Recognition of partially occulted objects

Amina Ait Aoudia

LRIA, Computer Science Department,

University of Sciences and Technology – Houari

Boumediene

Algiers, Algeria

aaitaoudia@usthb.dz

Saliha AOUAT

LRIA, Computer Science Department,

University of Sciences and Technology – Houari

Boumediene

Algiers, Algeria

saouat@usthb.dz

Abstract—We propose a new method to recognize partially

occulted objects. The technique consists to create first, an index

containing codes associated to each image of the database using

the Quad-tree structure, and then we define a tolerance threshold

that allows the recognition of partially occulted objects.

Keywords— Occlusion; Silhouette; Quad-tree; Index.

I. INTRODUCTION

We have chosen in our approach the Quad-tree structure to overcome the problem of object recognition in the presence of occlusion. The Quad-Tree is a data structure representing trees (Figure 1), where each node has four children [1,2,3,4,5,6]. To overcome the problem of occlusion and allow retrieving the most similar model for the query in the image database, we propose to use the linear codification as published in [1].

Fig. 1. Representation of a Quad-Tree[1]

II. RECOGNITION OF OCCULTED OBJECTS

The recognition is achieved by verifying if the descriptor of the occulted object is a sub-descriptor of the initial object. In this step, we have to verify the correspondence between descriptors relatively to the tolerance threshold that concludes if two compared descriptors are almost similar for the given threshold. The tolerance threshold is the number of differences in two descriptors to be matched. For example if 3 leaves of the query are matched to 3 internal nodes of the model, the threshold in this case is 3.

Figure 2 shows results obtained by our system after applying the technique with a tolerance threshold equals to 3. We can see that our system gives good results independently of the position of the occlusion. The approach is also invariant to rotation.

Fig. 2. Response of the system for many sides of occlusion

III. CONCLUSION

Quad-tree techniques give bad results when applied on occulted objects and especially in presence of rotation.

To overcome that problem, we have proposed in this work a new method to index and recognize partially occulted objects based on the quad-tree structure.

Tests applied on hundredth of images showed the efficiency of our method even thought the occlusion reached 30% of the object.

REFERENCES

[1] Aouat S., Larabi S.Indexing binary images using quad-tree Decomposition.International Conference on System, Man, and Cybernetics,Istambul, Turkey, 10-13 Octber, 2010.

[2] Oliveira F.Matching contours in images using curvature information,.Computational Vision and Medical Image Processing, 2008.

[3] Papadourakis V., Argyros A. Multiple objects tracking in the presence of long-term occlusions.Computer Vision and Image Understanding, 2011.

[4] Jamie Shotton, Toby Sharp, Alex Kipman, Andrew Fitzgibbon, Mark Finocchio, Andrew Blake, Mat Cook, Richard Moore.Real-time human pose recognition in parts from single depth images.Communications of the ACM, Volume 56 Issue 1, 2013.

[5] Torralba A., Murphy K.P., Freeman W.T. Using the Forest to See the Trees: Exploiting Context for Visual Object Detection and Localization.Communications of the ACM volume 53, Issue 3, 2012.

[6] Wu C., Kuo Y., Hsu W. Large-Scale Simultaneous Multi-Object Recognition and Localization via Bottom Up Search-Based Approach.MM '12: Proceedings of the 20th ACM international conference on Multimedia, 2012.

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