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25 | 05 | 2019
10.14489/vkit.2019.04.pp.013-024

DOI: 10.14489/vkit.2019.04.pp.013-024

Визильтер Ю. В., Выголов О. В., Желтов С. Ю., Князь В. В.
СЕМАНТИКО-МОРФОЛОГИЧЕСКОЕ ОПИСАНИЕ И СИНТЕЗ ИЗОБРАЖЕНИЙ С ИСПОЛЬЗОВАНИЕМ ГЛУБОКИХ НЕЙРОННЫХ СЕТЕЙ
(c. 13-24)

Аннотация. Обоснован подход к анализу и синтезу изображений на основе семантико-морфологических моделей (СММ), сочетающих свойства морфологических и семантических описаний изображений видимых сцен (семантико-морфологический анализ изображений). Определены СММ сегментированных мозаичных изображений. Дано описание возможных способов представления СММ в глубоких конволюционных нейронных сетях. Предложенный семантико-морфологический подход к описанию и синтезу изображений апробирован на примере задачи синтеза инфракрасных изображений по исходным цветным изображениям с использованием СММ и генеративно-состязательных сетей. Показано качественное преимущество полученных результатов предложенных СММ по сравнению с традиционными мозаичными морфологическими моделями и другими известными методами.

Ключевые слова:  морфологический анализ изображений; семантические модели; семантическая сегментация; синтез изображений; глубокие нейронные сети; генеративно-состязательные сети.

 

Vizilter Yu. V., Vygolov O. V., Zheltov S. Yu., Kniaz V. V.
SEMANTIC-MORPHOLOGICAL IMAGE DESCRIPTION AND SYNTHESIS VIA CONVOLUTIONAL NEURAL NETWORKS
(pp. 13-24)

Abstract. An approach for synthesis and analysis of images using SMM (Semantic-Morphological Models) was defined. SMM models combine properties of morphological and semantic image descriptions of the observed scene (semantic-morphological image analysis). Semantic-morphological models of the mosaic images were defined. A description was given for various approaches to implementation of SMM in deep convolutional neural networks. The proposed semantic-morphological approach to definition and synthesis of images was test on a task of infrared image synthesis. Infrared images are synthesized using GANs  (Generative Adversarial Networks). An SMM and a color image are used as an input for a generative adversarial network. An overview is presented that discusses the related work on image synthesis using generative adversarial networks. A modification of a generative adversarial network is presented and termed as SemanticThermalGAN. The proposed modification of a generative adversarial network performs the synthesis of infrared images in two steps. Firstly, the SemanticThermalGAN predicts the semantic segmentation for all objects in the scene. Secondly, the network predicts the relative temperature textures of objects in the scene. A qualitative evaluation was performed. The evaluation proves that results generated using SMM-based approach outperforms similar modern approaches, including mosaic morphological models.

Keywords: Morphological image analysis; Semantic model; Semantic segmentation; Image synthesis; Convolutional neural networks; Generative adversarial networks.

Рус

Ю. В. Визильтер, О. В. Выголов, С. Ю. Желтов, В. В. Князь (ФГУП «Государственный научно-исследовательский институт авиационных систем» ГНЦ РФ, Москва, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

Eng

Yu. V. Vizilter, O. V. Vygolov, S. Yu. Zheltov, V. V. Kniaz (State Research Institute of Aviation Systems State Scientific Center of Russian Federation, Moscow, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Рус

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Eng

1. Qi G.-J. (2016). Hierarchically Gated Deep Networks for Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2267-2275.
2. Xu C., Corso J. J. (2016). Actor-Action Semantic Segmentation with Grouping Process Models. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3083-3092.
3. Dai J., He K., Sun J. (2016). Instance-Aware Semantic Segmentation via Multi-Task Network Cascades. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3150-3158.
4. Lin G. et al. (2016). Scribble Sup: Scribble Supervised Convolutional Networks for Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3159-3167.
5. Lin G. et al. (2016). Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3194-3203.
6. Vemulapalli R. et al. (2016). Gaussian Conditional Random Field Network for Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3224-3233.
7. Bertasius G., Shi J., Torresani L. (2016). Semantic Segmentation with Boundary Neural Fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3602-3610.
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13. Zhang R. (2016). Colorful Image Colorization. European Conference on Computer Vision, Vol. 9907, 40, pp. 649-666.
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16. Isola P. et al. (2017). Image-to-Image Translation with Conditional Adversarial Networks. Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967-5976.
17. Zhu J.-Y. et al. (2017). Toward Multimodal Image-to-Image Translation. NIPS. Annual Conference on Neural Information Processing Systems, pp. 465-476.
18. Image Blind Denoising with Generative Adversarial Network Based Noise Modeling. (2018). Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3155-3164.
19. Ledig C. et al. (2017). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105-114.
20. Lim B., Son S., Kim H. (2017). Enhanced Deep Residual Networks for Single Image Super-Resolution. Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), pp. 1132-1140.
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Рус

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