An Adaptive Markov Random Field for Structured Compressive Sensing

Suwanwimolkul, Suwichaya; Zhang, Lei; Gong, Daozhi; Zhang, Zhen; Chen, Chao; Ranasinghe, Damith C.; Shi, Javen Qinfeng

doi:10.1109/tip.2018.2878294

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…To maximize adaptability, Suwanwimolkul et al proposed a new sparse signal estimation for robotic imaging systems. Among them, noise and signal parameter estimation were unified into a variational optimization problem [12]. e image processing variational model of the network-controlled robot image transmission and processing system has high practicability.…”

Section: Introductionmentioning

confidence: 99%

Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology

2022

Journal of Robotics

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The rapid development of the information age brings convenience to human life, but it also brings great challenges to information processing technology. Multimedia enhancement technology is an organic combination of multimedia technology and information processing technology, and it is also an important way of modern multimedia image information processing. However, its usefulness and effectiveness are increasingly negatively affected by the open information environment. The processing effect is also unable to meet the development requirements of the visual field. In order to improve this problem, this paper studied the image transmission and processing system of network-controlled robot on the basis of analyzing the characteristics of the problems existing in the current stage of multimedia technology. On this basis, a new image processing variational model was established and applied to multimedia enhancement technology, which improved the efficiency and effect of image information processing. Finally, the feasibility of its application function and performance was tested by experiments. The test results showed that in the difficult mode of the image processing task, the refresh time of the model in this paper in the multimedia enhancement technology was 1.13 s in total, which was not much different from the test results in the easy mode. Also, in the load stress test, the comprehensive test values under full-function operation and partial-function operation were 42.04% and 20.92%, respectively. Compared with the traditional model, the model in this paper has stronger carrying capacity in multimedia enhancement technology and has better processing ability and stability.

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Section: Introductionmentioning

confidence: 99%

Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology

2022

Journal of Robotics

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“…e characteristic of the Markov process is that the future state is only related to the present and has nothing to do with history [27]. at is to say, the state of the system at time t + 1 is only related to the state it was in at time t and is not affected by the state it was in before time t [28].…”

Section: Agricultural Insurance Risk Management Based On Markov Modelmentioning

confidence: 99%

Application of Markov Model-Based IoT in Agricultural Insurance and Risk Management

Chen

Jiang

2021

Mobile Information Systems

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As the foundation of the national economy, agriculture is a high-risk, weak industry. Affected by many factors, agricultural production is subject to catastrophe risks from time to time. Agricultural production is mainly faced with two major threats, natural disaster risk and market risk. As an effective risk management tool, the production and promotion of agricultural insurance have played an essential role in guaranteeing the development of the agricultural industry in some developed countries and major agricultural countries in the world. This article combines the Internet of Things and Markov model for agricultural insurance risk management. First, we combine the structure of the Internet of Things and select relevant statistical data. Then, we build a panel data system, starting from two perspectives in different regions and analyze agricultural insurance’s current development and characteristics at each stage. In addition, we use the Markov model to build a panel data model to explore the specific impact mechanisms deeply. We also study the effects of disaster risk levels in different regions on the development of agricultural insurance. After simulation verification, we believe that this model can effectively promote the balanced regional development of agricultural insurance.

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“…By including non-local positions, we hypothesize the MRF will better capture global patterns such as symmetry. Other MRF extensions have tested longer-range [33], [34] and adaptive neighbourhoods [35]; but to our knowledge, this is the first time MRFs have been extended with symmetric neighbors.…”

Section: B Algorithmsmentioning

confidence: 99%

Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning

Volz¹,

Justesen²,

Snodgrass³

et al. 2020

2020 IEEE Conference on Games (CoG)

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Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Mario Bros., DOOM, Zelda, and Kid Icarus), it is an open questions how well these approaches can capture large-scale visual patterns such as symmetry. In this paper, we propose match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns. We demonstrate that popular algorithm such as Generative Adversarial Networks struggle in this domain and propose adaptations to improve their performance. In particular we augment the neighborhood of a Markov Random Fields approach to not only take local but also symmetric positional information into account. We conduct several empirical tests including a user study that show the improvements achieved by the proposed modifications, and obtain promising results.

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An Adaptive Markov Random Field for Structured Compressive Sensing

Cited by 8 publications

References 43 publications

Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology

Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology

Application of Markov Model-Based IoT in Agricultural Insurance and Risk Management

Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning

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