A visual basic program for ridge axis picking on DEM data using the profile-recognition and polygon-breaking algorithm

Chang, Yet-Chung; Sinha, Gaurav

doi:10.1016/j.cageo.2006.06.007

Cited by 23 publications

(21 citation statements)

References 15 publications

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“…Ridge detection is based on the profile-recognition and polygon-breaking algorithm (PPA) by Chang and Sinha [28]. The PPA algorithm marks each terrain point that is likely to be on a ridge line, based on the point height profile.…”

Section: Feature Detection: Silhouettes and Ridgelinesmentioning

confidence: 99%

Feature-based terrain editing from complex sketches

Tasse

Emilien

Cani

et al. 2014

Computers & Graphics

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International audienceWe present a new method for first person sketch-based editing of terrain models. As in usual artistic pictures, the input sketchdepicts complex silhouettes with cusps and T-junctions, which typically correspond to non-planar curves in 3D. After analysingdepth constraints in the sketch based on perceptual cues, our method best matches the sketched silhouettes with silhouettes or ridgesof the input terrain. A deformation algorithm is then applied to the terrain, enabling it to exactly match the sketch from the givenperspective view, while insuring that none of the user-defined silhouettes is hidden by another part of the terrain. We extend thissketch-based terrain editing framework to handle a collection of multi-view sketches. As our results show, this method enablesusers to easily personalize an existing terrain, while preserving its plausibility and style

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Section: Feature Detection: Silhouettes and Ridgelinesmentioning

confidence: 99%

Feature-based terrain editing from complex sketches

Tasse

Emilien

Cani

et al. 2014

Computers & Graphics

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“…With the continuous development of visual inspection technology, visual recognition system for conveyor belt detection is becoming greatly prominent because it has noncontact and realtime characteristics [6,7]. Specifically, technological development can be divided into two aspects -algorithm and equipment.…”

Section: Introductionmentioning

confidence: 99%

Research on conditional characteristics vision real‐time detection system for conveyor belt longitudinal tear

Qiao

Pang

et al. 2017

IET Science, Measurement & Technology

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Abstract:Conveyor belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of conveyor belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and conveyor belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of conveyor belt, thus the problem of starting warning only based on single feature can be effectively avoided.

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“…So far, mainstream methods for terrain feature line extraction can be divided into two categories: water flow simulation-based methods [1,[4][5] and geometrical morphological analysis-based methods [6][7][8][9][10][11]. The former conduct water flow simulation on terrain surfaces, in which course flow direction matrix and flow accumulation matrix are constructed, then valley lines are formed by concatenating feature points whose flow accumulation amount is above a predefined threshold, and ridge lines are obtained by extracting watershed boundaries.…”

Section: Introductionmentioning

confidence: 99%

“…Wood [7] determines whether a candidate is a ridge or valley point by examining extreme points of the second derivative. Chang et al [8][9] put forward a method called PPA, which lowers the criteria for ridge candidates to have at least one lower neighbor on both sides, and conducts feature connection, polygon breaking, pruning and smoothing to accomplish feature line extraction. Zhang et al [10] adapt PPA to improve the accuracy and speed of feature extraction.…”

Section: Introductionmentioning

confidence: 99%