Fish Stereo Matching using Modified k-Dimensional Tree Nearest Neighbor Search

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI:10.1109/HNICEM54116.2021.9731879

Maria Gemel B. Palconit, Ronnie S. Concepcion, Jonnel D. Alejandrino, E. Sybingco, R. R. Vicerra, A. Bandala, E. Dadios

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引用次数: 2

Abstract

Stereo matching is one of the primary determining factors of 3D reconstruction accuracy. This study proposes a modified k-d tree algorithm to match the detected centroids of the fish in 527 pairs of stereo images. The primary purpose of the modified k-d tree is to eliminate the matching errors caused by the close proximities of the fish as measured by the convex hull. With the fish proximities at not greater than 500 convex hulls, the probability of successful matching is only 5%. Considering all the input image pairs with convex hulls of 0 to 6500, the centroid matching using the conventional k-d tree obtained the maximum precision of S7%. With the implementation of the modified k-d tree, the matching precision has increased to 100%, which means the errors in centroid matching were totally eliminated with all the varying levels of fish proximities.

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基于改进k维树最近邻搜索的鱼类立体匹配

立体匹配是决定三维重建精度的主要因素之一。本文提出了一种改进的k-d树算法，对527对立体图像中检测到的鱼的质心进行匹配。改进的k-d树的主要目的是消除由凸包测量的鱼的接近度引起的匹配误差。当鱼的邻近点不大于500个凸包时，匹配成功的概率仅为5%。考虑所有凸包为0 ~ 6500的输入图像对，使用传统的k-d树进行质心匹配获得了S7%的最大精度。通过改进的k-d树的实现，匹配精度提高到100%，完全消除了在所有不同级别的鱼邻近度下的质心匹配误差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)

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