Active contour model by combining edge and region information discrete dynamic systems

Wang, Zhifeng; Liu, Yan Jun

doi:10.1177/1687814017692947

Cited by 13 publications

(6 citation statements)

References 40 publications

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“…Recently, research on data analysis using multi-sensor/image fusion has also been presented. Qiu et al introduced various machine learning-based multi-sensor information fusion studies that have recently been published [9]. Of note, they have introduced wearable sensors, smart wearable devices, and key application areas, and proposed fusion methods for multi-modal and multi-location sensors.…”

Section: Related Workmentioning

confidence: 99%

“…In the inspection method using sensors, various studies have been published, including a method of determining and inspecting sensor data measured in real time using a deep learning model and an inspection method of analyzing the waveforms of sensor data through deep learning [2][3][4][5][6][7][8]. In the inspection method using 2D images, methods of inspecting products for defects after dividing the welding in images taken with 2D vision cameras have been announced, and recently, methods of inspecting quality using KNN (K-nearest neighbor), K-means, improved Grabcut, etc., have been announced [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Meanwhile, sensor inspection has the advantage of high accuracy, but it also has the disadvantage in that it is impossible to check the welding location.…”

Section: Introductionmentioning

confidence: 99%

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Welding Bead Inspection Using Image and Multi-Sensor Fusion

Lee,

Choi,

Kim

2023

Applied Sciences

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Welding is a crucial manufacturing technique utilized in various industrial sectors, playing a vital role in production and safety aspects, particularly in shear reinforcement of dual-anchorage (SRD) applications, which are aimed at enhancing the strength of concrete structures, ensuring that their quality is of paramount importance to prevent welding defects. However, achieving only good products at all times is not feasible, necessitating quality inspection. To address this challenge, various inspection methods were studied. Nevertheless, finding an inspection method that combines a fast speed and a high accuracy remains a challenging task. In this paper, we proposed a welding bead quality inspection method that integrates sensor-based inspection using average current, average voltage, and mixed gas sensor data with 2D image inspection. Through this integration, we can overcome the limitations of sensor-based inspection, such as difficulty in identifying welding locations, and the accuracy and speed issues of 2D image inspection. Experimental results indicated that while sensor-based and image-based inspections individually resulted in misclassifications, the integrated approach accurately classified products as ‘good’ or ‘bad’. In comparison to other algorithms, our proposed method demonstrated a superior performance and computational speed.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Welding Bead Inspection Using Image and Multi-Sensor Fusion

Lee,

Choi,

Kim

2023

Applied Sciences

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“…These algorithms are based on active contours and minimal distance curves that allow connecting the classical snakes based on the energy and curve evolution. Geodesic active contours perform better than other active contours in segmenting objects from different images because they are not easily trapped by the local minima points and have a lower rate of convergence [17].…”

Section: Related Workmentioning

confidence: 99%

Morphological geodesic active contour algorithm for the segmentation of the histogram‐equalized welding bead image edges

Mlyahilu

Lee

et al. 2022

IET Image Processing

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Assessment and evaluation are the essential processes of industrially manufactured products for the determination of the quality and quantity of products. They give justifications in a practical way about whether the machine is perfect or imperfect, which can lead to a better or poorer production. In this study, the authors propose an algorithm that uses morphological geodesic active contour and image processing techniques to perform segmentation and assess the performance of a robot used to manufacture welding beads. The algorithm has four parameters which are pre-processed images, balloon force, smoothing parameter, and number of iterations. To pre-process the images, the algorithm uses an inverse Gaussian gradient operator for edge detection and applies the histogram equalization method to level the distribution. To detect the external contour of the bead, the level set is initialized as the region of interest whereby a balloon force can inflate or deflate towards the edges. To smoothen the contour, a smoothing parameter is applied to convert the jagged lines into a curve over a reasonable number of iterations. Based on the experimental results, the authors' algorithm used a fixed balloon force of −2, a smoothing parameter value of 4, and 40 iterations to segment images obtained from three different environments. The computation time for the segmentation and evaluation of one image was 0.70, 0.61, and 0.67 s for datasets with high brightness, low brightness, and normal brightness, respectively. Additionally, the authors' proposed algorithm achieved an outstanding performance of 0.9954, 0.9843, 0.9892, and 0.9435 in terms of recall, precision, F-measure, and IOU, respectively. To justify the performance of the authors' proposed algorithm, the authors compared it with the existing algorithms and found that it worked better than all the others for segmentation, although it lagged behind the entropy-based algorithm in terms of speed.

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“…Starting with the initial contour, the Snake Head samples features at the vertex positions, calculates and applies the offsets, and repeats the process. Compared to conventional active contour models, which can take dozens [21] or even hundreds [28] of iterations to converge, the deep active contour model converges to satisfactory results after a small number of iterations. For our experiments, we set the number of iterations to 4.…”

Section: B Snake Headmentioning

confidence: 99%

Deep Active Contour Models for Delineating Glacier Calving Fronts

Heidler

Mou

Loebel

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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This work has been accepted by IEEE TGRS for publication in a future issue. Choosing how to encode a realworld problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often been approached as a semantic segmentation task. Recent studies have shown that combining segmentation with edge detection can improve the accuracy of calving front detectors. Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps. The proposed approach, called "Charting Outlines by Recurrent Adaptation" (COBRA), combines Convolutional Neural Networks (CNNs) for feature extraction and active contour models for the delineation. By training and evaluating on several large-scale datasets of Greenland's outlet glaciers, we show that this approach indeed outperforms the aforementioned methods based on segmentation and edgedetection. Finally, we demonstrate that explicit contour detection has benefits over pixel-wise methods when quantifying the models' prediction uncertainties.The project page containing the code and animated model predictions can be found at https://khdlr.github.io/COBRA/.

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Active contour model by combining edge and region information discrete dynamic systems

Cited by 13 publications

References 40 publications

Welding Bead Inspection Using Image and Multi-Sensor Fusion

Welding Bead Inspection Using Image and Multi-Sensor Fusion

Morphological geodesic active contour algorithm for the segmentation of the histogram‐equalized welding bead image edges

Deep Active Contour Models for Delineating Glacier Calving Fronts

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