Name
Abstract | ||
|---|---|---|
Image segmentation is a key topic in computer vision, serving as a pre-step in a number of robotics tasks, including object recognition, obstacle avoidance and topological localization. In the literature, image segmentation has been employed as auxiliary information in order to improve optical flow performance. In this work, an alternative approach is proposed, in which optical flow information is used to aid image segmentation, aiming at scene understanding for mobile robots. The proposed system performs dense optical flow analysis, followed by clustering of the optical flow vectors in a four dimensional space (formed by the x and y positions, angle and magnitude of each vector). Results from the clustering are used as `seeds' in the segmentation process, performed by watershed segmentation in our implementation. In addition, the flow `image' is combined with the original image, generating an image better suited for watershed segmentation, reducing the local minima effect often seen in this type of segmentation algorithms. The main pipeline considers the use of multi-modality cameras (visible and thermal-infrared). Since they see substantially different information, multi-modality further improves the amount of features of the resulting flows. Experimental results in urban and semi-urban scenarios with efficient segmentation illustrate the applicability of the method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICRA.2014.6906980 | Robotics and Automation |
Keywords | Field | DocType |
collision avoidance,image segmentation,object recognition,robot vision,4D space,auxiliary information,computer vision,dense optical flow analysis,flow image,image segmentation,local minima effect,mobile robots,motion,multimodality cameras,object recognition,obstacle avoidance,optical flow information,optical flow performance,optical flow vectors,pipeline,robotics tasks,scene segmentation algorithms,semi-urban scenarios,topological localization,watershed segmentation | Computer vision,Scale-space segmentation,Segmentation,Image texture,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Engineering,Optical flow,Minimum spanning tree-based segmentation | Conference |
Volume | Issue | ISSN |
2014 | 1 | 1050-4729 |
Citations | PageRank | References |
1 | 0.36 | 27 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulo Vinicius Koerich Borges | 1 | 126 | 6.28 |
| Peyman Moghadam | 2 | 165 | 12.92 |