Журнал «Труды Института системного анализа Российской академии наук» - A.E. Zhukovsky, E.E. Limonova, D.P. Nikolaev Exact implementation of common image processing algorithms using fully convolutional networks

Просматривается номер 2018-S1

Data mining and image recognition

N.S. Skoryukina, A.N. Milovzorov, D.V. Polevoy, V.V. Arlazarov Paintings recognition in uncontrolled conditions using one-shot learning

A.E. Zhukovsky Methods for interframe integration of document detection results in a video stream of a mobile device

I.A. Kunina, E. I. Panfilova, M.A. Povolotskiy Zebra-crossing detection on road images using dynamic time warping

O.A. Slavin, V.L. Arlazarov Method for classifying recognized pages of administrative documents on the basis of text key points

O.O. Petrova, K.B. Bulatov Methods of machine-readable zone recognition results post-processing

A.E. Marchenko, E.I. Ershov, D.A. Shepelev, D.S. Sidorchuk, V.P. Bozhkova, D.P. Nikolaev Designing of language of description of observable properties of recognized objects in the absence of samples

Intellectual systems and technologies

E.E. Limonova, N.L. Rzhenev, A.V. Uskov, M.I. Neiman-zade Fast implementation of Hamming distance on VLIW-architectures on the example of Elbrus platform

V.V. Arlazarov, K.B. Bulatov, A.V. Uskov A model of object recognition system in video stream of a mobile device

A.A. Ivanova, S.A. Gladilin, A.E. Zhukovsky, E.L. Pliskin Database for the administrative accounting of scientific publications

A.S. Ingacheva, A.V. Sheshkus, T. S. Chernov, E.E. Limonova, V.V. Arlazarov X-ray computed tomography scanner – a new tool in recognition

N.O. Beshaposhnikov, A.G. Kushnirenko, A.A. Levin A method for auto-calibration of the educational robot control parameters using computer vision library OpenCV

Image and signal processing

A.E. Zhukovsky, E.E. Limonova, D.P. Nikolaev Exact implementation of common image processing algorithms using fully convolutional networks

V.E. Prun Reducing the influence of high-absorbing inclusions on CT reconstructions using algebraic reconstruction technique

B.I. Savelyev, I.B. Mamay, D.P. Nikolaev, V.L. Arlazarov, K.B. Bulatov, N.S. Skoryukina A method of projective transformations graph adjustment for panorama stitching problem for images of planar objects

D.V. Tropin, D.P. Nikolaev, D.G. Slugin The method of image alignment based on sharpness maximization

J.A. Shemiakina, A.E. Zhukovsky, I.A. Konovalenko, D.P. Nikolaev Algorithm for automatic framing of digital images under projective transformation

MACHINE LEARNING

A.V. Gayer, A.V. Sheshkus, Y.S. Chernyshova Augmentation on the fly for the neural networks learning

V.V. Arlazarov, D.P. Matalov, S.A. Usilin Localization of the seal on the identity document image using machine learning approach

A.E. Lynchenko, A.V.Sheshkus, V.L.Arlazarov Identity document classifiaction algorithm based on similarity metric robust to projective distortions

V.A. Malykh, V.A. Lyalin On Classification of Noisy Texts

Y.S. Chernyshova, M.A. Aliev, A.V. Sheshkus Optical font recognition of images captured with mobile devices and its application for detecting identity documents forgery

D.A. Ilin Fast words boundaries localization in text fields for low quality document images

D.E. Ivanov, D.V. Polevoy, D.L. Sholomov Selection of informative elements for the training of a lightweight convolutional neural network classifier in the conditions of a strong imbalance of the training sample


	A.E. Zhukovsky, E.E. Limonova, D.P. Nikolaev Exact implementation of common image processing algorithms using fully convolutional networks
Abstract. We present differentiable implementations of several common image processing algorithms: Canny edge detector, Niblack thresholding and Harris corner detector. The implementations are presented in the form of fully convolutional networks and explicitly arranged exactly to the original algorithms. Usage of such form of algorithms allows to tune their parameters with gradient descend. As a part of these implementations we introduce a generalization of pooling algorithm, which allows to use arbitrary structure element. We also analyze the given architectures and show the connections with contemporary approaches. Keywords: canny edge detector, Harris corner detector, Niblack thresholding, fully convolutional network. PP. 108-116. DOI: 10.14357/20790279180512 References 1. S. Xie and Z. Tu. Holistically-nested edge detection, ICCV, 2015. 2. I. Kokkinos. Pushing the boundaries of boundary detection using deep learning, ICLR, 2016. 3. K.K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool. Convolutional Oriented Boundaries, ECCV, 2016. 4. K.K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool. Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks, TPAMI, 2018. 5. I. Pratikakis, K. Zagoris, G. Barlas, B. Gatos. ICDAR2017 Competition on Document Image Binarization (DIBCO 2017), ICDAR, 2017. 6. K. Lenc, A. Vedaldi. Learning Covariant Feature Detectors, GMLD workshop at ECCV, 2016. 7. M. Jaderberg, K. Simonyan, A. Zisserman, K. Kavukcuoglu. Spatial Transformer Networks, NIPS, 2015. 8. A. Sheshkus, A. Ingacheva, D.P. Nikolaev, Vanishing Points Detection Using Combination of Fast Hough Transform and Deep Learning, Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960H, pp. 1-8, 2018, DOI: 10.1117/12.2310170. 9. Audet C., Dennis J.E. Mesh Adaptive Direct Search Algorithms for Constrained Optimization, SIAM J. Optim. 17, 2006. p.188–217. 10. M. Aliev, D. Nikolaev, A. Saraev. Construction of fast computational schemes for tuning the Niblack binarization algorithm, [Postroenie bystrykh vychislitelnykh skhem nastroyki algoritma binarizatsii Nibleka], in Russian, Proceedings of the ISA RAS, 2014. 11. J. Long, E. Shelhamer, T. Darrell. Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015. 12. W. Niblack. An Introduction to Digital Image Processing, Englewood Cliffs, PrenticeHall, 1986 13. C. Harris, M. Stephens. A Combined Corner and Edge Detector, Alvey Vision Conference, 15, 1988. 14. R. Hahnloser, R. Sarpeshkar, M. A. Mahowald, R. J. Douglas, H. S. Seung. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit, Nature, 405, pp. 947–951, 2000. 15. J. Serra. Image Analysis and Mathematical Morphology, ISBN 0-12-637240-3, 1982. 16. Canny, J. A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6):679–698, 1986. 17. D. Martin, C. Fowlkes, D. Tal, J. Malik. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, Proc. 8th ICCV, 2001. 18. G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors, arXiv:1207.0580, 2012. 19. S. Ioffe, C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, ICML, 2015. 20. K. Simonyan, A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR, 2015.

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