Hurriyatul Fitriyah, Agung Setia Budi
Object counting based on image data has been developed in many research. It is fast, automatic and noncontact solution that is applied in health, microbiology, object tracking, robotics and industry. During the counting, object that differs from majority object might be presented in a frame. This outlier object should be detected and not be counted. This study present an algorithm to detect outliers in object counting based on color and shape information. The color was based on Hue whilst the shape was based on distance transform. Both features were chosen as it is invariant to position and rotation in plane. Outlier detection utilized Median Absolute Deviation (MAD) on Histograms of both features. The testing shows promising result (accuracy of 94.3%) in 35 images with simple background. © 2019 IEEE.
Universitas Brawijaya, Dept. of Computer Engineering, Malang, Indonesia
