Thuật toán RANSAC thích ứng và mở rộng cho nhận diện đối tượng trong các hình ảnh viễn thám

Multimedia Tools and Applications - Tập 81 - Trang 31685-31708 - 2022
Zahra Hossein-Nejad1, Mehdi Nasri2
1Department of Electrical Engineering, Sirjan Brach, Islamic Azad University, Kerman, Iran
2Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

Tóm tắt

Trong bài báo này, một phương pháp mới được đề xuất cho việc nhận diện đối tượng trong các hình ảnh viễn thám. Trong phương pháp được đề xuất, quá trình khớp giữa đối tượng trong hình mẫu và hình ảnh thử nghiệm được thực hiện dựa trên Biến đổi Đặc trưng Không nhạy với Tỷ lệ (SIFT). Để giảm thiểu các khớp sai của SIFT, một thuật toán đồng thuận mẫu ngẫu nhiên thích ứng (RANSAC) được sử dụng. Trong RANSAC được đề xuất, giá trị ngưỡng được tính toán một cách thích ứng dựa trên trung bình và phương sai của các điểm khớp đúng và sai. Cuối cùng, ranh giới chính xác của đối tượng được trích xuất bằng cách sử dụng thuật toán tăng trưởng vùng mở rộng. Thuật toán được đề xuất sử dụng các điểm khớp đúng như nhiều điểm gốc thay vì một điểm gốc duy nhất. Phương pháp được đề xuất được triển khai trong MATLAB và so sánh với các phương pháp phát hiện đối tượng cổ điển. Kết quả mô phỏng xác nhận sự vượt trội của phương pháp đề xuất dựa trên một số tiêu chí đánh giá như độ chính xác, tỷ lệ phát hiện đúng và tỷ lệ báo động sai.

Từ khóa

#Nhận diện đối tượng #Hình ảnh viễn thám #SIFT #RANSAC #Thuật toán tăng trưởng vùng.

Tài liệu tham khảo

Aldoma A, Tombari F, Di Stefano L, and Vincze M (2012) "A global hypotheses verification method for 3d object recognition," in European conference on computer vision pp. 511–524. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110:346–359 Borotschnig H, Paletta L, Prantl M, Pinz A (2000) Appearance-based active object recognition. Image Vis Comput 18:715–727 Cheng L, Pian Y, Chen Z, Jiang P, Liu Y, Chen G, … Li M (2016) Hierarchical filtering strategy for registration of remote sensing images of coral reefs. IEEE J Selected Topics Appl Earth Observ Remote Sensing 9:1–10 Cherloo MN, Shiri M, Daliri MR (2020) "an enhanced HMAX model in combination with SIFT algorithm for object recognition," signal. Image and Video Processing 14:425–433 Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 6:19959–19967 Ferrari V, Tuytelaars T, Van Gool L (2006) Simultaneous object recognition and segmentation from single or multiple model views. Int J Comput Vis 67:159–188 Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395 Flitton GT, Breckon TP, and Bouallagu NM, "Object recognition using 3d sift in complex ct volumes," in BMVC, 2010, pp. 1–12. Freixenet J, Muñoz X, Raba D, Martí J, and Cufí X (2002) "Yet another survey on image segmentation: Region and boundary information integration," Eur Conf Comput Vision, pp. 408–422 Gilani SAM (2008) "Object recognition by modified scale invariant feature transform," in Semantic Media Adaptation and Personalization, 2008. SMAP'08. Third International Workshop on.33–39. Guo H and Chen L (2016) "Object Recognition Based on MSER and SIFT," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 14 Gupta S, Kumar M, Garg A (2019) Improved object recognition results using SIFT and ORB feature detector. Multimed Tools Appl 78:34157–34171 Hossein-Nejad Z, Nasri M (2017) RKEM: redundant Keypoint elimination method in image registration. IET Image Process 11:273–284 Hossein-Nejad Z, Nasri M (2017) A review on image registration methods, concepts and applications. J Mach Vision Image Process:39–67 Hossein-Nejad Z, Nasri M (2017) An adaptive image registration method based on SIFT features and RANSAC transform. Comp Electrical Eng 62:524–537 Hossein-Nejad Z, Nasri M (2018) A-RANSAC: adaptive random sample consensus method in multimodal retinal image registration. Biomed Signal Process Control 45:325–338 Hossein-Nejad Z and Nasri M (2019) "Copy-Move Image Forgery Detection Using Redundant Keypoint Elimination Method," in Cryptographic and Information Security Approaches for Images and Videos, S. Ramakrishnan, Ed. Boca Raton: CRC Press,. 773–797 Hossein-Nejad Z and Nasri M (2019) "Retinal Image Registration based on Auto-Adaptive SIFT and Redundant Keypoint Elimination Method," in 2019 27th Iranian Conference on Electrical Engineering (ICEE), pp. 1294–1297. Hossein-Nejad Z, Agahi H, Mahmoodzadeh A (2020) Detailed Review of the Scale Invariant Feature Transform (SIFT) Algorithm; Concepts, Indices and Applications. J Mach Vision Image Process 7:165–190 Hu X, Tang Y, and Zhang Z (2008) "Video object matching based on SIFT algorithm," in Neural Networks and Signal Processing, 2008 International Conference onpp. 412–415. Johnson AE and Hebert M (1997) "Recognizing objects by matching oriented points," in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. pp. 684–689 Kamdi S, Krishna R (2012) Image segmentation and region growing algorithm. Int J Comput Technol Electron Eng (IJCTEE) 2 Kimmel R, Zhang C, Bronstein A, Bronstein M (2011) Are MSER features really interesting? IEEE Trans Pattern Anal Mach Intell 33:2316–2320 Kulkarni A, Jagtap J, Harpale V (2013) Object recognition with ORB and its implementation on FPGA. Int J Adv Comput Res 3:164 Lee H, Liu C-Y, Lin C-J, Huang C-F, Deng R-W, Li T-HS (2014) Implementation of real-time object recognition system for home-service robot by integrating SURF and BRISK. 2014 IEEE Int Conf Syst Sci Eng (ICSSE):273–278 Leng L, Zhang J, Khan MK, Chen X, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. Int J Phys Sci 5:2543–2554 Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76:333–354 Li Q, Wang G, Liu J, Chen S (2009) Robust scale-invariant feature matching for remote sensing image registration. Geosci Remote Sensing Lett. IEEE 6:287–291 Liu P, Yu H, Cang S (2019) Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dynamics 98:1447–1464 Loncomilla P (2016) Object recognition using local invariant features for robotic applications: a survey. Pattern Recogn 60:499–514 Lowe DG (1999) "Object recognition from local scale-invariant features," in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, 1150–1157. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110 Luo S, Mou W, Althoefer K, Liu H (2015) Novel tactile-SIFT descriptor for object shape recognition. IEEE Sensors J 15:5001–5009 Mian AS, Bennamoun M, Owens R (2006) Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans Pattern Anal Mach Intell 28:1584–1601 Nasir H, Stankovic V, Marshall S (2010) "image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus," in 18th European signal processing conference (EUSIPCO-2010) Aalborg Denmark Pavel FA, Wang Z, and Feng DD(2009) "Reliable object recognition using SIFT features," in Multimedia Signal Processing, MMSP'09. IEEE International Workshop on, 2009, pp. 1–6. Sedaghat A, Ebadi H (2015) Remote sensing image matching based on adaptive binning SIFT descriptor. IEEE Trans Geosci Remote Sensing Technol Appl 53:5283–5293 Seidenari L, Serra G, Bagdanov AD, Del Bimbo A (2014) Local pyramidal descriptors for image recognition. IEEE Trans Pattern Anal Mach Intell 36:1033–1040 Shah SAA, Bennamoun M, Boussaid F (2016) A novel feature representation for automatic 3D object recognition in cluttered scenes. Neurocomputing 205:1–15 Shweta Yakkali VN, Tikone N, Ingle D (2015) Robust Object Detection and Tracking Using Sift Algorithm. Int J Adv Res Comp Sci Software Eng 5:683–688 Sirmacek B, Unsalan C (2009) Urban-area and building detection using SIFT keypoints and graph theory. IEEE Trans Geosci Remote Sens 47:1156–1167 Sun L, Zhao C, Yan Z, Liu P, Duckett T, Stolkin R (2018) A novel weakly-supervised approach for RGB-D-based nuclear waste object detection. IEEE Sensors J 19:3487–3500 Tao C, Tan Y, Cai H, Tian J (2011) Airport detection from large IKONOS images using clustered SIFT keypoints and region information. IEEE Geosci Remote Sens Lett 8:128–132 Tranos Zuva OOO, Ojo SO, Ngwir SM (2011) Image segemntation available techniques, developments and open issues. Canad J Image Process Comput Vision.2: 20–29 Wang C, Yang F, Wang H, Guo P, Hou J (2019) Urban House Detection Using SAM and SIFT on Hyperspectral Remote Sensing Images, J Phys Conf Ser:032029 Wang S, You H, Fu K (2012) BFSIFT: a novel method to find feature matches for SAR image registration. IEEE Geosci Remote Sens Lett 9:649–653 Xie B, Liu Y, Zhang H, Yu J (2016) A novel supervised approach to learning efficient kernel descriptors for high accuracy object recognition. Neurocomputing 182:94–101 Ye Y, Shan J (2014) A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences. ISPRS J Photogramm Remote Sens 90:83–95 Zhang S, Wang C, Chan S-C, Wei X, Ho C-H (2015) New object detection, tracking, and recognition approaches for video surveillance over camera network. IEEE Sensors J 15:2679–2691 Zhang Y, Chu J, Leng L, Miao J (2020) Mask-refined R-CNN: a network for refining object details in instance segmentation. Sensors 20:1010 Zhu C, Jiang T (2003) Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images. NeuroImage 18:685–696 Zohrevand A, Ahmadyfard A, Pouyan A, and Imani Z (2014) "A SIFT based object recognition using contextual information," in Intelligent Systems (ICIS), 2014 Iranian Conference on pp. 1–4. Zucker SW (1976) Region growing: childhood and adolescence. Comput Graphics Image Process 5:382–399