Fast quality assessment of 3D printed surfaces based on structural similarity of image regions

Fastowicz, Jarosław; Okarma, Krzysztof

doi:10.1109/iiphdw.2018.8388399

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Nevertheless, some preliminary attempts to the off-line application of selected metrics assuming the division of 3D printed surface images into smaller fragments were presented in earlier papers [32], [33]. Obtained results were promising.…”

Section: Selected Full-reference Image Quality Assessment Methodsmentioning

confidence: 97%

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

Okarma¹,

Fastowicz²

2019

ELEKTRON ELEKTROTECH

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Automatic visual quality assessment of the 3D printed surfaces is currently one of the most demanding challenges in additive manufacturing. Regardless of the applications of the computer vision for the 3D printing process monitoring purposes, a reliable surface quality evaluation during manufacturing may introduce brand new possibilities. The detection of some distortions and their automatic evaluation can be helpful when deciding to stop the process to save time, energy, and filament. In some cases, some further corrections can also be made for relatively small distortions. Since many general-purpose image quality assessment methods have been proposed in recent years, their applications for the quality evaluation in the additive manufacturing are investigated. As most of the metrics are full-reference and require the availability of the original perfect quality image, their direct application is not possible. Therefore, their adaptation is described in the paper together with experimental verification of classification results obtained using various metrics.

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Section: Selected Full-reference Image Quality Assessment Methodsmentioning

confidence: 97%

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

Okarma¹,

Fastowicz²

2019

ELEKTRON ELEKTROTECH

Self Cite

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“…Although their direct application for such purposes is limited due to the unavailability of reference 3D prints, as well as the necessity of phase adjustments and colour calibration, the mutual comparison of image fragments is possible. The application of SSIM, CW-SSIM and STSIM metrics with the division of the image of the 3D printed surface into 4 and 16 parts was presented in the paper [79], whereas the application of feature similarity metrics (RFSIM and FSIM) was roughly analysed in [80]. An improved version of the application of SSIM, with the use of the Monte Carlo method to decrease the computational complexity, was proposed in [81], although its efficient application requires the additional phase adjustment of randomly chosen image fragments.…”

Section: Alternative Applications Of Iqa Methodsmentioning

confidence: 99%

Current Trends and Advances in Image Quality Assessment

Okarma¹

2019

ElAEE

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Image quality assessment (IQA) is one of the constantly active areas of research in computer vision. Starting from the idea of Universal Image Quality Index (UIQI), followed by well-known Structural Similarity (SSIM) and its numerous extensions and modifications, through Feature Similarity (FSIM) towards combined metrics using the multi-metric fusion approach, the development of image quality assessment is still in progress. Nevertheless, regardless of new databases and the potential use of deep learning methods, some challenges remain still up to date. Some of the IQA metrics can also be used efficiently for alternative purposes, such as texture similarity estimation, quality evaluation of 3D images and 3D printed surfaces as well as video quality assessment.

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“…Peneliti lain sebelumnya telah [14] melakukan penelitian menggunakan algoritma SSIM yang digunakan sebagai metode baru dalam menemukan ekspresi wajah yang paling mirip dengan gambar yang diberikan dengan memanfaatkan aspek structural visual dari ekspresi. Hasil penelitian ini adalah SSIM dapat digunakan sebagai algoritma dalam machine learning dalam menemukan ekspresi wajah.…”

Section: Hasil Dan Pembahasanunclassified

Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar

Sumarna

Utami²,

Hartanto

2021

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Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.Kata Kunci— SSIM, anomaly, gambar, deteksiImage enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.Keywords— SSIM, anomaly, gambar, deteksi

show abstract

Fast quality assessment of 3D printed surfaces based on structural similarity of image regions

Cited by 13 publications

References 17 publications

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

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

Current Trends and Advances in Image Quality Assessment

Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar

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