Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Okarma, Krzysztof; Fastowicz, Jarosław

doi:10.5755/j01.eie.25.5.24357

Cited by 11 publications

(3 citation statements)

References 32 publications

(34 reference statements)

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“…A variety of sensing techniques are used for increasing road traffic safety. One approach is based on equipping vehicles [ 13 , 14 , 15 ] with new sensors and another by upgrading road infrastructure with traffic monitoring systems, from inductive loops to microwave radars and optical fibers [ 4 , 5 , 6 , 7 , 8 , 9 ]. Although many methods can be used for intelligent traffic systems, there are few key technologies widely accepted as reliable: surveillance camera systems, inductive loops and magnetic field sensors.…”

Section: Relate Workmentioning

confidence: 99%

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Miklusis

Markevičius

Navikas

et al. 2021

Sensors

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Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

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Section: Relate Workmentioning

confidence: 99%

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Miklusis

Markevičius

Navikas

et al. 2021

Sensors

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“…Image augmentation techniques are not used. ADAM [55] is used as an optimizer during the training with a learning rate 0.0001. As shown in Figure 8, the average segmentation accuracy surpasses 85% on the validation dataset in less than 15 epochs.…”

Section: Step 1: Training Of the Semantic Segmentation Modelmentioning

confidence: 99%

Applications of Computer Vision in Automation and Robotics

2021

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Computer vision applications have become one of the most rapidly developing areas in automation and robotics, as well as in some other similar areas of science and technology, e.g., mechatronics, intelligent transport and logistics, biomedical engineering, and even in the food industry. Nevertheless, automation and robotics seems to be one of the leading areas of practical applications for recently developed artificial intelligence solutions, particularly computer and machine vision algorithms. One of the most relevant issues is the safety of the human-computer and human-machine interactions in robotics, which requires the "explainability" of algorithms, often excluding the potential application of some solutions based on deep learning, regardless of their performance in pattern recognition applications.Considering the limited amount of training data, typical for robotics, important challenges are related to unsupervised learning, as well as no-reference image and video quality assessment methods, which may prevent the use of some distorted video frames for image analysis applied for further control of, e.g., robot motion. The use of image descriptors and features calculated for natural images captured by cameras in robotics, both in "out-hand" and "in-hand" solutions, may cause more problems in comparison to artificial images, typically used for the verification of general-purpose computer vision algorithms, leading to a so-called "reality gap". This Special Issue on "Applications of Computer Vision in Automation and Robotics" brings together the research communities interested in computer and machine vision from various departments and universities, focusing on both automation and robotics as well as computer science.The paper [1] is related to the problem of image registration in printing defect inspection systems and the choice of appropriate feature regions. The proposed automatic feature region searching algorithm for printed image registration utilizes contour point distribution information and edge gradient direction and may also be applied for online printing defect detection.The next contribution [2] presents a method of camera-based calibration for optical see-through headsets used in augmented reality applications, also for consumer level systems. The proposed fast automatic offline calibration method is based on standard camera calibration and computer vision methods to estimate the projection parameters of the display model for a generic position of the camera. They are then refined using planar homography, and the validation of the proposed method has been made using a developed MATLAB application.The analysis of infrared images for pedestrian detection at night is considered in the paper [3], where a method based on an attention-guided encoder-decoder convolutional neural network is proposed to extract discriminative multi-scale features from low-resolution and noisy infrared images. The authors have validated their method using two pedestrian video datasets-Keimyung University (KMU) and Computer ...

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“…An additional problem might be related fo different colors and luminance of the acquired images and those rendered from the 3D models. Hence, the idea of the calculation of the average mutual similarity is proposed for the images of the 3D printed surfaces assuming the side location of cameras, initially investigated in the papers [54,55] exclusively for classification purposes. Such side-view mounting of a camera makes it possible to acquire images with visible individual layers of the filament, as illustrated in Figure 1.…”

Section: Proposed Approachmentioning

confidence: 99%

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

2020

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Quality assessment of the 3D printed surfaces is one of the crucial issues related to fast prototyping and manufacturing of individual parts and objects using the fused deposition modeling, especially in small series production. As some corrections of minor defects may be conducted during the printing process or just after the manufacturing, an automatic quality assessment of object’s surfaces is highly demanded, preferably well correlated with subjective quality perception, considering aesthetic aspects. On the other hand, the presence of some greater and more dense distortions may indicate a reduced mechanical strength. In such cases, the manufacturing process should be interrupted to save time, energy, and the filament. This paper focuses on the possibility of using some general-purpose full-reference image quality assessment methods for the quality assessment of the 3D printed surfaces. As the direct application of an individual (elementary) metric does not provide high correlation with the subjective perception of surface quality, some modifications of similarity-based methods have been proposed utilizing the calculation of the average mutual similarity, making it possible to use full-reference metrics without the perfect quality reference images, as well as the combination of individual metrics, leading to a significant increase of correlation with subjective scores calculated for a specially prepared dataset.

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Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Cited by 11 publications

References 32 publications

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Applications of Computer Vision in Automation and Robotics

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

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