Attention-based mechanism for SuperPoint feature point extraction in endoscopy
DOI: 10.23977/acss.2024.080311 | Downloads: 5 | Views: 173
Author(s)
Mingyue Zhang 1
Affiliation(s)
1 School of Information and Electronic Technology, Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province, Jiamusi University, Jiamusi, China
Corresponding Author
Mingyue ZhangABSTRACT
Routine endoscopes have been widely used in medical diagnosis. Three-dimensional (3D) modelling reconstruction of endoscopic images has become the development direction of future medical domain. Local feature extraction and matching is a key step for 3D modelling reconstruction. Handcrafted local features such as SIFT, SURF, ORB, are still a predominant tool for such tasks. Due to the special environment of endoscopes, there are generally weak textures and large lighting changes, which make traditional feature point extraction algorithms unable to extract feature points well. We explore the potential of the self-supervised method SuperPoint. Many existing works have shown the benefits of enhancing spatial encoding. We propose a new architecture unit, in which the SE attention mechanism module is proposed, which can explicitly model the interdependence between convolutional feature channels to improve the network's representation ability. The experimental results show that this multi-scale channel attention feature point extraction algorithm based on SuperPoint has better result and achieves higher matching quality than handcrafted local features and original algorithm in endoscopic images.
KEYWORDS
Attention mechanism, deep learning, self-supervision, local features, endoscopyCITE THIS PAPER
Mingyue Zhang, Attention-based mechanism for SuperPoint feature point extraction in endoscopy. Advances in Computer, Signals and Systems (2024) Vol. 8: 76-83. DOI: http://dx.doi.org/10.23977/acss.2024.080311.
REFERENCES
[1] H. Dubois, J. Creutzfeldt, M. Törnqvist, and M. Bergenmar, 2020, "Patient participation in gastrointestinal endoscopy—From patients' perspectives," Health Expectations, 23, 4, 893-903.
[2] A. Darzi and Y. Munz, 2004, "The impact of minimally invasive surgical techniques," Annu. Rev. Med., 55, 223-237.
[3] F. Chadebecq, F. Vasconcelos, E. Mazomenos, and D. Stoyanov, 2020, "Computer vision in the surgical operating room," Visceral Medicine, 36, 6, 456-462.
[4] C. Fergo, J. Burcharth, H.-C. Pommergaard, N. Kildebro, and J. Rosenberg, 2017, "Three-dimensional laparoscopy vs 2-dimensional laparoscopy with high-definition technology for abdominal surgery: a systematic review," The American Journal of Surgery, 213, 1, 159-170.
[5] A. Zaman, F. Yangyu, M. Irfan, M. S. Ayub, L. Guoyun, and L. Shiya, 2022, "LifelongGlue: Keypoint matching for 3D reconstruction with continual neural networks," Expert Systems with Applications, 195, 116613.
[6] O. L. Barbed, F. Chadebecq, J. Morlana, J. M. Montiel, and A. C. Murillo, 2022, "Superpoint features in endoscopy," in MICCAI Workshop on Imaging Systems for GI Endoscopy, 45-55.
[7] X. Liu et al., 2020, "Extremely dense point correspondences using a learned feature descriptor," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 4847-4856.
[8] D. DeTone, T. Malisiewicz, and A. Rabinovich, 2018, "Superpoint: Self-supervised interest point detection and description," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 224-236.
[9] J. Hu, L. Shen, and G. Sun, 2018, "Squeeze-and-excitation networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 7132-7141.
[10] Q. Liu, J. Zhang, J. Liu, and Z. Yang, 2022, "Feature extraction and classification algorithm, which one is more essential? An experimental study on a specific task of vibration signal diagnosis," International Journal of Machine Learning and Cybernetics, 13, 6, 1685-1696.
[11] A. Witkin, 1984, "Scale-space filtering: A new approach to multi-scale description," in ICASSP'84. IEEE international conference on acoustics, speech, and signal processing, 9, 150-153.
[12] H. Bay, T. Tuytelaars, and L. Van Gool, 2006, "Surf: Speeded up robust features," in Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, 404-417.
[13] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, 2011, "ORB: An efficient alternative to SIFT or SURF," in 2011 International conference on computer vision, 2564-2571.
[14] D. G. Viswanathan, 2009, "Features from accelerated segment test (fast)," in Proceedings of the 10th workshop on image analysis for multimedia interactive services, London, UK, 6-8.
[15] M. Calonder, V. Lepetit, C. Strecha, and P. Fua, 2010, "Brief: Binary robust independent elementary features," in Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece 778-792.
[16] Y. Tian, B. Fan, and F. Wu, 2017, "L2-net: Deep learning of discriminative patch descriptor in euclidean space," in Proceedings of the IEEE conference on computer vision and pattern recognition, 661-669.
[17] K. B. Ozyoruk et al., 2021, "EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos," Medical image analysis, 71, 102058.
[18] H. Borgli et al., 2020, "HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy," Scientific data, 7, 1, 283.
[19] K. Mikolajczyk and C. Schmid, 2005, "A performance evaluation of local descriptors," IEEE transactions on pattern analysis machine intelligence, 27, 10, 1615-1630.
Downloads: | 19518 |
---|---|
Visits: | 297777 |
Sponsors, Associates, and Links
-
Power Systems Computation
-
Internet of Things (IoT) and Engineering Applications
-
Computing, Performance and Communication Systems
-
Journal of Artificial Intelligence Practice
-
Journal of Network Computing and Applications
-
Journal of Web Systems and Applications
-
Journal of Electrotechnology, Electrical Engineering and Management
-
Journal of Wireless Sensors and Sensor Networks
-
Journal of Image Processing Theory and Applications
-
Mobile Computing and Networking
-
Vehicle Power and Propulsion
-
Frontiers in Computer Vision and Pattern Recognition
-
Knowledge Discovery and Data Mining Letters
-
Big Data Analysis and Cloud Computing
-
Electrical Insulation and Dielectrics
-
Crypto and Information Security
-
Journal of Neural Information Processing
-
Collaborative and Social Computing
-
International Journal of Network and Communication Technology
-
File and Storage Technologies
-
Frontiers in Genetic and Evolutionary Computation
-
Optical Network Design and Modeling
-
Journal of Virtual Reality and Artificial Intelligence
-
Natural Language Processing and Speech Recognition
-
Journal of High-Voltage
-
Programming Languages and Operating Systems
-
Visual Communications and Image Processing
-
Journal of Systems Analysis and Integration
-
Knowledge Representation and Automated Reasoning
-
Review of Information Display Techniques
-
Data and Knowledge Engineering
-
Journal of Database Systems
-
Journal of Cluster and Grid Computing
-
Cloud and Service-Oriented Computing
-
Journal of Networking, Architecture and Storage
-
Journal of Software Engineering and Metrics
-
Visualization Techniques
-
Journal of Parallel and Distributed Processing
-
Journal of Modeling, Analysis and Simulation
-
Journal of Privacy, Trust and Security
-
Journal of Cognitive Informatics and Cognitive Computing
-
Lecture Notes on Wireless Networks and Communications
-
International Journal of Computer and Communications Security
-
Journal of Multimedia Techniques
-
Automation and Machine Learning
-
Computational Linguistics Letters
-
Journal of Computer Architecture and Design
-
Journal of Ubiquitous and Future Networks