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Performance Comparison of Several Pre-Processing Methods in a Hand Gesture Recognition System based on Nearest Neighbor for Different Background Conditions
Regina Lionnie, Ivanna K. Timotius & Iwan Setyawan
Department of Electronic Engineering, Satya Wacana Christian University,
Jalan Diponegoro 52-60, Salatiga, 50711, Indonesia
Email: reginalionnie@gmail.com
Abstract. This paper presents a performance analysis and comparison of several pre-processing methods used in a hand gesture recognition system. The pre-processing methods are based on the combinations of several image processing operations, namely edge detection, low pass filtering, histogram equalization, thresholding and desaturation. The hand gesture recognition system is designed to classify an input image into one of six possible classes. The input images are taken with various background conditions. Our experiments show that the best result is achieved when the pre-processing method consists of only a desaturation operation, achieving a classification accuracy of up to 83.15%.
Keywords: binary image processing; desaturation; edge detection; hand gesture recognition; histogram equalization; low-pass filtering; nearest neighbor.
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