Defect detection of printing images on cans based on SSIM and chromatism

Wang, Jinnan; Hui, Changqing; Yang, Wenming; Zhou, Maomin; Wang, Guijin

doi:10.1109/compcomm.2017.8322912

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…In a home security automatic recording system described in [24], SSIM is used to compare the current and the next image frames to decide whether to trigger the recording process. This system achieves a high-accuracy and stable event detection, saving storage space and simplifying subsequent image analysis time.…”

Section: Related Workmentioning

confidence: 99%

FenceTalk: Exploring False Negatives in Moving Object Detection

Lin,

Liu,

Lin

et al. 2023

Algorithms

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Deep learning models are often trained with a large amount of labeled data to improve the accuracy for moving object detection in new fields. However, the model may not be robust enough due to insufficient training data in the new field, resulting in some moving objects not being successfully detected. Training with data that is not successfully detected by the pre-trained deep learning model can effectively improve the accuracy for the new field, but it is costly to retrieve the image data containing the moving objects from millions of images per day to train the model. Therefore, we propose FenceTalk, a moving object detection system, which compares the difference between the current frame and the background image based on the structural similarity index measure (SSIM). FenceTalk automatically selects suspicious images with moving objects that are not successfully detected by the Yolo model, so that the training data can be selected at a lower labor cost. FenceTalk can effectively define and update the background image in the field, reducing the misjudgment caused by changes in light and shadow, and selecting images containing moving objects with an optimal threshold. Our study has demonstrated its performance and generality using real data from different fields. For example, compared with the pre-trained Yolo model using the MS COCO dataset, the overall recall of FenceTalk increased from 72.36% to 98.39% for the model trained with the data picked out by SSIM. The recall of FenceTalk, combined with Yolo and SSIM, can reach more than 99%.

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Section: Related Workmentioning

confidence: 99%

FenceTalk: Exploring False Negatives in Moving Object Detection

Lin,

Liu,

Lin

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…Penelitian sebelumnya telah melakukan penelitian tentang penilaian gambar radiologi yang merupakan hasil dari gambar MRI dan film biasa [11]. Peneliti menggunakan 16 Hasil tersebut menunjukkan bahwa gambar radiologi dapat dianalisis menggunakan SSIM dengan cara membandingkan dengan referensi dataset gambar dan dapat digunakan sebagai pengganti ahli radiologi untuk menganalisis gambar radiologi tersebut.…”

Section: Hasil Dan Pembahasanunclassified

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

Sumarna

Utami²,

Hartanto

2021

citec

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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

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Automatisierte Qualitätssicherung via Image Mining und Computer Vision – Literaturrecherche und Prototyp

Trinks

2021

Big Data Analytics

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Defect detection of printing images on cans based on SSIM and chromatism

Cited by 8 publications

References 12 publications

FenceTalk: Exploring False Negatives in Moving Object Detection

FenceTalk: Exploring False Negatives in Moving Object Detection

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

Automatisierte Qualitätssicherung via Image Mining und Computer Vision – Literaturrecherche und Prototyp

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