Sub-Pixel Estimation Error Cancellation on Area-Based Matching

Abstract

Area-based image matching and sub-pixel displacement estimation using similarity measures are common methods that are used in various fields. Sub-pixel estimation using parabola fitting over three points with their similarity measures is also a common method to increase the matching resolution. However, few investigations or studies have explored the characteristics of this estimation.This study 1 analyzed sub-pixel estimation error using two different types of matching model. Our analysis demonstrates that the estimation contains a systematic error depending on image characteristics, the similarity function, and the fitting function. This error causes some inherently problematic phenomena such as the so-called pixel-locking effect, by which the estimated positions tend to be biased toward integer values. We also show that there are good combinations of the similarity functions and fitting functions.In addition, we propose a new algorithm to greatly reduce sub-pixel estimation error. This method is independent of the similarity measure and the fitting function. Moreover, it is quite simple to implement. The advantage of our novel method is confirmed through experiments using different types of images.

A high-accuracy stereo correspondence technique using 1D band-limited phase-only correlation

“…In this section, we evaluate the performance of the proposed technique in terms of computational cost and accuracy. We compare four different correspondence techniques using (i) 1D BLPOC (proposed), (ii) 2D POC [4], (iii) SAD [2] and (iv) SSD [2], where each technique is equipped with sub-pixel stereo matching capability. …”

“…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 [1,2]. Although SAD and SSD exhibit low computational cost, a major drawback is their low accuracy.…”

Stereo one-shot six-band camera system for accurate color reproduction

“…Although the two cameras take images of the same target object, the color balance between the two images is quite different because of the interference filter mounted in front of lens of one camera. General detection methods 12,13 cannot work well in such a case. To find corresponding points between a stereo image pair, we use a subpixel correspondence matching technique that combines local block matching by the phase-only correlation (POC) method and the coarse-to-fine strategy based on pyramid representation.…”

Performance evaluation using Mandelbrot images for image registration algorithms

“…The feature-based algorithms employ feature points on images to align images, e.g., SIFT (Scale Invariant Feature Transform) [2]. On the other hand, the areabased algorithms employ similarity (or dissimilarity) measures between images, e.g., SAD (Sum of Absolute Differences), SSD (Sum of Squared Differences) [3,4], and Phase-Only Correlation (POC) [5,6,7].…”