β Scribed by Tsutomu Yamamoto; Hirokazu Kato; Kosuke Sato; Seiji Inokuchi
No coin nor oath required. For personal study only.
Many human activities are based on visual recognition of a scene. Development of scene recognition capability on computer is essential to the development of an intelligent robot.
This paper proposes a threeβdimensional (3βD) object recognition system using functional models. In conventional geometric modelβbased recognition, one reference model must be provided for each possible object shape in the same category. This paper proposes a functional model that will allow flexibility in representation of objects belonging to the same category.
Experiments were conducted to provide measurement data for demonstrating this functional modelβbased object recognition technique. Scene segmentation uses both gray scale image and range image to provide superior segmentation results. The performance of a functional modelβbased method for recognizing objects with different shapes that belong to the same functional category is demonstrated.
π SIMILAR VOLUMES
Geometric hashing (GH) and partial pose clustering are well-known algorithms for pattern recognition. However, the performance of both these algorithms degrades rapidly with an increase in scene clutter and the measurement uncertainty in the detected features. The primary contribution of this paper