TY - GEN
T1 - Cognitive vision for efficient scene processing and object categorization in highly cluttered environments
AU - Choi, Changhyun
AU - Christensen, Henrik I.
PY - 2009/12/11
Y1 - 2009/12/11
N2 - One of the key competencies required in modern robots is finding objects in complex environments. For the last decade, significant progress in computer vision and machine learning literatures has increased the recognition performance of well localized objects. However, the performance of these techniques is still far from human performance, especially in cluttered environments. We believe that the performance gap between robots and humans is due in part to humans' use of an attention system. According to cognitive psychology, the human visual system uses two stages of visual processing to interpret visual input. The first stage is a pre-attentive process perceiving scenes fast and coarsely to select potentially interesting regions. The second stage is a more complex process analyzing the regions hypothesized in the previous stage. These two stages play an important role in enabling efficient use of the limited cognitive resources available. Inspired by this biological fact, we propose a visual attentional object categorization approach for robots that enables object recognition in real environments under a critical time limitation. We quantitatively evaluate the performance for recognition of objects in highly cluttered scenes without significant loss of detection rates across several experimental settings.
AB - One of the key competencies required in modern robots is finding objects in complex environments. For the last decade, significant progress in computer vision and machine learning literatures has increased the recognition performance of well localized objects. However, the performance of these techniques is still far from human performance, especially in cluttered environments. We believe that the performance gap between robots and humans is due in part to humans' use of an attention system. According to cognitive psychology, the human visual system uses two stages of visual processing to interpret visual input. The first stage is a pre-attentive process perceiving scenes fast and coarsely to select potentially interesting regions. The second stage is a more complex process analyzing the regions hypothesized in the previous stage. These two stages play an important role in enabling efficient use of the limited cognitive resources available. Inspired by this biological fact, we propose a visual attentional object categorization approach for robots that enables object recognition in real environments under a critical time limitation. We quantitatively evaluate the performance for recognition of objects in highly cluttered scenes without significant loss of detection rates across several experimental settings.
UR - http://www.scopus.com/inward/record.url?scp=76249092978&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=76249092978&partnerID=8YFLogxK
U2 - 10.1109/IROS.2009.5354590
DO - 10.1109/IROS.2009.5354590
M3 - Conference contribution
AN - SCOPUS:76249092978
SN - 9781424438044
T3 - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
SP - 4267
EP - 4274
BT - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
T2 - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Y2 - 11 October 2009 through 15 October 2009
ER -