  Textureless object recognition has become a significant task in Computer
Vision with the advent of Robotics and its applications in manufacturing
sector. It has been challenging to obtain good accuracy in real time because of
its lack of discriminative features and reflectance properties which makes the
techniques for textured object recognition insufficient for textureless
objects. A lot of work has been done in the last 20 years, especially in the
recent 5 years after the TLess and other textureless dataset were introduced.
In this project, by applying image processing techniques we created a robust
augmented dataset from initial imbalanced smaller dataset. We extracted edge
features, feature combinations and RGB images enhanced with feature/feature
combinations to create 15 datasets, each with a size of ~340,000. We then
trained four classifiers on these 15 datasets to arrive at a conclusion as to
which dataset performs the best overall and whether edge features are important
for textureless objects. Based on our experiments and analysis, RGB images
enhanced with combination of 3 edge features performed the best compared to all
others. Model performance on dataset with HED edges performed comparatively
better than other edge detectors like Canny or Prewitt.
