Autonomous image to model registration

Chao, Alan; Morgan, Daniel R.

doi:10.1109/icif.2003.177518

Cited by 6 publications

(4 citation statements)

References 2 publications

(1 reference statement)

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“…The first experiment used the same modality image pairs (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12) to test the algorithm's ability to identify the correct registration between two identical images with no transformation. The results for this test using ENVI (Fig.…”

Section: Results Using Itt Envimentioning

confidence: 99%

“…A number of methods for identifying the global optimum have been proposed in the literature, including restricted exhaustive searches, hill climbing, simulated annealing [17,18,15], genetic algorithms [6], hierarchical grid [7] and other specialist search methods attempting to create a function representing the surface which can be used to guide the search direction [39,8,23]. Exhaustive searches are very time consuming although guarantee to identify the global optimum.…”

Section: Introductionmentioning

confidence: 99%

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A multi-resolution area-based technique for automatic multi-modal image registration

Bunting

Labrosse

Lucas

2010

Image and Vision Computing

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Section: Results Using Itt Envimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-resolution area-based technique for automatic multi-modal image registration

Bunting

Labrosse

Lucas

2010

Image and Vision Computing

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“…This section presents an overview of the multisensor registration algorithm presented in [5,6]. The registration algorithm framework supports many different feature types, transformation types, and feature matcher types.…”

Section: Multisensor Registration Algorithm Descriptionmentioning

confidence: 99%

Automatic parameter selection for feature-based multi-sensor image registration

DelMarco

Tom

Webb

et al. 2006

Signal Processing, Sensor Fusion, and Target Recognition XV

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Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for featurebased registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

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“…The search chooses optimal parameter values by maximizing a suitable gain function (or minimizing a cost function). Parameter space search approaches have been used before for image alignment [39][40][41][42][43]. The three step approach consists of 1) transforming the test image according to a chosen geometric transform hypothesis; 2) evaluating the match between the transformed test image and reference image; 3) selecting the next transformation hypothesis.…”

Section: Introductionmentioning

confidence: 99%

Search algorithm complexity modeling with application to image alignment and matching

DelMarco

2014

SPIE Proceedings

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Search algorithm complexity modeling, in the form of penetration rate estimation, provides a useful way to estimate search efficiency in application domains which involve searching over a hypothesis space of reference templates or models, as in model-based object recognition, automatic target recognition, and biometric recognition. The penetration rate quantifies the expected portion of the database that must be searched, and is useful for estimating search algorithm computational requirements. In this paper we perform mathematical modeling to derive general equations for penetration rate estimates that are applicable to a wide range of recognition problems. We extend previous penetration rate analyses to use more general probabilistic modeling assumptions. In particular we provide penetration rate equations within the framework of a model-based image alignment application domain in which a prioritized hierarchical grid search is used to rank subspace bins based on matching probability. We derive general equations, and provide special cases based on simplifying assumptions. We show how previously-derived penetration rate equations are special cases of the general formulation. We apply the analysis to model-based logo image alignment in which a hierarchical grid search is used over a geometric misalignment transform hypothesis space. We present numerical results validating the modeling assumptions and derived formulation.

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Autonomous image to model registration

Abstract: -

Cited by 6 publications

References 2 publications

A multi-resolution area-based technique for automatic multi-modal image registration

A multi-resolution area-based technique for automatic multi-modal image registration

Automatic parameter selection for feature-based multi-sensor image registration

Search algorithm complexity modeling with application to image alignment and matching

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