Certain Applications and Case Studies of Evolutionary Computing Techniques for Image Processing

2017 ◽  
pp. 273-296
Author(s):  
A. Vasuki
Keyword(s):  
Image Processing ◽  
Case Studies ◽  
Evolutionary Computing

scholarly journals Overlay Videos for Quick and Accurate Watermark Identification, Comparison, and Matching: Creating and Using Overlay Videos

2021 ◽  
Author(s):  
C. Richard Johnson Jr. ◽  
William Sethares ◽  
Margaret Holben Ellis
Keyword(s):  
Image Processing ◽  
Case Studies ◽  
Digital Image Processing ◽  
Digital Image ◽  
Art History ◽  
Image Manipulation ◽  
Historical Documents ◽  
Identification Procedure ◽  
Processing Software ◽  
Image Processing Software

Identifying, comparing, and matching watermarks in pre-machine-made papers has occupied scholars of prints and drawings for some time. One popular but arduous approach is to overlay, either manually or digitally, an image of the watermark in question with its presumed match from a known source. For example, a newly discovered watermark in a Rembrandt print might be compared to a similar one reproduced in Erik Hinterding’s Rembrandt as an Etcher (2006). Such an overlay can confirm the pair as identical, i.e., as moldmates, or reveal their differences. But creating an accurate overlay for two images with different scales, orientations, or resolutions using standard image-manipulation tools can be time consuming and, ultimately, unsuccessful. Part One of this article describes advances in the emerging field of computational art history, specifically the development of digital image processing software, that can be used to semi-automatically create a reliable animated overlay of two watermarks, regardless of their relative “comparability.” Watermarks found in the prints of Rembrandt van Rijn (1606–1669) are used in three case studies to demonstrate the efficacy of user-generated overlay videos. Part Two discusses how searching for identical watermarks, i.e., moldmates, can be enhanced through the application of a new suite of software programs that exploit the data calculated during the creation of user-generated animated overlays. This novel watermark identification procedure allows for rapid, confident watermark searches with minimal user effort, given the existence of a pre-marked library of watermarks. Using a pre-marked library of Foolscap with Five-Pointed Collar watermarks, four case studies present different categories of previously undocumented matches 1) among Rembrandt’s prints; 2) between prints by Rembrandt and another artist, in this case Jan Gillisz van Vliet (1600/10–1668); and 3) between selected Rembrandt prints and contemporaneous Dutch historical documents.

Case Studies of the Use of Image Processing in Metrology and Failure Analysis

2005 ◽  
Author(s):  
Kartik Ramanujachar
Keyword(s):  
Image Processing ◽  
Failure Analysis ◽  
Case Studies ◽  
Nearest Neighbor ◽  
Image Data ◽  
Cumulative Distribution ◽  
Information Theoretic ◽  
Atomic Force ◽  
Processing Techniques

Abstract This paper describes the use of image processing techniques in metrology and failure analysis with the help of three case studies. The first study concerns a technique that significantly automates the process and hence enables both a rapid and accurate extraction of cumulative distribution function for transistor CD through the use of edge detection and quantification of image intensities. The second study is about utilizing a cross correlation algorithm and an appropriately chosen sample and image to estimate the "on image" spatial resolution of an scanning electron microscope. The last case study uses image data acquired with an atomic force microscope. The paper describes how information theoretic concepts like entropy and mutual information combined with image segmentation and nearest neighbor extraction can be used to isolate those regions of the AFM scan that can potentially benefit from further analysis.

Computation on Stochastic Bit Streams Digital Image Processing Case Studies

2014 ◽  
Vol 22 (3) ◽  
pp. 449-462 ◽  
Author(s):  
Peng Li ◽  
David J. Lilja ◽  
Weikang Qian ◽  
Kia Bazargan ◽  
Marc D. Riedel
Keyword(s):  
Image Processing ◽  
Case Studies ◽  
Digital Image Processing ◽  
Digital Image

Evolutionary Computing Approaches to System Identification

2016 ◽  
pp. 329-368
Author(s):  
Bidyadhar Subudhi ◽  
Debashisha Jena
Keyword(s):  
System Identification ◽  
Complex Systems ◽  
Evolutionary Computation ◽  
Case Studies ◽  
Evolutionary Computing ◽  
Identification Problem ◽  
Engineering Problem ◽  
Important Class ◽  
Essential Requirement ◽  
The Past

In this chapter, we describe an important class of engineering problem called system identification which is an essential requirement for obtaining models of system of concern that would be necessary for controlling, analyzing the systems. The system identification problem is essentially to pick up the best model out of the several candidate models. Thus, the problem of system identification or modeling building turns out to be an optimization problem. The chapter explain what are different evolutionary computing techniques used in the past and the state- of the art technologies on evolutionary computation. Then, some case studies have been included how the system identification of a number of complex systems effectively achieved by employing these evolutionary computing techniques.

scholarly journals A spark of 3D revisualization: new method for re-exploring segmented data

2020 ◽  
Author(s):  
Yuzhi Hu ◽  
A. Limaye ◽  
Jing Lu
Keyword(s):  
Image Processing ◽  
Case Studies ◽  
Scientific Visualization ◽  
3D Visualization ◽  
New Method ◽  
Current Image ◽  
Imaging Processing ◽  
Visualization Software ◽  
Non Destructive

Abstract3D scientific visualization is a popular non-destructive investigation tool, however current imaging processing and 3D visualization software has compatibility barriers which make replicability and reproducibility in research difficult. To solve this, we developed a new revisualization method and demonstrated four case studies using three mainstream image processing and 3D visualization software. Our method offers interchangeability amongst current image processing and 3D visualization software.

scholarly journals Medical Image Processing and Analysis Techniques for Detecting Giant Cell Arteritis

2021 ◽  
Author(s):  
Radwan Qasrawi ◽  
Diala Abu Al-Halawa ◽  
Omar Daraghmeh ◽  
Mohammad Hjouj ◽  
Rania Abu Seir
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Giant Cell Arteritis ◽  
Giant Cell ◽  
Case Studies ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Image Processing And Analysis ◽  
Detection And Diagnosis

Medical image segmentation and classification algorithms are commonly used in clinical applications. Several automatic and semiautomatic segmentation methods were used for extracting veins and arteries on transverse and longitudinal medical images. Recently, the use of medical image processing and analysis tools improved giant cell arteries (GCA) detection and diagnosis using patient specific medical imaging. In this chapter, we proposed several image processing and analysis algorithms for detecting and quantifying the GCA from patient medical images. The chapter introduced the connected threshold and region growing segmentation approaches on two case studies with temporal arteritis using ultrasound (US) and magnetic resonance imaging (MRI) imaging modalities extracted from the Radiopedia Dataset. The GCA detection procedure was developed using the 3D Slicer Medical Imaging Interaction software as a fast prototyping open-source framework. GCA detection passes through two main procedures: The pre-processing phase, in which we improve and enhances the quality of an image after removing the noise, irrelevant and unwanted parts of the scanned image by the use of filtering techniques, and contrast enhancement methods; and the processing phase which includes all the steps of processing, which are used for identification, segmentation, measurement, and quantification of GCA. The semi-automatic interaction is involved in the entire segmentation process for finding the segmentation parameters. The results of the two case studies show that the proposed approach managed to detect and quantify the GCA region of interest. Hence, the proposed algorithm is efficient to perform complete, and accurate extraction of temporal arteries. The proposed semi-automatic segmentation method can be used for studies focusing on three-dimensional visualization and volumetric quantification of Giant Cell Arteritis.

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