A Hybrid Top-Down Bottom-Up Approach for the Detection of Cuboid Shaped Objects

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Arrais, R (Author) Other; Oliveira, M (Author) Other; Toscano, C (Author) Other; Germano Veiga (Author) FEUP

Conference proceedings International

Title: IMAGE ANALYSIS AND RECOGNITION (ICIAR 2016)

Pages: 512-520

13th International Conference on Image Analysis and Recognition in Memory of Mohamed Kamel (ICIAR)

Povoa de Varzim, PORTUGAL, JUL 13-15, 2016

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Other information

Authenticus ID: P-00K-PB6

DOI: 10.1007/978-3-319-41501-7_57

Abstract (EN): While bottom-up approaches to object recognition are simple to design and implement, they do not yield the same performance as top-down approaches. On the other hand, it is not trivial to obtain a moderate number of plausible hypotheses to be efficiently verified by top-down approaches. To address these shortcomings, we propose a hybrid top-down bottom-up approach to object recognition where a bottom-up procedure that generates a set of hypothesis based on data is combined with a top-down process for evaluating those hypotheses. We use the recognition of rectangular cuboid shaped objects from 3D point cloud data as a benchmark problem for our research. Results obtained using this approach demonstrate promising recognition performances.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9