Araştırma Makalesi
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SIFT ve Şablon Eşleme ile Lastik İzi Desenlerinin Tanınması

Yıl 2022, Cilt: 9 Sayı: 2, 634 - 643, 31.05.2022
https://doi.org/10.31202/ecjse.990247

Öz

Lastik izi desenlerinin tanınması hem suç olay mahallerinin araştırılmasında hem de trafik kazalarında kazaya karışan araçların belirlenmesinde önemli role sahiptir. Taşıdığı zengin doku bilgisi nedeniyle lastiklerden alınan desen görüntülerinin tanınmasında doku öznitelikleri kullanılmaktadır. Fakat olay yerlerinden alınan lastik izlerinin tanınması konusu yeterince çalışılmamıştır. Bu çalışmada lastik izi/iz parçası görüntülerinin tanınması amacıyla SIFT tabanlı öznitelikler ve şablon eşleme yöntemleri kullanılmıştır. Deneylerde temiz izlerden alınan kesitlerin kirli izlerin ve kirli izlerden alınan kesitlerin, temiz izlerle eşleştirilmesi işleminde state of art yöntemlere göre daha yüksek tanıma başarımı elde edilmiştir.

Kaynakça

  • 1. Colbry D., Cherba D., Luchini J., “Pattern Recognition for Classification and Matching of Car Tires”, Tire Science and Technology, 2005, 33:2-17. http://dx.doi.org/10.2346/1.2186784
  • 2. Huang D. Y., Hu W. C., Wang Y. W., Chen C. I., Cheng C. H., “Recognition of Tire Tread Patterns Based on Gabor Wavelets and Support Vector Machine”, In: Collective Intelligence. Technologies and Applications (ICCCI), Lecture Notes in Computer Science, vol 6423, Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16696-9_11
  • 3. Bulan, O, Bernal, E A, Loce, R P, Wu, Wencheng, “Tire Classification from Still Images and Video”, 15th International IEEE Conference on Intelligent Transportation Systems (ITSC2012), Anchorage Alaska, United States, pp 485-490, (2012).
  • 4. Wang S., Liu Y., Li D., Yan H., Bai B., “An Improved SIFT Feature Extraction Method for Tyre Tread Patterns Retrieval”, 2014 Seventh International Symposium on Computer Intelligence and Design, pp. 539-543, (2014). https://doi.org/10.1109/ISCID.2014.276
  • 5. Liu Y., Yan H., Lim KP., “Study on rotation-invariant texture feature extraction for tire pattern retrieval”, Multidimensional Systms and Signal Processing, 2017, 28:757-770. https://doi.org/10.1007/s11045-015-0373-0
  • 6. Ge Y., Liu Y., Wang F., Zhu T., Wang D., “Texture Feature Extraction Based on Histogram of Oriented Gradient Domain Texture Tendency for Tyre Pattern Retrieval”, 2017 Eighth International Conference on Intelligent Control and Information Procesiing (ICICIP), pp.1-7, (2017). https://doi.org/10.1109/ICICIP.2017.8113908
  • 7. Liu Y., Zhang S., Wang F., Ling N., “Tread Pattern Image Classification using Convolutional Neural Network Based on Transfer Learning”, 2018 IEEE International Workshop on Signal Processing Systems (SiPS), pp.300-305, (2018). https://doi.org/10.1109/SiPS.2018.8598400
  • 8. Liu Y., Zhang S., Wang F., Lim K., Liu Q., Lei Y., Gong Y., Lu J., “ Wavelet-Energy-Weighted Local Binary Pattern Analysis for Tire Tread Pattern Classification”, 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), pp. 90-95, (2019). https://doi.org/10.1109/ICISPC.2019.8935658
  • 9. Wang F., Ding X., Liu Y., “Tyre Pattern Classification Based on Multi-scale GCN Model”, 2020 2nd Symposium on Signal Processing Systems, pp. 113-119, (2020). https://doi.org/10.1145/3421515.3421520
  • 10. URL: https://www.shutterstock.com, April 2021.
  • 11. Lowe, D. G., “Object recognition from local scale-invariant features”, Proceedings of the International Conference on Computer Vision, 2 pp.1150–1157 (1999). doi:10.1109/ICCV.1999.790410
  • 12. Lowe, D. G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 2004, 60(2):91–110. CiteSeerX 10.1.1.73.2924
  • 13. Fischler M. A., Bolles R. C., “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM, 1981, 24(6):381-395. https://doi.org/10.1145/358669.358692
  • 14. URL: https://en.wikipedia.org/wiki/Random_sample_consensus, September 2021.
  • 15. Brunelli R., “Template Matching Techniques in Computer Vision: Theory and Practice”, Wiley, ISBN 978-0-470-51706-2, (2009).

Recognition of Tire Track Patterns Using SIFT and Template Matching

Yıl 2022, Cilt: 9 Sayı: 2, 634 - 643, 31.05.2022
https://doi.org/10.31202/ecjse.990247

Öz

Recognition of tire track patterns has an important role in both the investigation of crime scenes and the identification of vehicles involved in traffic accidents. Due to the rich texture information they have, texture features are generally used to recognize track images taken from tires. However, recognition of tire tracks taken from crime scenes has not been studied sufficiently. In this study, SIFT-based features and template matching methods were used to recognize tire track/tire track fragment images. In the experiments, fragments taken from clean tracks and fragments taken from dirty tracks were matched with clean track images, and higher recognition performance was achieved compared to state of art methods.

Kaynakça

  • 1. Colbry D., Cherba D., Luchini J., “Pattern Recognition for Classification and Matching of Car Tires”, Tire Science and Technology, 2005, 33:2-17. http://dx.doi.org/10.2346/1.2186784
  • 2. Huang D. Y., Hu W. C., Wang Y. W., Chen C. I., Cheng C. H., “Recognition of Tire Tread Patterns Based on Gabor Wavelets and Support Vector Machine”, In: Collective Intelligence. Technologies and Applications (ICCCI), Lecture Notes in Computer Science, vol 6423, Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16696-9_11
  • 3. Bulan, O, Bernal, E A, Loce, R P, Wu, Wencheng, “Tire Classification from Still Images and Video”, 15th International IEEE Conference on Intelligent Transportation Systems (ITSC2012), Anchorage Alaska, United States, pp 485-490, (2012).
  • 4. Wang S., Liu Y., Li D., Yan H., Bai B., “An Improved SIFT Feature Extraction Method for Tyre Tread Patterns Retrieval”, 2014 Seventh International Symposium on Computer Intelligence and Design, pp. 539-543, (2014). https://doi.org/10.1109/ISCID.2014.276
  • 5. Liu Y., Yan H., Lim KP., “Study on rotation-invariant texture feature extraction for tire pattern retrieval”, Multidimensional Systms and Signal Processing, 2017, 28:757-770. https://doi.org/10.1007/s11045-015-0373-0
  • 6. Ge Y., Liu Y., Wang F., Zhu T., Wang D., “Texture Feature Extraction Based on Histogram of Oriented Gradient Domain Texture Tendency for Tyre Pattern Retrieval”, 2017 Eighth International Conference on Intelligent Control and Information Procesiing (ICICIP), pp.1-7, (2017). https://doi.org/10.1109/ICICIP.2017.8113908
  • 7. Liu Y., Zhang S., Wang F., Ling N., “Tread Pattern Image Classification using Convolutional Neural Network Based on Transfer Learning”, 2018 IEEE International Workshop on Signal Processing Systems (SiPS), pp.300-305, (2018). https://doi.org/10.1109/SiPS.2018.8598400
  • 8. Liu Y., Zhang S., Wang F., Lim K., Liu Q., Lei Y., Gong Y., Lu J., “ Wavelet-Energy-Weighted Local Binary Pattern Analysis for Tire Tread Pattern Classification”, 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), pp. 90-95, (2019). https://doi.org/10.1109/ICISPC.2019.8935658
  • 9. Wang F., Ding X., Liu Y., “Tyre Pattern Classification Based on Multi-scale GCN Model”, 2020 2nd Symposium on Signal Processing Systems, pp. 113-119, (2020). https://doi.org/10.1145/3421515.3421520
  • 10. URL: https://www.shutterstock.com, April 2021.
  • 11. Lowe, D. G., “Object recognition from local scale-invariant features”, Proceedings of the International Conference on Computer Vision, 2 pp.1150–1157 (1999). doi:10.1109/ICCV.1999.790410
  • 12. Lowe, D. G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 2004, 60(2):91–110. CiteSeerX 10.1.1.73.2924
  • 13. Fischler M. A., Bolles R. C., “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM, 1981, 24(6):381-395. https://doi.org/10.1145/358669.358692
  • 14. URL: https://en.wikipedia.org/wiki/Random_sample_consensus, September 2021.
  • 15. Brunelli R., “Template Matching Techniques in Computer Vision: Theory and Practice”, Wiley, ISBN 978-0-470-51706-2, (2009).
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Asuman Günay Yılmaz 0000-0003-3960-5085

Nasif Nabiyev 0000-0003-0314-8134

Yayımlanma Tarihi 31 Mayıs 2022
Gönderilme Tarihi 2 Eylül 2021
Kabul Tarihi 5 Kasım 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 9 Sayı: 2

Kaynak Göster

IEEE A. Günay Yılmaz ve N. Nabiyev, “Recognition of Tire Track Patterns Using SIFT and Template Matching”, ECJSE, c. 9, sy. 2, ss. 634–643, 2022, doi: 10.31202/ecjse.990247.