New Aggregation Approaches with HSV to Color Edge Detection

Flores-Vidal, Pablo A.; Gómez, Daniel; Castro, Javier; Montero, Javier

doi:10.1007/s44196-022-00137-x

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…Among them, dark images make it difficult to distinguish water surface colors through video images; therefore, they are also removed and not included in model training. HSV color space can better differentiate images with varying luminance by analyzing the threshold value of the V channel. , Thus, the V channel threshold under HSV conditions is employed to distinguish between good-light and low-light images, with a secondary assessment made on low-light images to differentiate them from dark images. Lastly, the region close to the bridge surface is selected as the ROI, and the good-light images are further divided into good-light images and shadow images by setting the V channel threshold value within the ROI.…”

Section: Methodsmentioning

confidence: 99%

An Approach Based on Video Image Intelligent Recognition for Water Turbidity Monitoring in River

Zhou,

Chen,

Chen

et al. 2024

ACS EST Water

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Water turbidity stands as a pivotal water quality indicator for water quality monitoring, but conventional contact monitoring equipment is expensive and challenging to maintain. The development of a cost-effective, noncontact turbidity measurement method remains paramount for scientific water quality monitoring. In this study, we propose a video image-based model for water turbidity recognition incorporating image regression algorithms. Residual neural networks (ResNet) and transfer learning are employed to enhance model robustness, and the balanced mean squared error (BMSE) loss function is utilized to address the impact of imbalanced training data. An image preclassification module is implemented to mitigate the influence of changing lighting conditions on turbidity recognition. The effectiveness of these methods is validated using data from the United States Geological Survey (USGS). Our findings reveal that the BMSE loss function outperforms the mean squared error (MSE) loss function in turbidity recognition. Global image recognition consistently surpasses region of interest (ROI) image recognition, providing more accurate and reliable results under various lighting scenarios. Additionally, the influence of neural network depth on recognition accuracy varies with lighting conditions, highlighting the need for a careful selection of network architectures. Despite the promising results, the study identifies challenges in continuous noncontact monitoring, especially during sunrise and sunset, where rapidly changing lighting conditions can lead to recognition errors. The video image-based water turbidity identification model established in the study provides a new way to carry out low-cost and noncontact monitoring of water quality in rivers.

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Section: Methodsmentioning

confidence: 99%

An Approach Based on Video Image Intelligent Recognition for Water Turbidity Monitoring in River

Zhou,

Chen,

Chen

et al. 2024

ACS EST Water

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“…The results are shown in the images of rows D and E in Figure 3b. As can be seen from the color information in rows D and E in Figure 3b, the HSV image is more intuitive than the RGB image to express the hue, vividness, and lightness and darkness of the colors, and it is able to clearly differentiate between the images of samples of black tea with different degrees of fermentation [25].…”

Section: Response Of Color Variables In the Black Tea Fermentation Pr...mentioning

confidence: 99%

Real-Time Discrimination and Quality Evaluation of Black Tea Fermentation Quality Using a Homemade Simple Machine Vision System

Yang,

An,

et al. 2023

Fermentation

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Fermentation is a key link in determining the quality and flavor formation of black tea. However, during the actual production, the judgment of black tea fermentation quality mainly relies on the sensory evaluation of the tea maker, which is more subjective and prone to cause inconsistency in tea quality. Traditional testing methods, such as physical and chemical analyses, are time-consuming, laborious, and costly and are unable to meet the needs of the actual production. In this study, a self-developed machine vision system was used to quickly and accurately identify the degree of black tea fermentation by acquiring color and texture information on the surface of fermented leaves. To accurately control the quality of black tea fermentation and to understand the dynamic changes in key endoplasmic components in the fermented leaves, a quantitative prediction model of the key endoplasmic components in the fermentation process of black tea was constructed. The experiments proved that the system achieved 100% accuracy in discriminating the degree of fermentation of black tea, and the prediction accuracy of catechin components and thearubigin content reached more than 0.895. This system overcomes the defects of accurate measurement of multiple sensors coupled together, reduces the detection cost, and optimizes the experimental process. It can meet the needs of online monitoring in actual production.

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“…Analysis of misclassification images and number of overlapping good pattern and bad pattern images, along with classification accuracy, sensitivity, and specificity of 3D film images. To evaluate the performance of the proposed algorithm, we analyzed the results of the existing algorithms using the Abs-based difference method, Otsu thresholding, Canny edge detection, the CNN with Canny [4], morphological geodesic active contour [7], the Michelson contrast [22], the Canny with HSV [3], and the SVM with Canny [14]. For the CNN with Canny method proposed by Mlyahilu et al [4], we used 32 and 64 nodes in the convolution layer, 10 epochs, the ReLu activation function, and Adam for the optimization function, as in the paper.…”

Section: Performance Of the Proposed Algorithm And Comparative Algori...mentioning

confidence: 99%

“…Thus, there is a need to develop an inspection algorithm to identify defective 3D film products. Quality inspection for 3D films can be performed using existing research methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

et al. 2023

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A 3D film pattern image was recently developed for marketing purposes, and an inspection method is needed to evaluate the quality of the pattern for mass production. However, due to its recent development, there are limited methods to inspect the 3D film pattern. The good pattern in the 3D film has a clear outline and high contrast, while the bad pattern has a blurry outline and low contrast. Due to these characteristics, it is challenging to examine the quality of the 3D film pattern. In this paper, we propose a simple algorithm that classifies the 3D film pattern as either good or bad by using the height of the histograms. Despite its simplicity, the proposed method can accurately and quickly inspect the 3D film pattern. In the experimental results, the proposed method achieved 99.09% classification accuracy with a computation time of 6.64 s, demonstrating better performance than existing algorithms.

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New Aggregation Approaches with HSV to Color Edge Detection

Cited by 10 publications

References 35 publications

An Approach Based on Video Image Intelligent Recognition for Water Turbidity Monitoring in River

An Approach Based on Video Image Intelligent Recognition for Water Turbidity Monitoring in River

Real-Time Discrimination and Quality Evaluation of Black Tea Fermentation Quality Using a Homemade Simple Machine Vision System

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

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