Title
Which Edges Matter?
Abstract
In this paper, we investigate the ability of humans to recognize objects using different types of edges. Edges arise in images because of several different physical phenomena, such as shadow boundaries, changes in material albedo or reflectance, changes to surface normals, and occlusion boundaries. By constructing synthetic photo realistic scenes, we control which edges are visible in a rendered image to investigate the relationship between human visual recognition and that edge type. We evaluate the information conveyed by each edge type through human studies on object recognition tasks. We find that edges related to surface normals and depth are the most informative edges, while texture and shadow edges can confuse recognition tasks. This work corroborates recent advances in practical vision systems where active sensors capture depth edges (e.g. Microsoft Kinect) as well as in edge detection where progress is being made towards finding object boundaries instead of just pixel gradients. Further, we evaluate seven standard and state-of-the-art edge detectors based on the types of edges they find by comparing the detected edges with known informative edges in the synthetic scene. We suggest that this evaluation method could lead to more informed metrics for gauging developments in edge detection, without requiring any human labeling. In summary, this work shows that human proficiency at object recognition is due to surface normal and depth edges and suggests that future research should focus on explicitly modeling edge types to increase the likelihood of finding informative edges.
Year
DOI
Venue
2013
10.1109/ICCVW.2013.80
ICCVW '13 Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops
Keywords
Field
DocType
object recognition,shadow edge,human visual recognition,human proficiency,edge detection,human study,state-of-the-art edge detector,depth edge,edge type,informative edge,image resolution,image sensors,visual perception
Computer vision,Shadow,Object detection,Image sensor,Pattern recognition,Visualization,Edge detection,Computer science,Artificial intelligence,Pixel,Normal,Cognitive neuroscience of visual object recognition
Conference
Volume
Issue
Citations 
2013
1
2
PageRank 
References 
Authors
0.40
32
5
Name
Order
Citations
PageRank
Aayush Bansal1906.38
Adarsh Kowdle258424.77
Devi Parikh32929132.01
Andrew Gallagher440916.03
C. Lawrence Zitnick57321332.72