Abstract
Gradient-based descriptors have proven successful in a wide variety of applications. Their standard implementations usually assume that the input images have been acquired using classic perspective cameras. In practice many real-world systems make use of wide angle cameras which allow to obtain wider Fields of View (FOV) but introduce radial distortion which breaks the rectilinear assumption. The most straightforward way to overcome such a problem is to compensate the distortion by unwarping the original image prior to computing the descriptor. The rectification process, however, is computationally expansive and introduces artefacts which can deceive the subsequent analysis (e.g., feature matching). We propose the Distortion Adaptive Descriptors (DAD), a new paradigm to correctly compute local descriptors directly in the distorted domain. We combine the DAD with existing techniques to correctly estimate the gradient of distorted images and hence derive a set of SIFT and HOG-based descriptors. Experiments show that the DAD paradigm allows to improve the matching ability of the SIFT and HOG descriptors when they are computed directly in the distorted domain.
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Furnari, A., Farinella, G.M., Puglisi, G., Bruna, A.R., Battiato, S.: Affine Region Detectors on the Fisheye Domain. In: IEEE International Conference on Image Processing (2014)
Miyamoto, K.: Fish eye lens. Journal of the Optical Society of America, 2–3 (1964)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms. In: Proceedings of the international conference on Multimedia, pp. 1469–1472, (2010)
Puig, L., Guerrero, J.J.: Omnidirectional Vision Systems. Springer (2013)
Baker, S., Nayar, S.K.: A theory of catadioptric image formation. In: International Conference on Computer Vision, pp. 35–42 (1998)
Lourenço, M., Barreto, J.P., Vasconcelos, F.: sRD-SIFT: keypoint detection and matching in images with radial distortion. IEEE Transactions on Robotics 28(3), 752–760 (2012)
Cinaroglu, I., Bastanlar, Y.: A direct approach for human detection with catadioptric omnidirectional cameras. In: Signal Processing and Communications Applications Conference, pp. 2275–2279 (2014)
Cruz-Mota, J., Bogdanova, I., Paquier, B., Bierlaire, M., Thiran, J.: Scale invariant feature transform on the sphere: theory and applications. International Journal of Computer Vision 98(2), 217–241 (2011)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110
Furnari, A., Farinella, G.M., Bruna, A.R., Battiato, S.: Generalized Sobel filters for gradient estimation of distorted images. In: The International Conference on Image Processing (submitted 2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition 1, 886–893 (2005)
Hughes, C., Glavin, M., Jones, E., Denny, P.: Wideangle camera technology for automotive applications: a review. IET Intelligent Transport Systems 3(1), 19–31 (2009)
Battiato, S., Farinella, G.M., Furnari, A., Puglisi, G., Snijders, A., Spiekstra, J.: A Customized System for Vehicle Tracking and Classification, Expert Systems With Applications (2015)
Battiato, S., Gallo, G., Puglisi, G., Scellato, S.: SIFT features tracking for video stabilization. In: International Conference on Image Analysis and Processing, pp. 825–830 (2007)
Dorko, G., Schmid, C.: Selection of Scale-Invariant Parts for Object Class Recognition. In: International Conference on Computer Vision, pp. 634–640 (2003)
Brown M., Lowe, D.: Recognising Panoramas. In: International Conference on Computer Vision, pp. 1218–1227 (2003)
Fitzgibbon, A.W.: Simultaneous linear estimation of multiple view geometry and lens distortion. In: Computer Vision and Pattern Recognition, vol. 1 (2001)
Mikolajczyk, K., Schmid, C.: Performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence 27(10), 1615–30 (2005)
Liu, C., Yuen, J., Torralba, A.: SIFT flow: dense correspondence across scenes and its applications. Pattern Analysis and Machine Intelligence 33(5) (2011)
Farinella, G.M., Allegra, D., Stanco, F.: A benchmark dataset to study the representations of food images. In: Assistive Computer Vision and Robotics in conjunction with the European Conference on Computer Vision (2011)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Computer Vision and Pattern Recognition, vol. 2 (2006)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)
Felzenszwalb, P.F., Grishick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. Pattern Analysis and Machine Intelligence (2009)
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Furnari, A., Farinella, G.M., Bruna, A.R., Battiato, S. (2015). Distortion Adaptive Descriptors: Extending Gradient-Based Descriptors to Wide Angle Images. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9280. Springer, Cham. https://doi.org/10.1007/978-3-319-23234-8_20
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DOI: https://doi.org/10.1007/978-3-319-23234-8_20
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