HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
Source abstract
Abstract
We propose a simple and robust HSV color-space-based algorithm that can automatically extract object position information without human intervention or prior knowledge. In manufacturing sites with high variability, it is difficult to recognize products through robot machine vision, especially in terms of extracting object information accurately, owing to various environmental factors such as the noise around objects, shadows, light reflections, and illumination interferences. The proposed algorithm, which does not require users to reset the HSV color threshold value whenever a product is changed, uses ROI referencing method to solve this problem. The algorithm automatically identifies the object’s location by using the HSV color-space-based ROI random sampling, ROI similarity comparison, and ROI merging. The proposed system utilizes an IoT device with several modules for the detection, analysis, control, and management of object data. The experimental results show that the proposed algorithm is very useful for industrial automation applications under complex and highly variable manufacturing environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Visual Attention and Saliency Detection · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
Department of Computer Engineering, Chungnam National University, Daejeon 34134, Korea
Funding
Ministry of SMEs and Startups and Ministry of Science and ICT, South Korea
References (52)
Show source-provided references
1. Industry 4.010.1007/s12599-014-0334-4 ↗
2. “Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges.”10.1109/ACCESS.2017.2783682 ↗
3. Park, K.T., Son, Y.H., Ko, S.W., and Noh, S.D. (2021). Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory. Appl. Sci., 11.10.3390/app11072977 ↗
4. IoT-based open manufacturing service platform for mass personalization
5. Wang, W., Chen, Y., Li, R., and Jia, Y. (2019). Learning and Comfort in Human–Robot Interaction: A Review. Appl. Sci., 9.10.3390/app9235152 ↗
6. Okarma, K. (2020). Applications of Computer Vision in Automation and Robotics. Appl. Sci., 10.10.3390/app10196783 ↗
7. Kang, H.C., Han, H.Y., Bae, H.C., Lee, E.S., Kim, M.G., Son, J., Kim, H., and Kim, Y.K. (2019, January 16–18). HSV Color Space Based Robot Grasping for Personalized Manufacturing Services. Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.10.1109/ICTC46691.2019.8939796 ↗
8. Robust spatio-temporal multimodal background subtraction for video surveillance10.1117/1.3002325 ↗
9. Chiu, S.-Y., Chiu, C.-C., and Xu, S.S.-D. (2018). A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis. Appl. Sci., 8.10.3390/app8060885 ↗
10. Piccardi, M. (2004, January 10–13). Background subtraction techniques: A review. Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), Hague, The Netherlands.
11. Tamersoy, B. (2009). Background Subtraction, The University of Texas at Austin.
12. Roy, P., Dutta, S., Dey, N., Dey, G., Chakraborty, S., and Ray, R. (2014, January 10–11). Adaptive thresholding: A comparative study. Proceedings of the International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India.10.1109/ICCICCT.2014.6993140 ↗
13. Adaptive thresholding using the integral image10.1080/2151237X.2007.10129236 ↗
14. Bouttefroy, P.L.M., Bouzerdoum, A., Phung, S.L., and Beghdadi, A. (2010, January 14–19). On the analysis of background subtraction techniques using Gaussian mixture models. Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, USA.10.1109/ICASSP.2010.5495760 ↗
15. Stauffer, C., and Grimson, W.E.L. (1999, January 23–25). Adaptive background mixture models for real-time tracking. Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA.
16. Illumination-invariant background subtraction: Comparative review, models, and prospects10.1109/ACCESS.2017.2699227 ↗
17. Jacques, J.C.S., Jung, C.R., and Musse, S.R. (2006, January 8–11). A background subtraction model adapted to illumination changes. Proceedings of the 2006 International Conference on Image Processing, Atlanta, GA, USA.
18. Parks, D.H., and Fels, S.S. (2008, January 1–3). Evaluation of Background Subtraction Algorithm with Post-Processing. Proceedings of the 2008 5th International Conference on Advanced Video & Signal Based Surveillance, Santa Fe, NM, USA.10.1109/AVSS.2008.19 ↗
19. Cast shadow segmentation using invariant color features10.1016/j.cviu.2004.03.008 ↗
20. Determining optical flow10.1016/0004-3702(81)90024-2 ↗
21. Ren, X., and Ramanan, D. (2013, January 23–28). Histograms of sparse codes for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2013.417 ↗
22. Yoon, H.S., Bae, Y.L., and Yang, Y.K. (2000, January 13–14). A Study on Image Retrieval Using Space Information of Color Histogram. Proceedings of the Korea Information Processing Society Conference, Daejeon, Korea.
23. Tuytelaars, T., and Mikolajczyk, K. (2008). Local Invariant Feature Detectors: A Survey, Now Publishers Inc.10.1561/9781601981394 ↗
24. Harris, C.G., and Stephens, M. (September, January 31). A combined corner and edge detector. Proceedings of the Alvey Vision Conference, Manchester, UK.
25. Dalal, N., and Triggs, B. (2005, January 20–25). Histograms of oriented gradients for human detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, USA.
26. Suleiman, A., and Sze, V. (2014, January 20–22). Energy-efficient HOG-based object detection at 1080HD 60 fps with multi-scale support. Proceedings of the 2014 IEEE Workshop on Signal Processing Systems (SiPS), Belfast, UK.10.1109/SiPS.2014.6986096 ↗
27. Distinctive image features from scale-invariant keypoints10.1023/B:VISI.0000029664.99615.94 ↗
28. Lowe, D.G. (1999, January 20–27). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision(ICCV), Kerkyra, Greece.10.1109/ICCV.1999.790410 ↗
29. A threshold selection method from gray-level histograms10.1109/TSMC.1979.4310076 ↗
30. GrabCut–Interactive Foreground Extraction using Iterated Graph Cut10.1145/1015706.1015720 ↗
31. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision10.1109/TPAMI.2004.60 ↗
32. Yi, F., and Moon, I. (2012, January 19–20). Image segmentation: A survey of graph-cut methods. Proceedings of the IEEE International Conference on Systems and Informatics (ICSAI2012), Yantai, China.10.1109/ICSAI.2012.6223428 ↗
33. Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21–23). Understanding of a convolutional neural network. Proceedings of the IEEE International Conference on Engineering and Technology (ICET), Antalya, Turkey.10.1109/ICEngTechnol.2017.8308186 ↗
34. Research on Robust Face Recognition against Lighting Variation using CNN
35. Varghese, A., Gubbi, J., Ramaswamy, A., and Balamuralidhar, P. (2018, January 8–14). ChangeNet: A deep learning architecture for visual change detection. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.10.1007/978-3-030-11012-3_10 ↗
36. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
37. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
38. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
39. Object detection with deep learning: A review10.1109/TNNLS.2018.2876865 ↗
40. Deep Learning-Based Illumination Estimation Using Light Source Classification10.1109/ACCESS.2020.2992121 ↗
41. How to train a cat: Learning canonical appearance transformations for direct visual localization under illumination change10.1109/LRA.2018.2799741 ↗
42. Comparative study of skin color detection and segmentation in HSV and YCbCr color space10.1016/j.procs.2015.07.362 ↗
43. Ganesan, P., Rajini, V., Sathish, B.S., and Shaik, K.B. (2014, January 10–11). HSV color space based segmentation of region of interest in satellite images. Proceedings of the International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India.10.1109/ICCICCT.2014.6992938 ↗
44. Herodotou, N., Plataniotis, K.N., and Venetsanopoulos, A.N. (1998, January 5–6). A color segmentation scheme for object-based video coding. Proceedings of the IEEE Symposium on Advances in Digital Filtering and Signal Processing, Symposium Proceedings, Victoria, BC, Canada.
45. Sural, S., Qian, G., and Pramanik, S. (2002, January 22–25). Segmentation and histogram generation using the HSV color space for image retrieval. Proceedings of the IEEE International Conference on Image Processing, Rochester, NY, USA.
46. Li, D. (2008). Encyclopedia of Microfluidics and Nanofluidics, Springer Science & Business Media.10.1007/978-0-387-48998-8 ↗
47. Instrument Society of America (ISA), ANSI/ISA-88.01-1995 (1995). “Batch Control Part1: Models and Terminology”, Instrument Society of America (ISA).
48. Bradski, G., and Kaehler, A. (2000). OpenCV. Dr. Dobb’s J. Softw. Tools, 3.
49. Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., and Yang, R. (2019, January 16–19). IoU Loss for 2D/3D Object Detection. Proceedings of the IEEE International Conference on 3D Vision (3DV), IEEE, Quebec, QC, Canada.10.1109/3DV.2019.00019 ↗
50. Scalera, L., Seriani, S., Gasparetto, A., and Gallina, P. (2019). Non-Photorealistic Rendering Techniques for Artistic Robotic Painting. Robotics, 8.10.3390/robotics8010010 ↗
51. Image Preprocessing for Artistic Robotic Painting. Inventions
52. Robotic grasp detection based on image processing and random forest10.1007/s11042-019-08302-9 ↗