Google Net is in its essence a deep CNN that uses inception architecture to attain leadingedge results for classification and detection problems. In this paper, a study was made on applications of computer vision techniques in retail andcustomer strategic projects. Further, it was analyzed that if cameras trained with CNN can work well enough to be deployed in retail market scenariosto automate sales and stock supervision.

A SURVEY OF BIOLOGICALLY INSPIRED IMAGE PROCESSING FOR OBJECTS RECOGNITION

2009 ◽  
Vol 09 (04) ◽  
pp. 495-510 ◽  
Author(s):  
WEIREN SHI ◽  
ZUOJIN LI ◽  
XIN SHI ◽  
ZHI ZHONG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Human Vision ◽  
Image Perception ◽  
Biologically Inspired ◽  
Recognition Mechanism ◽  
Environmental Objects ◽  
Future Work ◽  
Animal Vision

The human vision system is a very sophisticated image processing and objects recognition mechanism. However, it is a challenge to simulate the human or animal vision system to automate visual function in machines, because it is difficult to account for the view-invariant perception of universals such as environmental objects or processes and the explicit perception of featural parts and wholes in visual scenes. In this paper, we first present an introduction to the importance of biologically inspired computer vision and review general and key vision functions from neuroscience perspective. And most significantly, we give an important summarization to and discussion on the specific applications of biologically inspired modeling, including biologically inspired image pre-processing, image perception, and objects recognition. In the end, we give some important and challenging topics of computer vision for future work.

scholarly journals Special Issue on “Advances in Image Processing, Analysis and Recognition Technology”

2020 ◽  
Vol 10 (21) ◽  
pp. 7582
Author(s):  
Dariusz Frejlichowski
Keyword(s):  
Image Processing ◽  
Computer Analysis ◽  
Vision System ◽  
Human Vision ◽  
Human Vision System ◽  
Special Issue

For many decades researchers have been trying to make computer analysis of images as effective as the human vision system is [...]

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

Vehicle Security Distance Control Based on Binocular Parallax Vision

2014 ◽  
Vol 644-650 ◽  
pp. 207-210
Author(s):  
Shuang Liu ◽  
Xiang Jie Kong ◽  
Ming Cai Shan
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
High Precision ◽  
Stereo Matching ◽  
Vision System ◽  
Intelligent Transportation ◽  
Distance Control ◽  
Calibration Methods ◽  
Computer Vision Technology ◽  
Vehicle Security

Binocular parallax vision system is a kind of computer vision technology. Two cameras on different locations can get two different pictures of same object. The space position of the object can be calculated by the parallax information of two different pictures. The binocular parallax vision technology includes cameras calibration, image processing, and stereo matching analysis. The paper will introduce the inside and outside parameters calibration methods, and combing the traffic applications, designed the calibrating scheme. The parameters that obtained according to the scheme can meet the demands of measuring the vehicle distance. The high precision can meet the needs of intelligent transportation vehicles in a security vehicles spacing survey, which is an effective way for measuring the front car distance.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

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