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Joint Histogram-Based Cost Aggregation for Stereo Matching
Oct. 2013 (vol. 35 no. 10)
pp. 2539-2545
Dongbo Min, Adv. Digital Sci. Center, Singapore, Singapore
Jiangbo Lu, Adv. Digital Sci. Center, Singapore, Singapore
M. N. Do, 115 Coordinated Sci. Lab., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
This paper presents a novel method for performing efficient cost aggregation in stereo matching. The cost aggregation problem is reformulated from the perspective of a histogram, giving us the potential to reduce the complexity of the cost aggregation in stereo matching significantly. Differently from previous methods which have tried to reduce the complexity in terms of the size of an image and a matching window, our approach focuses on reducing the computational redundancy that exists among the search range, caused by a repeated filtering for all the hypotheses. Moreover, we also reduce the complexity of the window-based filtering through an efficient sampling scheme inside the matching window. The tradeoff between accuracy and complexity is extensively investigated by varying the parameters used in the proposed method. Experimental results show that the proposed method provides high-quality disparity maps with low complexity and outperforms existing local methods. This paper also provides new insights into complexity-constrained stereo-matching algorithm design.
Index Terms:
stereo image processing,computational complexity,filtering theory,image matching,image sampling,redundancy,complexity-constrained stereo-matching algorithm design,joint histogram-based cost aggregation,complexity reduction,matching window,computational redundancy reduction,search range,window-based filtering,sampling scheme,high-quality disparity maps,Complexity theory,Joints,Accuracy,Image color analysis,Histograms,Redundancy,joint histogram,Cost aggregation,stereo matching,disparity hypotheses
Citation:
Dongbo Min, Jiangbo Lu, M. N. Do, "Joint Histogram-Based Cost Aggregation for Stereo Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 10, pp. 2539-2545, Oct. 2013, doi:10.1109/TPAMI.2013.15
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