Structured Edge Detection for Improved Object Localization using the Discriminative Generalized Hough Transform

Gabriel, Eric; Hahmann, Ferdinand; Böer, Gordon; Schramm, Hauke; Meyer, Carsten

doi:10.5220/0005722803930402

Cited by 4 publications

(1 citation statement)

References 29 publications

(29 reference statements)

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“…In the implementation of Canny edge detection, a smoothed image processed by Gaussian filter is used to calculate edge gradient of characteristics by moving a mask, and the gradient image is used as a reference for identifying edge lines. The Hough transform, which is based on the Canny edge detector (hereafter, "HT-Canny"), is useful to detect straight line features [57][58][59]. In Hough transform, the detected edge lines are signified by a standard line model constituting of the detected line slope and length.…”

Section: Water Line Detection and Water Level Calculationmentioning

confidence: 99%

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

et al. 2020

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Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

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Section: Water Line Detection and Water Level Calculationmentioning

confidence: 99%

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

et al. 2020

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Analysis of the Discriminative Generalized Hough Transform for Pedestrian Detection

Gabriel

Schramm

Meyer

2017

Image Analysis and Processing - ICIAP 2017

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Semiautomatic training and evaluation of a learning-based vehicle make and model recognition system

et al. 2018

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Abstract. We describe a system for vehicle make and model recognition (MMR) that automatically detects and classifies the make and model of a car from a live camera mounted above the highway. Vehicles are detected using a histogram of oriented gradient detector and then classified by a convolutional neural network (CNN) incorporating the frontal view of the car. We propose a semiautomatic data-selection approach for the vehicle detector and the classifier, by using an automatic number plate recognition engine to minimize human effort. The resulting classification has a top-1 accuracy of 97.3% for 500 vehicle models. This paper presents a more extensive in-depth evaluation. We evaluate the effect of occlusion and have found that the most informative vehicle region is the grill at the front. Recognition remains accurate when the left or right part of vehicles is occluded. The small fraction of misclassifications mainly originates from errors in the dataset, or from insufficient visual information for specific vehicle models. Comparison of state-of-the-art CNN architectures shows similar performance for the MMR problem, supporting our findings that the classification performance is dominated by the dataset quality.

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Structured Edge Detection for Improved Object Localization using the Discriminative Generalized Hough Transform

Cited by 4 publications

References 29 publications

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Analysis of the Discriminative Generalized Hough Transform for Pedestrian Detection

Semiautomatic training and evaluation of a learning-based vehicle make and model recognition system

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