Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
GPT-4o
Gemini 2.5 Pro Pro
o3 Pro
GPT-4.1 Pro
DeepSeek R1 via Azure Pro
2000 character limit reached

CP HDR: A feature point detection and description library for LDR and HDR images (2403.19935v1)

Published 29 Mar 2024 in cs.CV

Abstract: In computer vision, characteristics refer to image regions with unique properties, such as corners, edges, textures, or areas with high contrast. These regions can be represented through feature points (FPs). FP detection and description are fundamental steps to many computer vision tasks. Most FP detection and description methods use low dynamic range (LDR) images, sufficient for most applications involving digital images. However, LDR images may have saturated pixels in scenes with extreme light conditions, which degrade FP detection. On the other hand, high dynamic range (HDR) images usually present a greater dynamic range but FP detection algorithms do not take advantage of all the information in such images. In this study, we present a systematic review of image detection and description algorithms that use HDR images as input. We developed a library called CP_HDR that implements the Harris corner detector, SIFT detector and descriptor, and two modifications of those algorithms specialized in HDR images, called SIFT for HDR (SfHDR) and Harris for HDR (HfHDR). Previous studies investigated the use of HDR images in FP detection, but we did not find studies investigating the use of HDR images in FP description. Using uniformity, repeatability rate, mean average precision, and matching rate metrics, we compared the performance of the CP_HDR algorithms using LDR and HDR images. We observed an increase in the uniformity of the distribution of FPs among the high-light, mid-light, and low-light areas of the images. The results show that using HDR images as input to detection algorithms improves performance and that SfHDR and HfHDR enhance FP description.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (72)
  1. C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point detectors,” International Journal of computer vision, vol. 37, no. 2, pp. 151–172, 2000.
  2. L. Chermak, N. Aouf, and M. Richardson, “HDR imaging for feature tracking in challenging visibility scenes,” Kybernetes, vol. 43, no. 8, pp. 1129–1149, Jan 2014. [Online]. Available: https://doi.org/10.1108/K-07-2014-0137
  3. B. T. Andrade, C. M. Mendes, J. de Oliveira Santos Jr, O. R. P. Bellon, and L. Silva, “3D preserving XVIII century Barroque masterpiece: Challenges and results on the digital preservation of Aleijadinho’s sculpture of the Prophet Joel,” Journal of Cultural Heritage, vol. 13, no. 2, pp. 210–214, 2012.
  4. A. Rana, G. Valenzise, and F. Dufaux, “Evaluation of feature detection in HDR based imaging under changes in illumination conditions,” in 2015 IEEE International Symposium on Multimedia (ISM), 2015, pp. 289–294.
  5. C. Aguilera, F. Barrera, F. Lumbreras, A. D. Sappa, and R. Toledo, “Multispectral image feature points,” Sensors, vol. 12, no. 9, pp. 12 661–12 672, 2012.
  6. B. Přibyl, A. Chalmers, and P. Zemčík, “Feature point detection under extreme lighting conditions,” in Proceedings of the 28th Spring Conference on Computer Graphics, 2012, pp. 143–150.
  7. B. Přibyl, A. Chalmers, P. Zemčík, L. Hooberman, and M. Čadík, “Evaluation of feature point detection in high dynamic range imagery,” Journal of Visual Communication and Image Representation, vol. 38, pp. 141–160, 2016.
  8. W. A. L. J. de Melo, J. A. O. de Tavares, D. O. Dantas, and B. T. Andrade, “Improving feature point detection in high dynamic range images,” in 2018 IEEE Symposium on Computers and Communications (ISCC).   IEEE, 2018, pp. 00 091–00 096.
  9. Y. Zhuang and L. Liang, “A novel local invariant feature extraction method for high-dynamic range images,” in 2019 2nd International Conference on Safety Produce Informatization (IICSPI).   IEEE, 2019, pp. 307–310.
  10. R. Mukherjee, M. Bessa, P. Melo-Pinto, and A. Chalmers, “Object detection under challenging lighting conditions using high dynamic range imagery,” IEEE Access, vol. 9, pp. 77 771–77 783, 2021.
  11. A. S. Nascimento., W. A. L. de Jesus Melo., B. T. Andrade., and D. O. Dantas., “Evaluation of a local descriptor for HDR images,” in Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, INSTICC.   SciTePress, 2022, pp. 299–306.
  12. A. S. Nascimento, W. A. L. de Jesus Melo, D. O. Dantas, and B. T. Andrade, “Feature point detection in HDR images based on coefficient of variation,” Multimedia Tools and Applications, Jul 2023. [Online]. Available: https://doi.org/10.1007/s11042-023-16055-9
  13. K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” in 12th International Conference on Evaluation and Assessment in Software Engineering (EASE) 12, 2008, pp. 1–10.
  14. B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering–a systematic literature review,” Information and software technology, vol. 51, no. 1, pp. 7–15, 2009.
  15. L. Chermak and N. Aouf, “Enhanced feature detection and matching under extreme illumination conditions with a HDR imaging sensor,” in 2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS), 2012, pp. 64–69.
  16. G. Kontogianni, E. Stathopoulou, A. Georgopoulos, and A. Doulamis, “HDR imaging for feature detection on detailed architectural scenes.” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2015.
  17. R. Mantiuk, K. Myszkowski, and H.-P. Seidel, “A perceptual framework for contrast processing of high dynamic range images,” ACM Transactions on Applied Perception (TAP), vol. 3, pp. 286–308, 2006.
  18. K. Jagadish and E. Sinzinger, “Image matching using high dynamic range images and radial feature descriptorsffff,” in Advances in Visual Computing, G. Bebis, R. Boyle, B. Parvin, D. Koracin, P. Remagnino, F. Porikli, J. Peters, J. Klosowski, L. Arns, Y. K. Chun, T.-M. Rhyne, and L. Monroe, Eds.   Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 359–369.
  19. E. D. Sinzinger, “A model-based approach to junction detection using radial energy,” Pattern Recognition, vol. 41, no. 2, pp. 494–505, 2008. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320307003056
  20. L. Worthy and E. Sinzinger, “Scene identification using invariant radial feature descriptors,” in Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS ’07), 2007, pp. 39–39.
  21. E. O. Ige, K. Debattista, and A. Chalmers, “Towards HDR based facial expression recognition under complex lighting,” in Proceedings of the 33rd Computer Graphics International, ser. CGI ’16.   New York, NY, USA: Association for Computing Machinery, 2016, p. 49–52. [Online]. Available: https://doi.org/10.1145/2949035.2949048
  22. R. P. Kovaleski and M. M. Oliveira, “High-quality reverse tone mapping for a wide range of exposures,” in 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images, 2014, pp. 49–56.
  23. Y. Huo, F. Yang, L. Dong, and V. Brost, “Physiological inverse tone mapping based on retina response,” The Visual Computer, vol. 30, no. 5, pp. 507–517, May 2014. [Online]. Available: https://doi.org/10.1007/s00371-013-0875-4
  24. G. Eilertsen, J. Kronander, G. Denes, R. K. Mantiuk, and J. Unger, “HDR image reconstruction from a single exposure using deep CNNs,” ACM Trans. Graph., vol. 36, no. 6, nov 2017. [Online]. Available: https://doi.org/10.1145/3130800.3130816
  25. D. Marnerides, T. Bashford-Rogers, J. Hatchett, and K. Debattista, “ExpandNet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content,” Computer Graphics Forum, vol. 37, no. 2, pp. 37–49, 2018. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13340
  26. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” 2016.
  27. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot MultiBox detector,” in Computer Vision – ECCV 2016.   Springer International Publishing, 2016, pp. 21–37. [Online]. Available: https://doi.org/10.1007%2F978-3-319-46448-0_2
  28. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results,” http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html.
  29. ——, “The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,” http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.
  30. C. Harris, M. Stephens et al., “A combined corner and edge detector,” in Alvey vision conference, vol. 15.   Citeseer, 1988, pp. 10–5244.
  31. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004.
  32. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Computer vision and image understanding, vol. 110, no. 3, pp. 346–359, 2008.
  33. P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in ACM SIGGRAPH 2008 classes, 2008, pp. 1–10.
  34. K. Zuiderveld, “Contrast limited adaptive histogram equalization,” Graphics gems, pp. 474–485, 1994.
  35. T. Ohdake and H. Chikatsu, “3D modelling of high relief sculpture using image-based integrated measurement system,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 36, no. 5/W17, p. 6, 2005.
  36. J. Shi et al., “Good features to track,” in 1994 Proceedings of IEEE conference on computer vision and pattern recognition.   IEEE, 1994, pp. 593–600.
  37. E. Rosten and T. Drummond, “Fusing points and lines for high performance tracking,” in Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1, vol. 2.   Ieee, 2005, pp. 1508–1515.
  38. A. M. Reza, “Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement,” Journal of VLSI signal processing systems for signal, image and video technology, vol. 38, no. 1, pp. 35–44, 2004.
  39. E. Reinhard and K. Devlin, “Dynamic range reduction inspired by photoreceptor physiology,” IEEE transactions on visualization and computer graphics, vol. 11, no. 1, pp. 13–24, 2005.
  40. R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” in Proceedings of the 29th annual conference on Computer graphics and interactive techniques, 2002, pp. 249–256.
  41. T. O. Aydın, R. Mantiuk, and H.-P. Seidel, “Extending quality metrics to full luminance range images,” in Human vision and electronic imaging xiii, vol. 6806.   International Society for Optics and Photonics, 2008, p. 68060B.
  42. A. Rana, G. Valenzise, and F. Dufaux, “An evaluation of HDR image matching under extreme illumination changes,” in 2016 Visual Communications and Image Processing (VCIP), 2016, pp. 1–4.
  43. A. Alahi, R. Ortiz, and P. Vandergheynst, “Freak: Fast retina keypoint,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition.   Ieee, 2012, pp. 510–517.
  44. S. Leutenegger, M. Chli, and R. Y. Siegwart, “Brisk: Binary robust invariant scalable keypoints,” in 2011 International conference on computer vision.   Ieee, 2011, pp. 2548–2555.
  45. A. Rana, G. Valenzise, and F. Dufaux, “Optimizing tone mapping operators for keypoint detection under illumination changes,” in 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), 2016, pp. 1–6.
  46. ——, “Learning-based adaptive tone mapping for keypoint detection,” in 2017 IEEE International Conference on Multimedia and Expo (ICME), 2017, pp. 337–342.
  47. K. Chiu, M. Herf, P. Shirley, S. Swamy, C. Wang, K. Zimmerman et al., “Spatially nonuniform scaling functions for high contrast images,” in Graphics Interface.   Canadian Information Processing Society, 1996, pp. 245–245.
  48. F. Drago, K. Myszkowski, T. Annen, and N. Chiba, “Adaptive logarithmic mapping for displaying high contrast scenes,” in Computer graphics forum, vol. 22, no. 3.   Wiley Online Library, 2003, pp. 419–426.
  49. A. Rana, G. Valenzise, and F. Dufaux, “Learning-based tone mapping operator for image matching,” in 2017 IEEE International Conference on Image Processing (ICIP), Sep. 2017, pp. 2374–2378.
  50. ——, “Learning-based tone mapping operator for efficient image matching,” IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 256–268, 2019.
  51. C.-H. Yeh and M.-H. Lin, “Robust 3D reconstruction using HDR-based SLAM,” IEEE Access, vol. 9, pp. 16 568–16 581, 2021.
  52. R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon, “Kinectfusion: Real-time dense surface mapping and tracking,” in 2011 10th IEEE international symposium on mixed and augmented reality.   Ieee, 2011, pp. 127–136.
  53. J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of RGB-D SLAM systems,” in 2012 IEEE/RSJ international conference on intelligent robots and systems.   IEEE, 2012, pp. 573–580.
  54. R. Mur-Artal and J. D. Tardós, “ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras,” IEEE transactions on robotics, vol. 33, no. 5, pp. 1255–1262, 2017.
  55. A. Albrecht and N. F. Heide, “Improving feature-based visual SLAM in person indoor navigation with HDR imaging,” in 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), Sep. 2019, pp. 369–373.
  56. T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion,” in 15th Pacific Conference on Computer Graphics and Applications (PG’07), 2007, pp. 382–390.
  57. T. Jinno, S. Kuriyama, and M. Okuda, “Tone-mapping for an HDR surveillance system using SIFT features,” in 21st European Signal Processing Conference (EUSIPCO 2013), 2013, pp. 1–5.
  58. E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” in Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, ser. SIGGRAPH ’02.   New York, NY, USA: Association for Computing Machinery, 2002, p. 267–276. [Online]. Available: https://doi.org/10.1145/566570.566575
  59. Y. Li, L. Sharan, and E. H. Adelson, “Compressing and companding high dynamic range images with subband architectures,” ACM Trans. Graph., vol. 24, no. 3, p. 836–844, Jul. 2005. [Online]. Available: https://doi.org/10.1145/1073204.1073271
  60. G. W. Larson, H. Rushmeier, and C. Piatko, “A visibility matching tone reproduction operator for high dynamic range scenes,” IEEE Transactions on Visualization and Computer Graphics, vol. 3, no. 4, pp. 291–306, 1997.
  61. M. Ashikhmin, “A tone mapping algorithm for high contrast images,” in Proceedings of the 13th Eurographics Workshop on Rendering, ser. EGRW ’02.   Goslar, DEU: Eurographics Association, 2002, p. 145–156.
  62. S. Pattanaik and H. Yee, “Adaptive gain control for high dynamic range image display,” in Proceedings of the 18th Spring Conference on Computer Graphics, ser. SCCG ’02.   New York, NY, USA: Association for Computing Machinery, 2002, p. 83–87. [Online]. Available: https://doi.org/10.1145/584458.584472
  63. C. Schlick, “An adaptive sampling technique for multidimensional integration by ray-tracing,” in Photorealistic Rendering in Computer Graphics, P. Brunet and F. W. Jansen, Eds.   Berlin, Heidelberg: Springer Berlin Heidelberg, 1994, pp. 21–29.
  64. R. Mantiuk, S. Daly, and L. Kerofsky, “Display adaptive tone mapping,” ACM Trans. Graph., vol. 27, no. 3, p. 1–10, Aug. 2008. [Online]. Available: https://doi.org/10.1145/1360612.1360667
  65. R. Fattal, “Edge-avoiding wavelets and their applications,” ACM Trans. Graph., vol. 28, no. 3, Jul. 2009. [Online]. Available: https://doi.org/10.1145/1531326.1531328
  66. C. Kiser, E. Reinhard, M. Tocci, and N. Tocci, “Real time automated tone mapping system for HDR video,” in IEEE International Conference on Image Processing, vol. 134.   IEEE Orlando, FL, 2012.
  67. R. Yates and R. Lyons, “DC blocker algorithms [DSP tips & tricks],” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 132–134, 2008.
  68. F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” in Proceedings of the 29th annual conference on Computer graphics and interactive techniques, 2002, pp. 257–266.
  69. A. Chalmers and K. Debattista, “HDR video past, present and future: A perspective,” Signal Processing: Image Communication, vol. 54, pp. 49–55, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S092359651730019X
  70. P. Ledda, A. Chalmers, T. Troscianko, and H. Seetzen, “Evaluation of tone mapping operators using a high dynamic range display,” ACM Trans. Graph., vol. 24, no. 3, p. 640–648, Jul. 2005. [Online]. Available: https://doi.org/10.1145/1073204.1073242
  71. M. Čadík, M. Wimmer, L. Neumann, and A. Artusi, “Evaluation of HDR tone mapping methods using essential perceptual attributes,” Computers & Graphics, vol. 32, no. 3, pp. 330–349, 2008. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0097849308000460
  72. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 10, pp. 1615–1630, 2005.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets