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A Navigation Assistant for the Visually Impaired by Means of Robust and Streamlined Object Recognition
The issue of mobility is considered among the foremost of concerns for visually impaired individuals. Scale-Invariant Feature Transform (SIFT) and Content-Based Image Retrieval (CBIR) are techniques often used for object recognition and image retrieval in the field of computer vision. This research develops a Navigation Assistant that aids the visually impaired with mobility in a known environment such as a household, by combining the principles behind SIFT and CBIR. Furthermore, this research tests several techniques developed by the authors to improve the Navigation Assistant. Using a video stream that was obtained from a small camera that the user carries, key objects in the household were identified using the software developed. The control Navigation Assistant, developed without the authors’ enhancements, had an object identification accuracy of 45.555% and an average runtime of 3.427 seconds. The experimental Navigation Assistant, which included the techniques of Color Segmentation, Difference of Images, and Contrast Manipulation created by the authors, had an accuracy of 75.555% and an average runtime of 1.820 seconds. Thus, the Navigation Assistant created using the enhancements was markedly improved in both accuracy and speed from the control Assistant, and is therefore applicable to real-life usage by visually impaired individuals.
Content-Based Image Retrieval, Scale-Invariant Feature Transform, Visually Impaired, Color Segmentation, Image Analysis, Difference of Images, Contrast Manipulation.
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