Lake edge detection using Canny algorithm and Otsu thresholding

Setiawan, Budi; Rusydi, Alfi Nur; Pradityo, Koko

doi:10.1109/isyg.2017.8280676

Cited by 20 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, because of disparate geographical environment and dynamic water conditions, it is impossible to use the same NDWI threshold for all the lakes in all the years. In this research, we used local Otsu method (Otsu, 1979;Setiawan et al, 2017) to dynamically segment NDWI images. Specifically, a Canny edge detection algorithm (Bao et al, 2005) was first used to extract lake shorelines from NDWI images (see the yellow box in Fig.…”

Section: Water Segmentationmentioning

confidence: 99%

Lake area and volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. The Tibetan Plateau, known as "the third pole of the Earth", is a region susceptible to climate change. With little human disturbance, lake storage changes serve as a unique indicator of climate change, but comprehensive lake storage data are rare in the region, especially for the lakes with an area less than 10 km2 which are the most sensitive to environmental changes. In this paper, we completed a census of annual lake volume change for 976 lakes larger than 1 km2 in the endorheic basin of the Tibetan Plateau (EBTP) during 1989–2019 using Landsat imagery and digital terrain models. Validation and comparison with several existing studies indicate that our data are more reliable. Lake volume in the EBTP exhibited a net increase of 193.45 km3 during the time period with an increasing rate of 6.45 km3 year−1. In general, the larger the lake area, the greater the lake volume change, though there are some exceptions. Lakes with an area less than 10 km2 have more severe volume change whether decreasing or increasing. This research complements existing lake studies by providing a comprehensive and long-term lake volume change data for the region. The dataset is available on Zenodo (https://doi.org/10.5281/zenodo.5543615, Wang et al., 2021).

show abstract

Section: Water Segmentationmentioning

confidence: 99%

Lake area and volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Apart from those reasons, we mainly chose OTT because we focused on determining the HAZ by identifying the optimal thresholding that is highly capable of detecting the area and edge of HAZ. In our case, HAZ images need optimal thresholding to avoid erroneous detection of an edge, dark pixels in the background, and an excessive number of pixels for the edge [33]. Therefore, in our case, OTT was the best option because of its performance in detecting edges and the HAZ area properly.…”

Section: Literature Reviewmentioning

confidence: 88%

“…Therefore, in our case, OTT was the best option because of its performance in detecting edges and the HAZ area properly. The equation and demonstration of OTT application has been discussed in literature [33]. The OTT is based on the histogram of grayscale images, the main idea is to maximize the between-class variance (foreground and background region) in the histogram.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Analysis of Heat-Affected Zone of Laser-Cut Heat-Resistant Paper using Otsu Thresholding Technique

Rosnan,

Peifu,

Enomae

et al. 2022

IJACSA

View full text Add to dashboard Cite

Since ancient times, natural fibers have been essential in paper production and packaging fabrication. However, beauty-marring carbonization, or a heat-affected zone (HAZ) generated during the laser cutting process of paper materials led to an intriguing discussion on the possibility of reducing this defect zone. Thus, paper loaded with aluminum hydroxide [Al(OH) 3 ] (AH) was prepared and tested with laser cutting. There were two input parameters of laser processing: the ratio of laser power to a maximum and the cutting speed. The study discussed the HAZ area of the paper with AH loaded at 0-40% on a dry pulp basis. The HAZ area was measured through image processing software. The Otsu thresholding technique (OTT) was applied to HAZ area determinations. The results from the image analysis signified that the smallest HAZ area was successfully achieved on samples with AH loaded at 40%. The optimal condition for the sample with 40% AH loaded was 60% power ratio and 20 mm/s in cutting speed. Based on the results, the cutting speed was the most significant parameter to produce the smallest HAZ area; therefore, the laser processing parameters were optimized to achieve a minimum HAZ area, and it was possible to reduce its dark color appearance of the material surfaces. Based on this study, it was found that the application of the Otsu thresholding technique was of significance to the HAZ area determination and reduction of the time consumption for the image analysis.

show abstract

“…The Canny algorithm consists of several steps, described in the form of a flowchart as follows [16]: The magnitude of the gradients magnitude is determined by the equation:…”

Section: Canny Edge Detectionmentioning

confidence: 99%

Implementation of CBIR Method for Identification of Corn Disease Using Extreme Learning Machine

Purnomo¹,

Mudjirahardjo²,

Pramono³

2022

IJCATR

View full text Add to dashboard Cite

Various artificial neural network methods and digital image processing techniques have been applied in agriculture. Previous researchers have proposed methods for identification or detection of plant diseases, such as using K-NN, support vector machine (SVM), backpropagation and so on. The disease attack can reduce the amount of agricultural productivity and result in other material losses for farmers. Plant diseases can be observed from the physical and color changes of the affected parts such as roots, stems and leaves. However, with the development of computer vision technology, observations can be assisted by image processing methods and artificial neural networks, for automation in agriculture. This study proposes a method of applying digital image processing with artificial neural networks to identify corn plant diseases. The purpose of this research is as a comparative study of the proposed method with other conventional methods. The methods used are content based image retrieval (CBIR) and extreme learning machine (ELM). Content based image retrieval (CBIR) is the process of searching for an image in a database by comparing the features in the query image with the features that have been stored in the image database. Meanwhile, extreme learning machine (ELM) is an artificial neural network with one hidden layer or single hidden layer feedforward neural network (SLFNs). The feature extraction used consists of 3 features, namely color, shape, and texture feature extraction. The dataset consists of 3 types of images, namely images of healthy leaves, leaf spot disease, and rust disease. The ELM artificial neural network serves as a classifier that classifies the types of diseases based on the given feature extraction. The proposed system is able to identify the type of disease based on the pattern that appears on the leaf surface with the highest precision of 0.9922.

show abstract

Lake edge detection using Canny algorithm and Otsu thresholding

Cited by 20 publications

References 6 publications

Lake area and volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019

Lake area and volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019

Image Analysis of Heat-Affected Zone of Laser-Cut Heat-Resistant Paper using Otsu Thresholding Technique

Implementation of CBIR Method for Identification of Corn Disease Using Extreme Learning Machine

Contact Info

Product

Resources

About