Human Face Recognition

Phase only Correlation



                  Recently the demand of high-accuracy 3D measurement is rapidly growing in a variety of computer vision applications [1]. Existing 3D measurement techniques are classified into two major types —active and passive. In general, active measurement employs structureillumination (structure projection, phase shift, moire topography,etc.) or laser scanning, which is not desirable in many applications.On the other hand, passive 3D measurement techniques based on stereo vision have the advantages of simplicity and applicability,since such techniques require simple instrumentation. However, poor reconstruction quality still remains as a major issue for passive 3D measurement, due to the difficulty in finding accurate correspondence between stereo images [2].The most common stereo correspondence techniques employ Sum of Absolute Differences (SAD) or Sum of Squared Differences (SSD), where corresponding points between stereo images can be obtained by minimizing SAD or SSD in area-based block matching [3, 4]. Although SAD and SSD exhibit low computational cost, a major drawback is their low accuracy. Recently, sub-pixel block matching techniques using SAD and SSD have been investigated [4], but the obtained accuracy is not sufficient in some applications.On the other hand, image matching methods using 2D Phase-Only Correlation (POC) 1 exhibit much better matching performance than the methods using SAD and SSD in general [5, 6, 7]. The authors have already developed POC-based passive 3D measurement  system, whose accuracy is comparable with those of projector-based active 3D measurement systems [8, 9]. A drawback of POC-based approach is its high computational cost in evaluating the 2D POC for correspondence search, which limits the area of applications.Addressing this problem, in this paper, we propose a technique for high-accuracy correspondence search between two rectified images using 1D version of POC. The correspondence search between stereo images can be reduced to 1D search through image rectification. However, conventional approach is to employ block matching with 2D rectangular image blocks for finding the best matching point within 1D search interval. In this paper, on the other hand, we propose the use of 1D POC (instead of 2D block matching) for stereo correspondence search. The use of 1D POC makes possible significant reduction in computational cost without sacrificing reconstruction accuracy, compared with the 2D POC-based approach. Also, the resulting reconstruction accuracy is much higher than those of conventional stereo matching techniques using SAD (Sum of Absolute Differences) and SSD (Sum of Squared Differences) combined with sub-pixel disparity estimation [4]. A set of experiments demonstrate that the stereo vision system employing the proposed technique can measure 3D surfaces of free-form objects with sub-mm accuracy.

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