&lt;title&gt;Correlators for rank order shape similarity measurement&lt;/title&gt;

Gregga, Jason B.; Power, Gregory J.; Iftekharuddin, Khan M.

doi:10.1117/12.458407

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Wang et al (2005) proposed a correlation templatematching algorithm for searching target in the estimation position's neighbour in order to get target's accurate position. Gregga et al (2002) discussed to use correlation techniques including the matched filter and phase-only filter for rank order shape similarity measurement. Liu (2005) ferent algorithms in order to find an appropriate filter for grinding forces measurement.…”

Section: Introductionmentioning

confidence: 99%

Empirical modelling of grinding force based on multivariate analysis

Liu

Chen

Wang

et al. 2008

Journal of Materials Processing Technology

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Section: Introductionmentioning

confidence: 99%

Empirical modelling of grinding force based on multivariate analysis

Liu

Chen

Wang

et al. 2008

Journal of Materials Processing Technology

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“…[1][2][3][4] Shape of a desired object can vary from the template for a variety of reasons including noise, blur, and other artifacts in sensors and imaging. Another factor in template matching is object classification.…”

Section: Introductionmentioning

confidence: 99%

Remotely sensed object recognition using multiple comparative measures

Power

2004

Image and Signal Processing for Remote Sensing IX

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In the real world of remote sensing, rarely does the extracted object precisely match a stored template of that object. A certain level of uncertainty must be permitted between the stored template and the extracted object. One solution to deal with this uncertainty is to evaluate measures between the object and template. Many measures have been introduced independently in literature for evaluating the statistical nature of the extracted objects such as a variety of shape and texture measures. The object is measured and compared to similar measures taken from the template. This paper suggests using measures extracted through a joint comparative process of template and object. It also suggests using multiple measures from the joint class of measures as opposed to using an individual measure to determine the sufficiency of the match. In particular, this paper demonstrates the value of using multiple comparative shape measures as opposed to one particular shape measure to achieve confidence in a match. The multiple-shape measure approach uses a matched filter measure, a Procrustes metric, a partial-direct hausdorf measure, and percent-pixels same measure. Each shape measure gives slightly different insight about the shape comparison which allows more confidence in the match. An experimental result is given that demonstrates the implementation and usefulness of the multiple comparative measure approach for recognizing objects from remotely sensed imagery.

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“…[7] [35] where Z(m) is normalized according to Z(m) √ t |z(t)| 2 such that 0.0 is the minimum (least similar) and 1.0 is the maximum (most similar):…”

Section: Identification Of Known Match Rac Pairsmentioning

confidence: 99%

Quantitative assessment of the discrimination potential of class and randomly acquired characteristics for crime scene quality shoeprints

Richetelli¹

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Quantitative assessment of the discrimination potential of class and randomly acquired characteristics for crime scene quality shoeprints Nicole Richetelli, B.A. Footwear evidence has tremendous forensic value; it can focus a criminal investigation, link suspects to scenes, help reconstruct a series of events, or otherwise provide information vital to the successful resolution of a case. When considering the specific utility of a linkage, the strength of the connection between the source footwear and an impression left at the scene of a crime varies with the known rarity of the shoeprint itself, which is a function of the class characteristics, as well as the complexity, clarity, and quality of randomly acquired characteristics (RACs) available for analysis. To help elucidate the discrimination potential of footwear as a source of forensic evidence, the aim of this research was threefold. The first (and most time consuming obstacle) of this study was data acquisition. In order to efficiently process footwear exemplar inputs and extract meaningful data, including information about randomly acquired characteristics, a semi-automated image processing chain was developed. To date, 1,000 shoes have been fully processed, yielding a total of 57,426 RACs characterized in terms of position (θ, r, r norm), shape (circle, line/curve, triangle, irregular) and complex perimeter (e.g., Fourier descriptor). A plot of each feature versus position allowed for the creation of a heat map detailing coincidental RAC co-occurrence in position and shape. Results indicate that random chance association is as high as 1:756 for lines/curves and as low as 1:9,571 for triangular-shaped features. However, when a detailed analysis of the RAC's geometry is evaluated, each feature is distinguishable. The second goal of this project was to ascertain the baseline performance of an automated footwear classification algorithm. A brief literature review reveals more than a dozen different approaches to automated shoeprint classification over the last decade. Unfortunately, despite the multitude of options and reports on algorithm inter-comparisons, few studies have assessed accuracy for crime-scene-like prints. To remedy this deficit, this research quantitatively assessed the baseline performance of a single metric, known as Phase Only Correlation (POC), on both high quality and crime-scene-like prints. The objective was to determine the baseline performance for high quality exemplars with high signalto-noise ratios, and then determine the degree to which this performance declined as a function of variations in mixed media (blood and dust), transfer mechanisms (gel lifters), enhancement techniques (digital and chemical) and substrates (ceramic tiles, vinyl tiles, I would first like to acknowledge my chair, Dr. Jacqueline Speir. Thank you for all of your help and inspiration throughout the last two years. I have achieved more than I ever thought possible.

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<title>Correlators for rank order shape similarity measurement</title>

Cited by 5 publications

References 7 publications

Empirical modelling of grinding force based on multivariate analysis

Empirical modelling of grinding force based on multivariate analysis

Remotely sensed object recognition using multiple comparative measures

Quantitative assessment of the discrimination potential of class and randomly acquired characteristics for crime scene quality shoeprints

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