The general problem of precise position estimation is analyzed in the context of quantization errors. A formalism referred to as the locales approach to geometric precision is reviewed. A lower bound on position estimation uncertainty due to quantization is discussed. Some simulation results for an optimal position estimation algorithm, referred to as position decoding, are presented. A heuristic development of an image geometric fidelity measure is presented.

The Analysis Of Precision In Digital Image Metrology

The author examines both theoretically and through a simulation study the feasibility of identifying the location within a large reference gray-level array of a smaller sensed array to an accuracy finer than one pixel. It is assumed that the sensed image before discretization into pixels consists of a translated, but not rotated, section of the reference image with some superposed noise. The theoretical and empirical results show that when the noise has standard deviation no larger than that of a realistic reference field, the upper quartile of the registration error is on the order of 0.25-0.5 pixels. >

Subpixel translation-registration of random fields

World Directory of Robotics Research and Development Activities

A closed-form expression has been reported in the literature for L/sub N/, the number of digital line segments of length N that correspond to lines of the form y=ax+ beta , O >

/pdf/on-the-number-of-digital-straight-line-segments-2h5swpe906.pdf

On the number of digital straight line segments

A method for estimating the translation offset between a pair of images to subpixel accuracy is described and analyzed This method assumes the extraction of a digital edge representing a straight line in the image and then computes the best real line which could have given rise to the digital line The key difference between this method and other approaches to subpixel fitting is that our method uses subpixel line location to estimate a digital model such as the digital line and then fits the digital model to the image while other approaches directly fit a continuous model to the image or an image related object such as a cross correlation matrix The present paper lays a theoretical foundation for this digital approach This technique can be applied in a variety of remote sensing and industrial vision problems requiring subpixel registration accuracy

A geometric approach to subpixel registration accuracy

The area of subpixel accuracy in image registration and edge detection was studied. Two main directions of research were pursued, edge detection and matching based on the digital geometry of edges, and random field models for probablistic analysis of registration error. In the edge detection approach, error bounds and error probabilities were computed using theoretical models. Algorithms were developed and tests on simulated imagery. The methods appear promising for high accuracy edge position estimation and registration, though further refinement of the procedures is required. Using random field models, a statistical measure of the quality of the cross correlation peak as an estimate of the offset between a sensed and a reference image was developed. Simulations were performed to determine the validity of this estimte with real imagery and to study the results of interpolating digital correlation functions to estimate the translation offset to subpixel accuracy.

Analysis of subpixel registration

Geometric and probabilistic models for subpixel accuracy are developed. The geometric models bound the error in offset estimation using the pixels in an observed digital straight line. One probabilistic model bounds the estimate of error offset for continuous images. The other model bounds the error for discrete images given that one is in the correct pixel.

Analysis of Subpixel Registration Accuracy

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Analysis of subpixel registration

Analysis of Subpixel Registration Accuracy