Geometric Surface Image Prediction for Image Recognition Enhancement

TL;DR

This paper introduces a method to predict geometric surface images from photographs using GANs, aiming to improve object recognition accuracy under varying lighting conditions by providing a more consistent representation.

Contribution

The work presents a novel approach to generate geometric surface images from color photos to enhance image recognition robustness against lighting variations.

Findings

Predicted geometric surface images reduce ambiguity compared to color images.

The method improves recognition accuracy under different lighting conditions.

GAN-based prediction effectively generates surface images from color photographs.

Abstract

This work presents a method to predict a geometric surface image from a photograph to assist in image recognition. To recognize objects, several images from different conditions are required for training a model or fine-tuning a pre-trained model. In this work, a geometric surface image is introduced as a better representation than its color image counterpart to overcome lighting conditions. The surface image is predicted from a color image. To do so, the geometric surface image together with its color photographs are firstly trained with Generative Adversarial Networks (GAN) model. The trained generator model is then used to predict the geometric surface image from the input color image. The evaluation on a case study of an amulet recognition shows that the predicted geometric surface images contain less ambiguity than their color images counterpart under different lighting conditions and can be used effectively for assisting in image recognition task.