Title :
Grouping-based nonadditive verification
Author :
Amir, Arnon ; Lindenbaum, Michael
Author_Institution :
Dept. of Comput. Sci., Technion-Israel Inst. of Technol., Haifa, Israel
fDate :
2/1/1998 12:00:00 AM
Abstract :
Verification is the final decision stage in many object recognition processes. It is carried out by evaluating a score for every hypothesis and choosing the hypotheses associated with the highest score. This paper suggests a grouping-based verification paradigm, relying on the observation that a group of data features belonging to a hypothesized object instance should be a “good group”. Therefore, it should support perceptual grouping information available from the image by grouping relations. The proposed score, which is the joint likelihood of these grouping cues, quantifies this observation in a probabilistic framework. Experiments with synthetic and real images show that the proposed method performs better in difficult cases
Keywords :
image recognition; object recognition; probability; data features; grouping-based nonadditive verification; hypothesis scores; joint likelihood; object recognition processes; Additives; Humans; Image segmentation; Layout; Object recognition; Particle measurements; Reliability theory;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on