Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification
Automated inspection machines for wafer defects usually captured thousands of images on a large scale to preserve the detail of defect features. However, most transfer learning architecture requires smaller images as input images. Thus, proper compression is required to preserve the defect features...
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| Language: | en |
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Penerbit UMP
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33607/1/Effect%20of%20image%20compression%20using%20fast%20fourier%20transformation.pdf http://umpir.ump.edu.my/id/eprint/33607/ https://doi.org/10.15282/mekatronika.v2i1.6704 |
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| author | Jessnor Arif, Mat Jizat Ahmad Fakhri, Ab. Nasir Anwar P. P., Abdul Majeed Yuen, Edmund |
| author_facet | Jessnor Arif, Mat Jizat Ahmad Fakhri, Ab. Nasir Anwar P. P., Abdul Majeed Yuen, Edmund |
| author_sort | Jessnor Arif, Mat Jizat |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Automated inspection machines for wafer defects usually captured thousands of images on a large scale to preserve the detail of defect features. However, most transfer learning architecture requires smaller images as input images. Thus, proper compression is required to preserve the defect features whilst maintaining an acceptable classification accuracy. This paper reports on the effect of image compression using Fast Fourier Transformation and Discrete Wavelet Transformation on transfer learning wafer defect image classification. A total of 500 images with 5 classes with 4 defect classes and 1 non-defect class were split to 60:20:20 ratio for training, validating and testing using InceptionV3 and Logistic Regression classifier. However, the input images were compressed using Fast Fourier Transformation and Discrete Wavelet Transformation using 4 level decomposition and Debauchies 4 wavelet family. The images were compressed by 50%, 75%, 90%, 95%, and 99%. As a result, the Fast Fourier Transformation compression show an increase from 89% to 94% in classification accuracy up to 95% compression, while Discrete Wavelet Transformation shows consistent classification accuracy throughout albeit diminishing image quality. From the experiment, it can be concluded that FFT and DWT image compression can be a reliable method for image compression for grayscale image classification as the image memory space drop 56.1% while classification accuracy increased by 5.6% with 95% FFT compression and memory space drop 55.6% while classification accuracy increased 2.2% with 50% DWT compression. |
| format | Article |
| id | my.ump.umpir.33607 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| record_format | eprints |
| spelling | my.ump.umpir.336072022-04-01T07:17:08Z http://umpir.ump.edu.my/id/eprint/33607/ Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification Jessnor Arif, Mat Jizat Ahmad Fakhri, Ab. Nasir Anwar P. P., Abdul Majeed Yuen, Edmund QA76 Computer software TJ Mechanical engineering and machinery Automated inspection machines for wafer defects usually captured thousands of images on a large scale to preserve the detail of defect features. However, most transfer learning architecture requires smaller images as input images. Thus, proper compression is required to preserve the defect features whilst maintaining an acceptable classification accuracy. This paper reports on the effect of image compression using Fast Fourier Transformation and Discrete Wavelet Transformation on transfer learning wafer defect image classification. A total of 500 images with 5 classes with 4 defect classes and 1 non-defect class were split to 60:20:20 ratio for training, validating and testing using InceptionV3 and Logistic Regression classifier. However, the input images were compressed using Fast Fourier Transformation and Discrete Wavelet Transformation using 4 level decomposition and Debauchies 4 wavelet family. The images were compressed by 50%, 75%, 90%, 95%, and 99%. As a result, the Fast Fourier Transformation compression show an increase from 89% to 94% in classification accuracy up to 95% compression, while Discrete Wavelet Transformation shows consistent classification accuracy throughout albeit diminishing image quality. From the experiment, it can be concluded that FFT and DWT image compression can be a reliable method for image compression for grayscale image classification as the image memory space drop 56.1% while classification accuracy increased by 5.6% with 95% FFT compression and memory space drop 55.6% while classification accuracy increased 2.2% with 50% DWT compression. Penerbit UMP 2020-06 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33607/1/Effect%20of%20image%20compression%20using%20fast%20fourier%20transformation.pdf Jessnor Arif, Mat Jizat and Ahmad Fakhri, Ab. Nasir and Anwar P. P., Abdul Majeed and Yuen, Edmund (2020) Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (1). pp. 16-22. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v2i1.6704 https://doi.org/10.15282/mekatronika.v2i1.6704 |
| spellingShingle | QA76 Computer software TJ Mechanical engineering and machinery Jessnor Arif, Mat Jizat Ahmad Fakhri, Ab. Nasir Anwar P. P., Abdul Majeed Yuen, Edmund Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification |
| title | Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification |
| title_full | Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification |
| title_fullStr | Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification |
| title_full_unstemmed | Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification |
| title_short | Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification |
| title_sort | effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification |
| topic | QA76 Computer software TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/33607/1/Effect%20of%20image%20compression%20using%20fast%20fourier%20transformation.pdf http://umpir.ump.edu.my/id/eprint/33607/ https://doi.org/10.15282/mekatronika.v2i1.6704 https://doi.org/10.15282/mekatronika.v2i1.6704 |
| url_provider | http://umpir.ump.edu.my/ |