Research on image enhancement of light stripe based on template matching

Liu, Siyuan; Bao, Haojing; Zhang, Yunhui; Lian, Fenghui; Zhang, Zhihui; Tan, Qing

doi:10.1186/s13640-018-0362-y

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The main goal of this stage is to extract features by separately exploring different regions of the original image I . The idea is similar to the methods presented in [17], [18]. It allows us to easily eliminate the parts of the image that do not contain the flame.…”

Section: B Horizontal and Vertical Image Stripesmentioning

confidence: 99%

Unsupervised Method for Wildfire Flame Segmentation and Detection

Buza

Akagić

2022

IEEE Access

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In the last decade, there have been many reports on the negative impact of wildfires on various ecosystems. Unfortunately, wildfires have been intensifying as global temperatures, droughts, and other instances of extreme weather events rise around the world. These circumstances are forcing communities to vigorously address the uncontrolled spread of wildfires, where the ultimate goal is the protection of wildlife. At the same time, many disaster prevention and monitoring methods, based on image processing and computer vision, have been developed. In this paper, we present a new unsupervised method based on RGB color space for the early detection of wildfires from still images. From the analysis of existing state-ofthe-art methods, it is evident that different methods explore different color spaces for the extraction of flame features. Our motivation was to use only RGB color space and thus eliminate the time-consuming task of color space conversion. The proposed method consists of several new image processing techniques used to efficiently extract flame features. It outperforms the existing methods, where an increase of 3% and 2% is recorded in the F1 score and Matthews correlation coefficient, respectively. Such performance demonstrates the merits of the proposed method for flame segmentation and detection.

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Section: B Horizontal and Vertical Image Stripesmentioning

confidence: 99%

Unsupervised Method for Wildfire Flame Segmentation and Detection

Buza

Akagić

2022

IEEE Access

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“…In the work by Du et al [15], a robust laser stripe extraction method that uses ridge segmentation and region ranking was proposed; however, ridge direction cannot be computed for saturated regions of the stripe, which may occur in rail images. In the method proposed by Liu et al [16], template matching is utilized to enhance the light stripe image. In this method, the grayscale distribution of the normal cross-section of the corresponding position of a good-quality light stripe image is used as a template for poorquality light stripe images.…”

Section: Introductionmentioning

confidence: 99%

Robust Analysis and Laser Stripe Center Extraction for Rail Images

Gao

2021

Applied Sciences

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In this paper, a novel method for the effective extraction of the light stripes in rail images is proposed. First, a preprocessing procedure that includes self-adaptive threshold segmentation and brightness enhancement is adopted to improve the quality of the rail image. Secondly, center of mass is utilized to detect the center point of each row of the image. Then, to speed up the procedure of centerline optimization, the detected center-points are segmented into several parts based on the geometry of the rail profile. Finally, piecewise fitting is adopted to obtain a smooth and robust centerline. The performance of this method is analyzed in detail, and experimental results show that the proposed method works well for rail images.

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