Abstract
Computer vision relies on image features describing points, edges, objects or colour. The book concerns solely so-called hand-made features contrary to learned features which exist in deep learning methods. Image features can be generally divided into global and local methods.
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References
Al-Amri, S.S., Kalyankar, N.V., et al.: Image segmentation by using threshold techniques (2010). arXiv preprint arXiv:1005.4020
Bansal, B., Saini, J.S., Bansal, V., Kaur, G.: Comparison of various edge detection techniques. J. Inf. Oper. Manag. 3(1), 103–106 (2012)
Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1485–1490 (2005)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Computer vision–ECCV 2006, pp. 404–417. Springer (2006)
Bazarganigilani, M.: Optimized image feature selection using pairwise classifiers. J. Artif. Intell. Soft Comput. Res. 1 (2011)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: Binary robust independent elementary features. Comput. Vis. ECCV 2010, 778–792 (2010)
Canny, J.: A computational approach to edge detection. Pattern Anal. Mach. Intell. IEEE Trans. PAMI-8(6), 679–698 (1986). https://doi.org/10.1109/TPAMI.1986.4767851
Chang, Y., Wang, Y., Chen, C., Ricanek, K.: Improved image-based automatic gender classification by feature selection. J. Artif. Intell. Soft Comput. Res. 1(3), 241–253 (2011)
Chatzichristofis, S.A., Boutalis, Y.S.: Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: International Conference on Computer Vision Systems, pp. 312–322. Springer (2008)
Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1197–1203. IEEE (1999)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Damiand, G., Resch, P.: Split-and-merge algorithms defined on topological maps for 3d image segmentation. Gr. Models 65(1), 149–167 (2003)
Derpanis, K.G.: Mean shift clustering. Lecture Notes (2005). http://www.cse.yorku.ca/~kosta/CompVis_Notes/mean_shift.pdf
Evans, C.: Notes on the opensurf library. University of Bristol, Technical Report CSTR-09-001, January (2009)
Fei-Fei Li, M.A., Ranzato, M.: The pascalobject recognition database collection, unannotated databases - 101 object categories (2009)
Georgescu, B., Shimshoni, I., Meer, P.: Mean shift based clustering in high dimensions: a texture classification example. In: Proceedings of Ninth IEEE International Conference on Computer Vision, 2003, pp. 456–463. IEEE (2003)
Glantz, R., Pelillo, M., Kropatsch, W.G.: Matching segmentation hierarchies. Int. J. Pattern Recogn. Artif. Intell. 18(03), 397–424 (2004)
Górecki, P., Sopyła, K., Drozda, P.: Ranking by K-means voting algorithm for similar image retrieval. In: International Conference on Artificial Intelligence and Soft Computing, pp. 509–517. Springer (2012)
Gould, S., Gao, T., Koller, D.: Region-based segmentation and object detection. In: Advances in Neural Information Processing Systems, pp. 655–663 (2009)
Grycuk, R.: Novel visual object descriptor using surf and clustering algorithms. J. Appl. Math. Comput. Mech. 15(3), 37–46 (2016)
Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image indexing by data clustering and inverse document frequency. Beyond Databases. Architectures and Structures 2014, Communications in Computer and Information Science, pp. 374–383. Springer, Berlin, Heidelberg (2014)
Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Romanowski, J.: Improved digital image segmentation based on stereo vision and mean shift algorithm. In: Parallel Processing and Applied Mathematics 2013, Lecture Notes in Computer Science. Springer Berlin Heidelberg (2014). Manuscript accepted for publication
Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Voloshynovskiy, S.: From single image to list of objects based on edge and blob detection. In: International Conference on Artificial Intelligence and Soft Computing, pp. 605–615. Springer (2014)
Gunn, S.R.: On the discrete representation of the laplacian of gaussian. Pattern Recogn. 32(8), 1463–1472 (1999)
Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Comput. Vis. Graph. Image Process. 29(1), 100–132 (1985)
Hare, J.S., Samangooei, S., Lewis, P.H.: Efficient clustering and quantisation of sift features: exploiting characteristics of the sift descriptor and interest region detectors under image inversion. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 2. ACM (2011)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Royal Stat. Soc Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Iakovidou, C., Bampis, L., Chatzichristofis, S.A., Boutalis, Y.S., Amanatiadis, A.: Color and edge directivity descriptor on gpgpu. In: 2015 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 301–308. IEEE (2015)
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision, vol. 5. McGraw-Hill New York (1995)
Jiang, X., Bunke, H.: Edge detection in range images based on scan line approximation. Comput. Vis. Image Underst. 73(2), 183–199 (1999)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Katto, J., Ohta, M.: Novel algorithms for object extraction using multiple camera inputs. In: Proceedings of International Conference on Image Processing, 1996, vol. 1, pp. 863–866. IEEE (1996)
Kirillov, A.: Detecting some simple shapes in images. (2010). http://www.aforgenet.com
Kumar, P.P., Aparna, D.K., PhD, V.R.: Compact descriptors for accurate image indexing and retrieval: Fcth and cedd. Int. J. Eng. Res. Technol. (IJERT) 1, 2278–0181 (2012)
Lowe, D.G.: Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on Computer vision, 1999, vol. 2, pp. 1150–1157. IEEE (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Luo, Y., Duraiswami, R.: Canny edge detection on nvidia cuda. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–8. IEEE (2008)
Macedo-Cruz, A., Pajares-Martinsanz, G., Peñas, M.S.: Unsupervised cassification of images in RGB color model and cluster validation techniques. In: IPCV, pp. 526–532 (2010)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, pp. 281–297. Oakland, CA, USA (1967)
Maintz, T.: Digital and Medical Image Processing. Universiteit Utrecht (2005)
Marugame, A., Yamada, A., Ohta, M.: Focused object extraction with multiple cameras. Circuits Syst. Video Technol. IEEE Trans. 10(4), 530–540 (2000)
Montazer, G.A., Giveki, D.: Content based image retrieval system using clustered scale invariant feature transforms. Optik-Int. J. Light and Electron. Opt. 126(18), 1695–1699 (2015)
Moon, W.K., Shen, Y.W., Bae, M.S., Huang, C.S., Chen, J.H., Chang, R.F.: Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images. IEEE Trans. Med. Imag. 32(7), 1191–1200 (2013)
Nakib, A., Najman, L., Talbot, H., Siarry, P.: Application of graph partitioning to image segmentation. Graph Parti., 249–274 (2013)
Ng, P.C., Henikoff, S.: Sift: predicting amino acid changes that affect protein function. Nucleic Acid. Res. 31(13), 3812–3814 (2003)
Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: Proceedings of the Fourth ACM International Conference on Multimedia, pp. 65–73. ACM (1997)
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Computer Vision–ECCV 2006, pp. 430–443. Springer (2006)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)
Schreiber, J., Schubert, R., Kuhn, V.: Femur detection in radiographs using template-based registration. In: Bildverarbeitung für die Medizin 2006, pp. 111–115. Springer (2006)
Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation-a survey of soft computing approaches. Int. J. Recent Trends Eng. 1(2), 250–254 (2009)
Shrivakshan, G., Chandrasekar, C., et al.: A comparison of various edge detection techniques used in image processing. IJCSI Int. J. Comput. Sci. Issues 9(5), 272–276 (2012)
Šváb, J., Krajník, T., Faigl, J., Přeučil, L.: Fpga based speeded up robust features. In: IEEE International Conference on Technologies for Practical Robot Applications, 2009. TePRA 2009, pp. 35–41. IEEE (2009)
Tamaki, T., Yamamura, T., Ohnishi, N.: Image segmentation and object extraction based on geometric features of regions. In: Electronic Imaging 1999, pp. 937–945. International Society for Optics and Photonics (1998)
Tao, D.: The corel database for content based image retrieval (2009)
Terriberry, T.B., French, L.M., Helmsen, J.: GPU accelerating speeded-up robust features. In: Proceedings International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), pp. 355–362. Citeseer (2008)
Velmurugan, K., Baboo, L.D.S.S.: Content-based image retrieval using surf and colour moments. Global J. Comput. Sci. Technol. 11(10) (2011)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S., et al.: Constrained k-means clustering with background knowledge. ICML 1, 577–584 (2001)
Wang, B., Fan, S.: An improved canny edge detection algorithm. In: Second International Workshop on Computer Science and Engineering, 2009. WCSE 2009. , vol. 1, pp. 497–500. IEEE (2009)
Wani, M.A., Batchelor, B.G.: Edge-region-based segmentation of range images. IEEE Trans. Pattern Anal. Mach. Intell. 16(3), 314–319 (1994). https://doi.org/10.1109/34.276131
Wu, M.N., Lin, C.C., Chang, C.C.: Brain tumor detection using color-based k-means clustering segmentation. In: Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007, vol. 2, pp. 245–250. IEEE (2007)
Wu, Q., Yu, Y.: Two-level image segmentation based on region and edge integration. In: DICTA, pp. 957–966 (2003)
Yoon, Y., Ban, K.D., Yoon, H., Kim, J.: Blob extraction based character segmentation method for automatic license plate recognition system. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2192–2196. IEEE (2011)
Young, R.A.: The gaussian derivative model for spatial vision: I. retinal mechanisms. Spat. Vis. 2(4), 273–293 (1987)
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Scherer, R. (2020). Feature Detection. In: Computer Vision Methods for Fast Image Classification and Retrieval. Studies in Computational Intelligence, vol 821. Springer, Cham. https://doi.org/10.1007/978-3-030-12195-2_2
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