Improved Morphological Band-Pass Filtering Algorithm and Its Application in Circle Detection

Xian, Wang; Zhao, Qiancheng; Tan, Jianping

doi:10.1155/2018/3765164

Cited by 3 publications

(2 citation statements)

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“…It was impossible to accurately locate the target during the experiment. To quantitatively evaluate the tracking accuracy, the accuracy evaluation system proposed in Hoover et al ( 1996 ) is adopted in this study to manually capture the target edge and determine the position of the target centre (Wang et al, 2018 ) using the least square method as the true value. The tracking error is then identified as the distance between the centre position of the target obtained by each tracking method and the true value.…”

Section: Experiments and Analysismentioning

confidence: 99%

Introducing Depth Information Into Generative Target Tracking

et al. 2021

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Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.

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Section: Experiments and Analysismentioning

confidence: 99%

Introducing Depth Information Into Generative Target Tracking

et al. 2021

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“…Dilation is a morphological operation of the image processing which increases the size of objects by using a structuring element as an input [46]. In [47], a method based on this technique is proposed to improve image recognition smoothing regions of the image.…”

Section: ) Image Dilationmentioning

confidence: 99%

Bullet Impact Detection in Silhouettes Using Mask R-CNN

Vilchez

Mauricio

2020

IEEE Access

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The bullet impact detection in the silhouette plays an important role in the institutions which have a shooting range, specially where are regulated the license to carry a gun and where the evaluation manual process could present distortion in the ability's evaluation of the shooter and like a consequence the license's issuance. This paper proposes a method for an automatic detection of the bullet impacts in silhouettes based on deep learning and image processing, which consist of the following steps: preprocessing, impacts detection, edge detection and evaluation results. The experiments about 600 silhouettes with 2401 bullet impacts images of the proposed and implemented method considering the models Resnet 50 and Resnet 101 for Mask R-CNN show that Resnet 50 get better results than Resnet 101, achieving 97.6 %, 99.5 %, and 97.9 %, of accuracy, precision and recall, respectively, above the methods Circular Hough Transform, Circlet Detection, Random Sample Consensus, Randomized Hough Transform, Randomized Circle Detection, Support Vector Machine, Faster R-CNN, MnasNet and YOLO. Also, the results show 100 % of effectiveness in the edge detection and the count of the detected bullet impacts.INDEX TERMS Edge detection, bullet impact detection, image processing, Mask R-CNN.

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