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Regular version of the site
Of all publications in the section: 14
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Article
Savchenko A. Computer Optics. 2013. Vol. 37. No. 2. P. 254-262.

The usage of the probabilistic neural network with homogeneity testing is proposed in image recognition problem. This decision is shown to be optimal in Bayesian terms if the task is formulated as a statistical testing for homogeneity of query and model images' feature sets. The problem of the lack of computing efficiency with many classes and large dimensions of feature set is discovered. The possibility of its overcoming in the case of discrete features is explored by synthesizing the novel recognition criterion with the comparison of the histograms of query and model images. It is shown that a particular case of this criterion is the nearest neighbor rule with popular measures of similarity, namely, chi-square distance and Jensen-Shannon divergence. The results of experimental research in a problem of face recognition with widely used databases (AT&T, JAFFE) are presented. The proposed approach is demonstrated to achieve better recognition accuracy in comparison with conventional solution with reduction the recognition task to the statistical classification.

Added: Jul 1, 2013
Article
Konushin A., Nikitin M., Konushin V. Computer Optics. 2017.

Computer Optics

Added: Feb 7, 2018
Article
Umnov A., Krylov A. S. Computer Optics. 2016. Vol. 40. No. 6. P. 895-903.

In this paper we suggest an algorithm for ringing suppression based on a sparse  representation method. As one of its steps, the suggested method includes image deblurring  based on the Wiener-Hunt deconvolution algorithm. The ringing suppression algorithm uses  the signals' mutual coherence and sparsities analysis when dealing with the ringing effect  based on the sparse representation method. We also analyze the mutual coherence and  sparsities for blurred images and images with white Gaussian noise.

Added: Oct 21, 2017
Article
Savchenko A. Computer Optics. 2012. Vol. 36. No. 1. P. 116-123.

The problem of the choice of algorithms parameters in automatic image recognition is put and solved by ensemble classifiers construction using the maximum posterior probability principle. The new criterion of parameters choice is strictly synthesized for Kullback-Leibler information discrimination and modern SIFT (Scale-Invariant Feature Transform) method of object recognition. The program and results of experimental research in a problem of face recognition with widely used databases (Yale, AT&T) are presented. It is shown that the proposed criterion allows to achieve recognition accuracy equal to the algorithm with the best parameters set, and not only for Kullback-Leibler information discrimination, but also for other popular distances (Euclidean metric, Kullback information divergence).

Added: Feb 4, 2013
Article
Pham Cong T., Копылов А. Computer Optics. 2018. P. 1-8.

We consider here image denoising procedures, based on computationally effective tree-serial parametric dynamic programming procedures, different representations of an image lattice by the set of acyclic graphs and non-convex regularization of a new type which allows to flexibly set a priori preferences. Experimental results in image denoising, as well as comparison with related methods, are provided. A new extended version of multi quadratic dynamic programming procedures for image denoising, proposed here, shows an improved accuracy for images of a different type.

Added: Jul 21, 2018
Article
Савченко А. В. Компьютерная оптика. 2012. Т. 36. № 1. С. 117-124.

The problem of the choice of algorithms parameters in automatic image recognition is put and solved by ensemble classifiers construction using the maximum posterior probability principle. The new criterion of parameters choice is strictly synthesized for Kullback-Leibler information discrimination and modern SIFT (Scale-Invariant Feature Transform) method of object recognition. The program and results of experimental research in a problem of face recognition with widely used databases (Yale, AT&T) are presented. It is shown that the proposed criterion allows to achieve recognition accuracy equal to the algorithm with the best parameters set, and not only for Kullback-Leibler information discrimination, but also for other popular distances (Euclidean metric, Kullback information divergence).

Added: Jun 9, 2012
Article
Савченко А. В. Компьютерная оптика. 2017. Т. 41. № 3. С. 422-430.

In this paper we focus on the image recognition problem in the case of small sample size based on the nearest neighbor rule and matching of high-dimensional feature vectors extracted with the deep convolutional neural network. We propose the novel recognition algorithm based on the maximum likelihood method for the joint density of dissimilarities between an observed image and available instances in the training set. This likelihood is estimated using the known asymptotically normally distribution of the Jensen-Shannon divergence between image features, if the latter can be treated as the probability density estimates. This asymptotic behavior is in agreement with the well-known experimental estimates of distributions of dissimilarity distances between high-dimensional vectors. The experimental study in unconstrained face recognition for the LFW (Labeled Faces in the Wild) and YTF (YouTube Faces) datasets demonstrated, that the proposed approach makes it possible to increase the recognition accuracy at 1-5% when compared with conventional classifiers.

Added: Jul 8, 2017
Article
Фурсов В. А., Козин Н. Е. Компьютерная оптика. 2008. Т. 32. № 4. С. 400-402.
Added: Feb 7, 2010
Article
Шахуро В. И., Конушин А. С. Компьютерная оптика. 2016. Т. 40. № 2. С. 294-300.

A new public dataset of traffic sign images is presented. The dataset is intended for training and testing the algorithms of traffic sign recognition. We describe the dataset structure and guidelines for working with the dataset, comparing it with the previously published traffic sign datasets. The evaluation of modern detection and classification algorithms conducted using the proposed dataset has shown that existing methods of recognition of a wide class of traffic signs do not achieve the accuracy and completeness required for a number of applications.

Added: Jul 8, 2016
Article
Фурсов В. А., Козин Н. Е. Компьютерная оптика. 2008. Т. 32. № 3. С. 307-310.
Added: Feb 7, 2010
Article
Савченко А. В. Компьютерная оптика. 2018. Т. 42. № 1. С. 149-158.

In this paper we study the image recognition tasks, in which images are described by high dimensional feature vectors extracted with deep convolutional neural networks and principal component analysis. In particular, we focus on the problem of high computational complexity of statistical approach with non-parametric estimates of probability density implemented by the probabilistic neural network. We propose the novel statistical classification method based on the density estimators with the orthogonal expansions using trigonometric series. It is shown that this approach makes it possible to overcome the drawbacks of the probabilistic neural network caused by the memory-based approach of instance-based learning. Our experimental study with Caltech-101 and CASIA WebFaces demonstrates that the proposed approach reduces error rate at 1-5%, and increases computational speed in 1.5-6 times when compared to the original probabilistic neural network for small samples of reference images.

Added: Apr 11, 2018