• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1251-1260
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• 2018

High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on sea-ice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized cross-correlation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.99-102
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• 2022

실재하는 물체나 장소를 디지털 카메라나 휴대폰 카메라로 여러 장 촬영하여 얻은 2차원 이미지 데이터셋으로부터 3차원 영상을 얻기 위해서 이미지를 이루는 각 pixel의 depth 정보를 얻는 것은 필수적인 과정이다. 주어진 이미지에서 depth 정보를 얻기 위해 Shuhan Shen은 PatchMatch 알고리즘을 활용하는 것을 제안하였다. 그 이후 PatchMatch 기반의 알고리즘은 널리 사용되며 우수한 성능을 보이고 있다. PatchMatch 기반의 알고리즘을 사용해 depth를 추정하는 과정에서 depth와 법선 벡터를 Zero-mean Normalized Cross Correlation(ZNCC)를 사용해 평가한다. 하지만, ZNCC는 depth를 평가하려는 pixel의 주변 pixel들의 밝기 값 혹은 색상 값의 분포를 사용하기 때문에 밝기 값이나 색상 값의 변화가 적은 texture-less region에서는 신뢰성이 떨어진다. 본 논문에서는 이 문제를 epipolar geometry를 활용한 기하학적 정보를 이용하여 개선하고자 한다.

• Convergence Security Journal
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• v.13 no.3
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• pp.9-15
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• 2013

Calculating of matching cost is an important for efficient stereo matching. To investigate the performance of matching process, the concepts of the existing methods are introduced. Also we analyze the performance and merits of them. The simplest matching costs assume constant intensities at matching image locations. We consider matching cost functions which can be distinguished between pixel-based and window-based approaches. The Pixel-based approach includes absolute differences (AD) and sampling-intensitive absolute differences (BT). The window-based approach includes the sum of the absolute differences, the sum of squared differences, the normalized cross-correlation, zero-mean normalized cross-correlation, census transform, and the absolute differences census transform (AD-Census). We evaluate matching cost functions in terms of accuracy and time complexity. In terms of the accuracy, AD-Census method shows the lowest matching error ratio (the best solution). The ZNCC method shows the lowest matching error ratio in non-occlusion and all evaluation part. But it performs high matching error ratio at the discontinuities evaluation part due to blurring effect in the boundary. The pixel-based AD method shows a low complexity in terms of time complexity.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.107-116
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• 2015

In compliance with digital device growth, the proliferation of high-tech computers, the availability of high quality and inexpensive video cameras, the demands for automated video analysis is increasing, especially in field of intelligent monitor system, video compression and robot vision. That is why object tracking of computer vision comes into the spotlight. Tracking is the process of locating a moving object over time using a camera. The consideration of object's scale, rotation and shape deformation is the most important thing in robust object tracking. In this paper, we propose a robust object tracking scheme using Random Forest. Specifically, an object detection scheme based on region covariance and ZNCC(zeros mean normalized cross correlation) is adopted for estimating accurate object location. Next, the detected region will be divided into five regions for random forest-based learning. The five regions are verified by random forest. The verified regions are put into the model pool. Finally, the input model is updated for the object location correction when the region does not contain the object. The experiments shows that the proposed method produces better accurate performance with respect to object location than the existing methods.