A survey and examination of subpixel measurement techniques

West, Geoff; Clarke, Timothy A.

doi:10.1117/12.2294301

Cited by 35 publications

(28 citation statements)

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“…Precise displacement estimation between images is much needed in many areas of image processing such as stereo image processing [6], motion estimation [3], target tracking [5,7], image measurement [4], machine vision, remote sensing, image registration [2], and sequential image analysis. Toward this goal, area-based matching is adopted as a fundamental processing in many applications.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional simultaneous subpixel estimation for area-based matching

ShimizuMasao

OkutomiMasatoshi

2005

Syst. Comp. Jpn.

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SUMMARYArea-based matching is a fundamental image processing operation to obtain the displacement between images. Also, the similarity interpolation method to estimate subpixel displacement is commonly used to enhance resolution. Conventionally, similarity interpolation estimation is performed by assuming that the horizontal and vertical displacements are independent. Almost no investigations of estimation error over the conventional method have been made, but it is inferred experientially that subpixel estimation with image interpolation or gradient-based methods is more precise than the similarity interpolation method. This paper proposes a novel 2D subpixel displacement estimation method based on similarity interpolation through modeling of 2D self-similarity. The proposed method requires no "a priori" knowledge of 2D similarity and no images at all. It adopts no iteration. Furthermore, the proposed method entails only slightly higher calculation costs than the conventional similarity interpolation method. The proposed method can obtain more precise estimation than both the conventional similarity interpolation method and the image interpolation method using comparison of subpixel estimation accuracy. An experiment using actual images demonstrates the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Two-dimensional simultaneous subpixel estimation for area-based matching

ShimizuMasao

OkutomiMasatoshi

2005

Syst. Comp. Jpn.

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“…Area-based image matching is a common and basic method that is used in many fields such as motion estimation (Aggarwal and Nandhakumar, 1988), object tracking (Shortis et al, 1994), machine vision (Aghajan et al, 1993), video data coding, stereo image processing (Kanade and Okutomi, 1994), image-based measurement (West and Clarke, 1990), image registration (Irani and Peleg, 1991), remote sensing, and fluidics (Raffel et al, 1998). For estimating sub-pixel displacement, many applications have employed some methods to find a true peak of a similarity function by fitting a parabola or other functions over three indices near its extremum (Frischholz and Spinnler, 1993;Fusiello and Roberto, 2000;Raffel et al, 1998;Tian and Huhns, 1986).…”

Section: Introductionmentioning

confidence: 99%

Sub-Pixel Estimation Error Cancellation on Area-Based Matching

Shimizu

Okutomi

2005

Int J Comput Vision

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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.

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“…A CCURATE estimation of displacement or location of a signal or image is important in many applications of signal and image processing such as time delay estimation [22], target tracking [37], noncontact measurement [2], [41], remote sensing [4], [11], computer vision [1], image registration [8], [39], and so on. In video coding, motion estimation is proven to be very useful for the reduction of temporal redundancy.…”

Section: Introductionmentioning

confidence: 99%

Interpolation-free subpixel motion estimation techniques in DCT domain

Koc¹,

Liu

1998

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Currently existing subpixel motion estimation algorithms require interpolation of interpixel values which undesirably increases the overall complexity and data flow and deteriorates estimation accuracy. In this paper, we develop discrete cosine transform (DCT)-based techniques to estimate subpel motion at different desired subpel levels of accuracy in the DCT domain without interpolation. We show that subpixel motion information is preserved in the DCT of a shifted signal under some condition in the form of pseudophases, and we establish subpel sinusoidal orthogonal principles to extract this information. The proposed subpixel techniques are flexible and scalable in terms of estimation accuracy with very low computational complexity O(N 2 ) compared to O(N 4 ) for the full-search block-matching approach and its subpixel versions. Above all, motion estimation in the DCT domain instead of the spatial domain simplifies the conventional hybrid DCT-based video coder, especially the heavily loaded feedback loop in the conventional design, resulting in a fully DCT-based high-throughput video codec. In addition, the computation of pseudophases is local, and thus a highly parallel architecture is feasible for the DCT-based algorithms. Finally, simulation on video sequences of different characteristics shows comparable performance of the proposed algorithms to block-matching approaches.

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A survey and examination of subpixel measurement techniques

Cited by 35 publications

References 0 publications

Two-dimensional simultaneous subpixel estimation for area-based matching

Two-dimensional simultaneous subpixel estimation for area-based matching

Sub-Pixel Estimation Error Cancellation on Area-Based Matching

Interpolation-free subpixel motion estimation techniques in DCT domain

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