The Douglas-peucker algorithm: sufficiency conditions for non-self-intersections

Wu, Shin-Ting; Silva, Adler C. G. da; Marquez, M.R.G.

doi:10.1590/s0104-65002004000100006

Cited by 26 publications

(12 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A polyline is created using the input dataset coordinates as polyline vertexes, from which a tolerance distance or an idealized number of points can be predefined. The algorithm strategy recursively creates new segments approximating the original polyline, until all vertices of the polyline satisfy the predefined tolerance condition [39]. Both sampling design approaches were further evaluated to assess kriging interpolation accuracy metrics using an external validation subset as further detailed in the next section.…”

Section: Em38-mk2 Data Filtering and External Validationmentioning

confidence: 99%

Finding Suitable Transect Spacing and Sampling Designs for Accurate Soil ECa Mapping from EM38-MK2

et al. 2020

View full text Add to dashboard Cite

Finding an ideal sampling design is a crucial stage in detailed soil mapping to assure reasonable accuracy of resulting soil property maps. This study aimed to evaluate the influence of sampling designs and sample sizes on the quality of soil apparent electrical conductivity (ECa) maps from an electromagnetic sensor survey. Twenty-six (26) parallel transects were gathered in a 72-ha plot in Southeastern Brazil. Soil ECa measurements using an on-the-go electromagnetic induction sensor were taken every second using sensor vertical orientation. Two approaches were used to reduce the sample size and simulate kriging interpolations of soil ECa. Firstly, the number of transect lines was reduced by increasing the distance between them; thus, 26 transects with 40 m spacing; 13 with 80 m; 7 with 150 m; and 4 with 300 m. Secondly, random point selection and Douglas-Peucker algorithms were used to derive four reduced datasets by removing 25, 50, 75, and 95% of the points from the ECa survey dataset. Soil ECa was interpolated at 5 m output spatial resolution using ordinary kriging and the four datasets from each simulation (a total of twelve datasets). Map uncertainty was assessed by root mean square error and mean error metrics from 400 random samples previously selected for external map validation. Maps were evaluated on their uncertainty and spatial structure of variation. The transect elimination approach showed that maps produced with transect spacing up to 150 m could preserve the spatial structure of ECa variations. Douglas-Peucker results showed lower nugget values than random point simulations for all selected sample densities, except for a 95% point reduction. The soil ECa maps derived from the 75% reduced dataset (by random sampling or Douglas-Peucker) or from 13 transect lines (80 m spacing) showed reasonable accuracy (RMSE of validation circa 0.7) relative to the map interpolated from all survey points (RMSE of 0.5), suggesting that transect spacing of 80 m and reading intervals greater than one second can be used for improving the efficiency of on-the-go soil ECa surveys.

show abstract

Section: Em38-mk2 Data Filtering and External Validationmentioning

confidence: 99%

Finding Suitable Transect Spacing and Sampling Designs for Accurate Soil ECa Mapping from EM38-MK2

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It reduces the number of curve in an image and transform it to a straight line between two points. As a result, the contour of object can be identified easier [29]. If the number of contours is 4, then the object is classified as a rectangle object.…”

Section: Shape Segmentationmentioning

confidence: 99%

Traffic sign detection optimization using color and shape segmentation as pre-processing system

Handoko¹,

Pratama²,

Yohanes³

2021

TELKOMNIKA

View full text Add to dashboard Cite

One of performance indicators in an autonomous vehicle (AV) is its ability to accommodate rapid environment changing; and performance of traffic sign detection (TSD) system is one of them. A low frame rate of TSD impacts to late decision making and may cause to a fatal accident. Meanwhile, adding any GPU to TSD will significantly increases its cost and make it unaffordable. This paper proposed a pre-processing system for TSD which implement a color and a shape segmentation to increase the system speed. These segmentation systems filter input frames such that the number of frames sent to artificial intelligence (AI) system is reduced. As a result, workload of AI system is decreased and its frame rate increases. HSV threshold is used in color segmentation to filter frames with no desired color. This algorithm ignores the saturation when performing color detection. Further, an edge detection feature is employed in shape segmentation to count the total contours of an object. Using German traffic sign recognition benchmark dataset as model, the pre-processing system filters 97% of frames with no traffic sign objects and has an accuracy of 88%. TSD system proposed allows a frame rate improvement up to 32 frame per second (FPS) when You Only Look Once (YOLO) algorithm is used. This is an open access article under the CC BY-SA license.

show abstract

“…After the prepossessing of the eye image, the visible eyeball area is considered as an ellipse. To identify the outermost border of this ellipse border following an algorithm by Suzuki and Abe [45] is used and then Douglas-Peucker approximation algorithm [46] is utilized to reduce the number of points in the curve. Then, the center (x,ȳ) of the ellipse is estimated using moments [47] and is used to indicate the center of the eye.…”

Section: ) Estimation Of Gaze Directionmentioning

confidence: 99%

Active Vision-Based Attention Monitoring System for Non-Distracted Driving

et al. 2021

View full text Add to dashboard Cite

Inattentive driving is a key reason of road mishaps causing more deaths than speeding or drunk driving. Research efforts have been made to monitor drivers' attentional states and provide support to drivers. Both invasive and non-invasive methods have been applied to track driver's attentional states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort. In this paper, a vision-based scheme is proposed for monitoring the attentional states of the drivers. The system comprises four major modules such as cue extraction and parameter estimation, monitoring and decision making, level of attention estimation, and alert system. The system estimates the attentional level and classifies the attentional states based on the percentage of eyelid closure over time (PERCLOS), the frequency of yawning and gaze direction. Various experiments were conducted with human participants to assess the performance of the suggested scheme, which demonstrates the system's effectiveness with 92% accuracy.

show abstract

The Douglas-peucker algorithm: sufficiency conditions for non-self-intersections

Cited by 26 publications

References 6 publications

Finding Suitable Transect Spacing and Sampling Designs for Accurate Soil ECa Mapping from EM38-MK2

Finding Suitable Transect Spacing and Sampling Designs for Accurate Soil ECa Mapping from EM38-MK2

Traffic sign detection optimization using color and shape segmentation as pre-processing system

Active Vision-Based Attention Monitoring System for Non-Distracted Driving

Contact Info

Product

Resources

About