HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color and Texture with a Finger-Mounted Camera

Autor:	Medeiros, Alexander J., Stearns, Lee, Froehlich, Jon E.
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition H.5.m
Druh dokumentu:	Working Paper
Popis:	We demonstrate the use of DeCAF and Improved Fisher Vector image features to classify clothing texture. The issue of choosing clothes is a problem for the blind every day. This work attempts to solve the issue with a finger-mounted camera and state-of-the-art classification algorithms. To evaluate our solution, we collected 520 close-up images across 29 pieces of clothing. We contribute (1) the HCTD, an image dataset taken with a NanEyeGS camera, a camera small enough to be mounted on the finger, and (2) evaluations of state-of-the-art recognition algorithms applied to our dataset - achieving an accuracy >95%. Throughout the paper, we will discuss previous work, evaluate the current work, and finally, suggest the project's future direction. Comment: 10 pages, 15 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.12225 Zobrazit plný text záznamu View this record from Arxiv