Edge Detection Comparison of Hybrid Feature Extraction for Combustible Fire Segmentation: A Canny vs Sobel Performance Analysis

Malbog, Mon Arjay F.; Lacatan, Luisito Lolong; Dellosa, Rhowel M.; Austria, Yolanda D.; Cunanan, Christopher Franco

doi:10.1109/icsgrc49013.2020.9232632

Cited by 14 publications

(10 citation statements)

References 21 publications

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“…Malbog et al [13] used Canny edge detection to obtain the shape of the action and made it an action template, extracting key frames from the video and matching the saved action template and then distinguishing the type of human action. Alp and Keles [14] combined "motion energy image" and "motion history image" into a time-domain template and calculated the horses between the motion to be recognized and the template.…”

Section: Global Feature Representation Methodmentioning

confidence: 99%

[Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining

Zhai

2021

Complexity

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There are complex posture changes in dance movements, which lead to the low accuracy of dance movement recognition. And none of the current motion recognition uses the dancer’s attributes. The attribute feature of dancer is the important high-level semantic information in the action recognition. Therefore, a dance movement recognition algorithm based on feature expression and attribute mining is designed to learn the complicated and changeable dancer movements. Firstly, the original image information is compressed by the time-domain fusion module, and the information of action and attitude can be expressed completely. Then, a two-way feature extraction network is designed, which extracts the details of the actions along the way and takes the sequence image as the input of the network. Then, in order to enhance the expression ability of attribute features, a multibranch spatial channel attention integration module (MBSC) based on an attention mechanism is designed to extract the features of each attribute. Finally, using the semantic inference and information transfer function of the graph convolution network, the relationship between attribute features and dancer features can be mined and deduced, and more expressive action features can be obtained; thus, high-performance dance motion recognition is realized. The test and analysis results on the data set show that the algorithm can recognize the dance movement and improve the accuracy of the dance movement recognition effectively, thus realizing the movement correction function of the dancer.

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Section: Global Feature Representation Methodmentioning

confidence: 99%

[Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining

Zhai

2021

Complexity

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“…The CFAS systems require human intervention, for instance, visiting a fire location to confirm fire in case of any alarm. To cope up with these limitations, many visual sensors-based fire detection systems have been presented in the literature [ 22 , 23 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Yar

Hussain

Khan

et al. 2021

Computational Intelligence and Neuroscience

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Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response are very significant. Therefore, we develop a novel framework based on a lightweight convolutional neural network (CNN), requiring less training time, and it is applicable over resource-constrained devices. The internal architecture of the proposed model is inspired by the block-wise VGG16 architecture with a significantly reduced number of parameters, input size, inference time, and comparatively higher accuracy for early fire detection. In the proposed model, small-size uniform convolutional filters are employed that are specifically designed to capture fine details of input fire images with a sequentially increasing number of channels to aid effective feature extraction. The proposed model is evaluated on two datasets such as a benchmark Foggia’s dataset and our newly created small-scaled fire detection dataset with extremely challenging real-world images containing a high-level of diversity. Experimental results conducted on both datasets reveal the better performance of the proposed model compared to state-of-the-art in terms of accuracy, false-positive rate, model size, and running time, which indicates its robustness and feasible installation in real-world scenarios.

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“…Laplacian edge detection, unlike other edge detection methods, utilizes second-order differential equations and excels at detecting edges between light and dark regions. Edge detection is typically used as part of preprocessing, and recently, research combining it with machine learning techniques has been consistently emerging [10][11][12][13][14][15]. Mlyahilu et al proposed a method where they detected the edges of a 3D pattern film using Canny, Sobel, and Laplacian edge detection during the preprocessing stage and then classified 3D pattern images using a convolutional neural network (CNN) [10].…”

Section: Related Workmentioning

confidence: 99%

“…The experimental results showed an improvement of 11.87% compared to the existing methods. Furthermore, in segmentation research, studies have proposed methods utilizing drones equipped with high-resolution proximity cameras for capturing images and then employing methods such as dual tree complex wavelet transform (DTCWT) and discrete wavelet transform (DWT) to segment and detect concrete cracks [13], detecting fires and extracting fire features using different image-processing techniques such as Canny, Sobel, and HSV transformations [14], and segmenting and detecting concrete cracks in images using edge detectors like Roberts, Prewitt, Sobel, and deep convolutional neural networks (DCNN) [15]. However, 3D pattern films pose a challenge for pattern detection due to their blurred contours.…”

Section: Related Workmentioning

confidence: 99%

Classification Method of 3D Pattern Film Images Using MLP Based on the Optimal Widths of Histogram

Lee,

Choi,

Kim

2024

Electronics

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3D pattern film is a film that makes a 2D pattern appear 3D depending on the amount and angle of light. However, since the 3D pattern film image was developed recently, there is no established method for classifying and verifying defective products, and there is little research in this area, making it a necessary field of study. Additionally, 3D pattern film has blurred contours, making it difficult to detect the outlines and challenging to classify. Recently, many machine learning methods have been published for analyzing product quality. However, when there is a small amount of data and most images are similar, using deep learning can easily lead to overfitting. To overcome these limitations, this study proposes a method that uses an MLP (Multilayer Perceptron) model to classify 3D pattern films into genuine and defective products. This approach entails inputting the widths derived from specific points’ heights in the image histogram of the 3D pattern film into the MLP, and then classifying the product as ‘good’ or ‘bad’ using optimal hyper-parameters found through the random search method. Although the contours of the 3D pattern film are blurred, this study can detect the characteristics of ‘good’ and ‘bad’ by using the image histogram. Moreover, the proposed method has the advantage of reducing the likelihood of overfitting and achieving high accuracy, as it reflects the characteristics of a limited number of similar images and builds a simple model. In the experiment, the accuracy of the proposed method was 98.809%, demonstrating superior performance compared to other models.

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Edge Detection Comparison of Hybrid Feature Extraction for Combustible Fire Segmentation: A Canny vs Sobel Performance Analysis

Cited by 14 publications

References 21 publications

[Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining

[Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Classification Method of 3D Pattern Film Images Using MLP Based on the Optimal Widths of Histogram

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