Assessing Feature Importance in the Context of Object Recognition

摘要

A popular paradigm in computer vision is based on dividing the vision problem into three stages namely segmentation, feature extraction and recognition. For example edge detection followed by line detection followed by planar object recognition. It can be argued that each of these stages needs to be thoroughly described to enable vision systems to be configured with predictable performance. However an alternative view is that the performance of each stage is not in itself important as long as the overall performance is acceptable. This paper discusses feature performance concentrating on the assessment of edge-based feature detection and object recognition. Evaluation techniques are discussed for assessing arc and line detection algorithms and for features in the context of verification and pose refinement strategies. These techniques can then be used for the design and integration of indexing and verification stages of object recognition. A theme of the paper is the need to assess feature extraction in the context of the chosen task.