Machine vision applications have become increasingly important and are becoming an integral part of our everyday life. This is fueled by the rapid increase of automation in industrial settings. While the demand is high, machine vision still faces significant challenges and is unable to complete vision tasks that are easier for humans. Such dilemma is primarily attributed to the limitations of shape and color-based object recognition paradigm that is being currently used. This research effort is aimed at developing an innovative solution to realize computer vision beyond color and shape to enable extensive application in manufacturing, agriculture, cybersecurity, transportation, construction, and biomedicine. The proposed laser object recognition method will enable advancements and understanding of object recognition via transformative computer vision methods. The research will lead to significant impact on object recognition in dark and cluttered environments, which is known to be difficult using conventional computer vision methods. This project proposes a novel object recognition method for confident object recognition. In contrast to existing shape and color-based object recognition methods, where limited visual information is utilized, the new laser object recognition method is designed to rely upon harnessing the laser-material interaction information to conduct object recognition. The proposed laser object recognition method is fundamentally rooted in the laws of phy