scholarly journals A survey on computer vision technology in Camera Based ETA devices

2015 ◽  
Vol 1 (3) ◽  
pp. 115
Author(s):  
Amir Ramezani Dooraki
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Stereo Vision ◽  
Visually Impaired ◽  
High Potential ◽  
Methods And Techniques ◽  
Computer Vision Technology ◽  
Visually Impaired Persons ◽  
Vision Technique

Electronic Travel Aid systems are expected to make impaired persons able to perform their everyday tasks such as finding an object and avoiding obstacles easier. Among ETA devices, Camera Based ETA devices are the new one and with a high potential for helping Visually Impaired Persons. With recent advances in computer science and specially computer vision, Camera Based ETA devices used several computer vision algorithms and techniques such as object recognition and stereo vision in order to help VIP to perform tasks such as reading banknotes, recognizing people and avoiding obstacles. This paper analyses and appraises a number of literatures in this area with focus on stereo vision technique. Finally, after discussing about the methods and techniques used in different literatures, it is concluded that the stereo vision is the best technique for helping VIP in their everyday navigation.

scholarly journals Real-Time Object Detection for Aiding Visually Impaired using Deep Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1600-1605 ◽  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Internet Of Things ◽  
Computer Science ◽  
Visually Impaired ◽  
Vision Loss ◽  
Assistive Device ◽  
Blind People ◽  
Normal People ◽  
The People

This research aims to create an assistive device for the people who are suffering from vision loss or impairment. The device is designed for blind people to overcome the daily challenges they face which may be perceived to be trivial to normal people. The device is created by using advance computer science technologies such as deep learning, computer vision and internet of things. The device created would be able to detect and classify daily objects and give a voice feedback to the user who is handicapped with blindness.

Data Analyzing via Probabilistic Modeling

2021 ◽  
pp. 25-51
Author(s):  
Dariusz Jacek Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing, and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. An important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation are possible using a new method of Hurwitz-Radon matrices.

Object Recognition via Contour Points Reconstruction Using Hurwitz - Radon Matrices

2013 ◽  
pp. 998-1018
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. Important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation is possible using new method of Hurwitz - Radon Matrices.

Object Recognition via Contour Points Reconstruction Using Hurwitz - Radon Matrices

2011 ◽  
pp. 87-107 ◽  
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. Important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation is possible using new method of Hurwitz - Radon Matrices.

Comparative Study of Stereo Correspondence Techniques for Underwater Images

2020 ◽  
pp. 230-261
Author(s):  
Prabhakar C. J.
Keyword(s):  
Computer Vision ◽  
Comparative Study ◽  
3D Reconstruction ◽  
Stereo Vision ◽  
Stereo Correspondence ◽  
Stereo Images ◽  
Visual Comparison ◽  
Simulation Results ◽  
Vision Technique

The aim of the chapter is to provide an overview of the computer vision techniques involved in stereo correspondence of underwater images, which is one of the important steps in the 3D reconstruction of underwater objects and scenes. The author provide briefly the various categories of techniques for 3D reconstruction of objects. Also, the author provides steps involved in the 3D reconstruction of objects using stereo vision technique, particularly, more focused on stereo correspondence step and its techniques available in the literature. Further, they present some of the local and global stereo correspondence methods employed for underwater stereo images with simulation results. Finally, the author presents a visual comparison of local and global stereo correspondence techniques employed for underwater stereo images.

Image Feature Extraction and Object Recognition Based on Vision Neural Mechanism

2019 ◽  
Vol 34 (06) ◽  
pp. 2054017 ◽  
Author(s):  
Peng Cheng Wei ◽  
Yang Zou
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Object Recognition ◽  
Visual Information ◽  
Neural Mechanism ◽  
Image Features ◽  
Image Feature ◽  
Training Samples ◽  
Computer Vision Technology ◽  
Processing Of Visual Information

As an important branch of artificial intelligence, computer vision plays a huge role in the rapid development of artificial intelligence. From a biological point of view, in the acquisition and processing of information, vision is much more important than hearing, touch, etc., because 70% of the human cerebral cortex is processing visual information. Therefore, advances in computer vision technology are critical to the development of artificial intelligence that is designed to allow machines to think and handle things like humans. The acquisition and processing of visual information has always been the focus of computer vision research, and it is also difficult. The main problem of traditional computer vision technology in the processing of visual information is that the extracted image features are less discriminative, the generalization ability of image features in complex background scenes is insufficient, and the recognition ability on object recognition is poor. In response to these problems, based on the visual neural mechanism, this paper establishes an appropriate computer model for the neuronal cells in the human primary visual cortex, models the recognition response mechanism of the visual ventral system, and performs image feature extraction on the training samples. And object recognition. The results show that compared with the traditional methods, the proposed method effectively improves the discrimination of image features, and the image features extracted under complex background scenes have good generalization ability. On this basis, the training samples can be effectively recognized. The results show that the model based on the visual neural mechanism, the recognition of the edge, orientation and contour of the training sample show the advantages of the biological vision mechanism in object recognition.

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