Machine Vision Algorithms

Steger, Carsten

doi:10.1002/9783527413409.ch9

Cited by 13 publications

(22 citation statements)

References 96 publications

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“…Overall, the survey of methods in this review is extensive, but it does not claim to be complete. The description and contextualization of most methods is based upon [88][89][90][91]. Due to the small amount of case studies on linear feature extraction that employ high resolution sensors of <0.5 m, one group of the described table includes case studies on resolutions of up to 5 m, whereas the other includes the remaining case studies.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step-including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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“…The instrument consists of a stirring component (5 in Fig.1), liquid removal parts (3, 4), titration units (7,8), step-rotation parts (11,12), image acquisition units (9, 10) and processor (13). The operation of stirring component, liquid removal part, titration unit, step-rotation and image acquisition parts are controlled by the program control module and the image processing module in the processor.…”

Section: Hardware Constructionmentioning

confidence: 99%

A rapid automatic analyzer and its methodology for effective bentonite content based on image recognition technology

Long

Wang

2016

China Foundry

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“…The morphological dilation can be considered using a transpose structural element in Minkowski Addition. The Minkowski addition can be defined as Eq.2 shows [4] :…”

Section: Run Length Encoding-morphological Dilation Optimized Algorithmmentioning

confidence: 99%

Application of Run Length Encoding In Track Vision Inspection System

Kang¹

2016

Proceedings of the 2016 International Forum on Management, Education and Information Technology Application

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Abstract. Optimized the binary image morphological dilation algorithm by run length encoding with the optimization features in background redundant and intersection and union simple operation, the Run Length Encoding-Morphological Dilation optimized algorithm had been proposed. The track contour binary image was processed by morphological dilation with the optimized algorithm. Compared analysis based on the image processing result and the speed before and after optimization. The edge burr and disconnection phenomenon can be removed by the optimized algorithm. The processing result of the track contour single frame image increased 5 times. The detection accuracy of the track visual inspection system can be improved to 20 points per meter.

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Machine Vision Algorithms

Cited by 13 publications

References 96 publications

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

A rapid automatic analyzer and its methodology for effective bentonite content based on image recognition technology

Application of Run Length Encoding In Track Vision Inspection System

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