An improved method for image thresholding based on the valley-emphasis method

Ng, Hui-Fuang; Jargalsaikhan, Davaajargal; Tsai, Hwei-Fang; Lin, Chih-Yang

doi:10.1109/apsipa.2013.6694261

Cited by 12 publications

(21 citation statements)

References 9 publications

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“…A fixed threshold value cannot satisfy all scenarios; thus, a better approach is to set the threshold automatically depending on the images being compared. The Gaussian valley emphasis (VE) method proposed in [30] is used, generating a CM with only a few "noise changes" while retaining the "crack change", as observed in Figure 7. The standard deviation of the Gaussian window was empirically set to σ = 5.…”

Section: Image Differencementioning

confidence: 99%

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Attard

Debono

Valentino

et al. 2021

Sensors

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Tunnel structural health inspections are predominantly done through periodic visual observations, requiring humans to be physically present on-site, possibly exposing them to hazardous environments. These surveys are subjective (relying on the surveyor experience), time-consuming, and may demand operation shutdown. These issues can be mitigated through accurate automatic monitoring and inspection systems. In this work, we propose a remotely operated machine vision change detection application to improve the structural health monitoring of tunnels. The vision-based sensing system acquires the data from a rig of cameras hosted on a robotic platform that is driven parallel to the tunnel walls. These data are then pre-processed using image processing and deep learning techniques to reduce nuisance changes caused by light variations. Image fusion techniques are then applied to identify the changes occurring in the tunnel structure. Different pixel-based change detection approaches are used to generate temporal change maps. Decision-level fusion methods are then used to combine these change maps to obtain a more reliable detection of the changes that occur between surveys. A quantitative analysis of the results achieved shows that the proposed change detection system achieved a recall value of 81%, a precision value of 93% and an F1-score of 86.7%.

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Section: Image Differencementioning

confidence: 99%

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Attard

Debono

Valentino

et al. 2021

Sensors

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“…Among these, for our comparative analysis, we selected visual identification of the threshold guided by the Valley Emphasis criterion, the Otsu method, and the unsupervised classification method based on K-Means Clustering. The first method consisted of visually inspecting a grayscale histogram of each SAR image and applying the Valley Emphasis criterion [58][59][60]. For the application of this criterion, we first analyzed the grayscale histogram in SNAP and, subsequently, chose the minimum backscatter intensity value that corresponded to the valley between the two peaks, in the case of bimodal distribution, or to the bottom rim of a single peak for a unimodal distribution.…”

Section: Extraction Of the Water Body Areamentioning

confidence: 99%

Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery

Ruzza

Guerriero

Grelle

et al. 2019

Water

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Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time.

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“…Hence, optimal image segmentation is a crucial step in image preconditioning for further analysis because it precedes processing stages such as object extraction, parameter measurement, and object recognition [9,15]. Specifically, the thresholding methods are the most widely utilized in image segmentation due to their simplicity and effectiveness [9,11,[15][16][17]. In layman's terms, these methods aim to separate the image foreground from its background by finding a limit or threshold in the image histogram.…”

Section: Introductionmentioning

confidence: 99%

“…As usual, in the healthy development of computer science procedures, these techniques have disadvantages, so improved versions have appeared. For example, those that enhance the Otsu algorithm performance include e.g., the Valley Emphasis [17,25], Fan-Lei [26] and Xing-Yang methods [27]. These algorithms are suitable when the gray level histogram exhibits an evident bimodal behavior, and the optimal threshold is located at the valley bottom [28].…”

Section: Introductionmentioning

confidence: 99%

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Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

et al. 2021

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Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

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An improved method for image thresholding based on the valley-emphasis method

Cited by 12 publications

References 9 publications

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

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