SMART APPLICATION FOR THE VISUALLY- IMPAIRED

In this work, an application that would allow recognizing objects from images recorded by the camera of a mobile device was developed. An android phone camera was used to take images of some objects and then store them in the android database and the name of each object was stored in an audio mode....

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Bibliographic Details
Published in:International journal of advanced research in computer science 2021-04, Vol.12 (2), p.10-17
Main Author: Olayinka Obe, Olumide
Format: Article
Language:English
Subjects:
Accelerated tests
Algorithms
Blindness
Cameras
Computer science
Corner detection
Electronic devices
Hessian matrices
Object recognition
Performance enhancement
Radio frequency identification
Smartphones
Software
Sound filters
Visual impairment
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Summary:In this work, an application that would allow recognizing objects from images recorded by the camera of a mobile device was developed. An android phone camera was used to take images of some objects and then store them in the android database and the name of each object was stored in an audio mode. The SIFT (ScaleInvariant Feature Transform) was applied for the development of the application. To improve the performance of the application, one of the fastest corner detection algorithms, the Features from Accelerated Segment Test (FAST) algorithm was implemented. Since the algorithm was implemented on a smartphone, OpenCV for Android SDK was used. The cascaded filters approach was used by SIFT to detect scaleinvariant characteristic points, where the difference of Gaussians (DoG) was calculated on rescaled images progressively. A blob detector based on the Hessian matrix to find points of interest was used by SURF. To measure local change around the points, the determinant of the Hessian matrix was used, and points were chosen based on where this determinant is maximal. The determinant of the Hessian was used by SURF to select scale. The auditory presentation of object recognition results to the blind user was done through a prerecorded message. 97% accuracy is recorded in the performance of the system.
ISSN:0976-5697
0976-5697
DOI:10.26483/ijarcs.v12i2.6690