Abstract
This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Feature (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene inside the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detection results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al.
Highlights
In the area of intelligent systems, the autonomous mobile robots are expected to have the ability to recognize their surrounding environment in real time
Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al Keywords: Object Detection; Speed-Up Robust Feature (SURF); Simple Linear Iterative Clustering (SLIC) Superpixels; Keypoints Detection; Local Features; Voting
The proposed method is based on the use of SLIC super pixel [6] and SURF [7], together with a voting process and the probability map, which is introduced in this work in order to improve the accuracy of object detection
Summary
In the area of intelligent systems, the autonomous mobile robots are expected to have the ability to recognize their surrounding environment in real time. A weakness shared by all of the above approaches is that they can fail when local image information is insufficient, that is, if the target is very small or highly occluded To reduce these problems, Mae et al [8] included a local feature matching algorithm using local geometric consistency for object detection. This research is suited for objects that have texture, and performs better when the objects have flat surface or when they are observed from the same view angle The advantage of this approach is the simplicity of the implementation and portability for various robot control systems, minimal knowledge for the target pattern and fairly good performance. The matching could worsen if the object has non-planar surface and if it is observed from a different view-point
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