Color refinement using deep neural networks for enhancing color recognition in a projector‐camera system

Abstract

In projector‐camera systems, object recognition is essential to enable users to interact with physical objects. Among several input features used by the object classifier, color information is widely used as it is easily obtainable. However, the color of an object seen by the camera changes due to the projected light from the projector, which degrades the recognition performance. To solve this problem, we propose a method to restore the original color of an object from the observed color through camera. The color refinement method has been developed based on the deep neural network. The inputs to the neural network are the color of the projector light as well as the observed color of the object in multiple color spaces, including RGB, HSV, HIS, and HSL. The neural network is trained in a supervised manner. Through a number of experiments, we show that our refinement method reduces the difference from the original color and improves the object recognition rate implemented with a number of classification methods.

TFT‐LCD mura defect visual inspection method in multiple backgrounds

“…In recent years, deep learning-based object detection methods have been well used in many fields, [11][12][13][14] and they are mostly based on supervised learning methods. Compared with traditional machine learning, deep learning algorithm has obvious advantages.…”