Enhancing the Repeatability Quality of Feature Detector in Epipolar Geometry

• A. KUSNADI Universitas Multimedia Nusantara http://orcid.org/0000-0002-9587-1057
• W. S. KOM Universitas Multimedia Nusantara http://orcid.org/0000-0002-9587-1057
• R. WINANTYO Universitas Multimedia Nusantara http://orcid.org/0000-0002-7532-8268
• I. ZUHDI PANE Universitas Multimedia Nusantara http://orcid.org/0000-0002-4752-6611

Palabras clave: Epipolar Geometry, Feature Detector, Keypoint, Repeatability Quality. Calidad de repetibilidad, Detector de características, Geometría epipolar, Punto clave.

Resumen

ABSTRACT

 

This study discusses evaluating the repeatability quality of feature detector algorithms for image key points in epipolar geometry. Although no one has been able to examine performance in this area; hence, the necessity of conducting research based on improvement. In our method, the image format is converted into YCbCr, using only the Y channel, which is then tested with a variety of threshold values, and Weiner algorithm was applied as noise removal. Therefore, to prove the effectiveness, a comparison is made with the test results before and after method implementation, thus, verifying the success of the technique applied.

RESUMEN

 

Este estudio analiza la evaluación de la calidad de repetibilidad de los algoritmos de detección de características para puntos clave de imagen en geometría epipolar. Aunque nadie ha podido examinar el desempeño en esta área; de ahí la necesidad de realizar investigaciones basadas en la mejora. En nuestro método, el formato de imagen se convierte en YCbCr, utilizando solo el canal Y, que luego se prueba con una variedad de valores umbral, y se aplicó el algoritmo Weiner como eliminación de ruido. Por lo tanto, para probar la efectividad, se realiza una comparación con los resultados de la prueba antes y después de la implementación del método, verificando así el éxito de la técnica aplicada.

Citas

BIBLIOGRAPHY

AHMAD, MI, WOO, WL, & DLAY, S (2016). “Non-stationary feature fusion of face and palmprint multimodal biometrics”, in: Neurocomputing, 177, pp.49-61.

AKINTOYE, KA, ISMIAL, NAFB, OTHMAN, NZSB, RAHIM, MSM, & ABDULLAH, AH (2018). “Composite Median Wiener Filter Based Technique for Image Enhancement”, in: Journal of Theoretical & Applied Information Technology, 96(15).

ALEXANDER, L, KUSNADI, A, WELLA, W, WINANTYO, R, & PANE, IZ (2018), December. Authentication System Using 3D Face With Algorithm DLT and Neural Network. In 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS) (pp. 186-189). IEEE.

BAN, Y, KIM, SK, KIM, S, TOH, KA, & LEE, S (2014). “Face detection based on skin color likelihood”, in: Pattern Recognition, 47(4), pp.1573-1585.

BRONSTEIN, AM, BRONSTEIN, MM, & KIMMEL, R (2003). June. Expression-invariant 3D face recognition. In international conference on Audio-and video-based biometric person authentication (pp. 62-70). Springer, Berlin, Heidelberg.

CHINCHOLKAR, YD, & BANGADKAR, S (2007). A Review of ToF PMD Camera. IJAREEIE, pp.2320-3765.

GOWDA, SN, & YUAN, C (2018). December. ColorNet: Investigating the importance of color spaces for image classification. In Asian Conference on Computer Vision (pp. 581-596). Springer, Cham.

HARTLEY, R, & ZISSERMAN, A (2000). Introduction—-A tour of multple view geometry. In Multiple view geometry in computer vision (Vol. 2, pp. 6-19). Cambridge Univ. Press.

JAIN, AK, ROSS, A, & PANKANTI, S (2006). “Biometrics: a tool for information security”, in: IEEE transactions on information forensics and security, 1(2), pp.125-143.

JOSHI, M, MAZUMDAR, B, & DEY, S (2018). Security vulnerabilities against fingerprint biometric system. arXiv preprint arXiv:1805.07116.

KARTHICK, S, SATHIYASEKAR, K, & PURANEESWARI, A (2014). “A survey based on region based segmentation”, in: International Journal of Engineering Trends and Technology, 7, pp.143-147.

KUMAWAT, A, & PANDA, S (2018). “Feature Detection and Description in Remote Sensing Images using a Hybrid Feature Detector”, in: Procedia computer science, 132, pp.277-287.

LIETZ, H, & EBERHARDT, J (2017). Introduction to Fourier ptychographic imaging for 3D ToF cameras. In Engineering for a Changing World: Proceedings; 59th IWK, Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 11-15, 2017 (Vol. 59, No. 1.4. 02).

MAHESHKAR, V, KAMBLE, S, AGARWAL, S, & SRIVASTAVA, VK (2012). Feature Image Generation Using Energy Distribution for Face Recognition in Transform Domain. In International Conference on Computer Science and Information Technology (pp. 644-653). Springer, Berlin, Heidelberg.

MOUATS, T, AOUF, N, NAM, D, & VIDAS, S (2018). “Performance Evaluation of Feature Detectors and Descriptors Beyond the Visible”, in: Journal of Intelligent & Robotic Systems, 92(1), pp.33-63.

POYNTON, CA (1996). A technical introduction to digital video. John Wiley & Sons, Inc.

QU, X, SOHEILIAN, B, HABETS, E, & PAPARODITIS, N (2016). EVALUATION OF SIFT AND SURF FOR VISION BASED LOCALIZATION. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41.

SASAKI, Y (2007). The truth of the F-measure. Teach Tutor mater, 1(5), pp.1-5.

SEONG, H, CHOI, H, SON, H, & KIM, C (2018). Image-based 3D Building Reconstruction Using A-KAZE Feature Extraction Algorithm. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 35, pp. 1-5). IAARC Publications.

TIKKANEN, H, ALAJOUTSIJÄRVI, K, & TÄHTINEN, J (2000). “The concept of satisfaction in industrial markets: a contextual perspective and a case study from the software industry”, in: Industrial Marketing Management, 29(4), pp.373-386.

WANG, Y, & SONG, Y (2014). Facial Key points Detection.

ZAIDAN, AA, AHMAD, NN, KARIM, HA, LARBANI, M, ZAIDAN, BB, & SALI, A (2014). “Image skin segmentation based on multi-agent learning Bayesian and neural network”, in: Engineering Applications of Artificial Intelligence, 32, pp.136-150.