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2. Learning Gaze Transitions from Depth to Improve Video Saliency Estimation

Author

Leifman, G., Rudoy, D., Swedish, T., Bayro-Corrochano, E., and Raskar, R.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative convolutional neural network which predicts a saliency map for a frame, given the fixation map of the previous frame. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired and yet hard to display. This can be explained, on the one hand, by the dramatic improvement of 3D-capable acquisition equipment. On the other hand, despite the considerable progress in 3D display technologies, most of the 3D displays are still expensive and require wearing special glasses. To evaluate the performance of our approach, we present a new comprehensive database of eye-fixation ground-truth for RGBD videos. Our experiments indicate that integrating depth into video saliency calculation is beneficial. We demonstrate that our approach outperforms state-of-the-art methods for video saliency, achieving 15% relative improvement.

Published
2016

3. ADVANCES IN AGRICULTURAL IMAGE SEGMENTATION: FROM TRADITIONAL TECHNIQUES TO DEEP LEARNING APPROACHES.

Author

Gapon, N., Puzerenko, A., Zhdanova, M., Safaryan, O., and Rudoy, D.

Subjects
COMPUTER vision, MACHINE learning, AGRICULTURE, IMAGE processing, AGRICULTURAL development, DEEP learning, IMAGE segmentation
Abstract

In recent years, image segmentation has been widely applied to solve problems in various fields. With the application of deep learning in machine vision, the excellent performance has been transferred to agricultural image processing by combining them with traditional methods. Segmentation methods have revolutionized the development of agricultural automation and are commonly used to analyze uniform sowing, crop type, identify pests, diseases, etc. This paper provides a review of the recent advances in traditional and deep learning-based methods for agricultural image segmentation. We present traditional methods that can effectively utilize the original image information and the high performance of deep learning-based methods. The review introduces key agricultural image datasets. The main metrics for evaluating the quality of image segmentation are presented, and the evaluation results of various classical and deep learning-based segmentation methods are presented. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Impact of globalization and digitalization on the efficiency of environmental development of territories

Author

Meskhi B, Bondarenko V, Efremenko I, Romanishina T, Rudoy D, and Olshevskaya A

Subjects
Environmental sciences, GE1-350
Abstract

Territory marketing is a popular area in terms of developing directions for territorial development and practical steps for the implementation of this activity. The interest in this area has been updated due to the development of globalization and increased competition of territories with each other. However, in the scientific literature there are many similar concepts related to territorial marketing, territorial marketing and regional marketing, which requires understanding and understanding of their essential characteristics and relationships. Along with this, it is of objective interest to characterize the main approaches to territory marketing due to their high demand for Russian reality.

Published
2020
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7. Preface

Author

Rudoy D. and Murgul V.

Subjects
Environmental sciences, GE1-350
Published
2019
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