Block Motion Based Dynamic Texture Analysis: A Review

Rahman, Akhlaqur; Tasnim, Sumaira

doi:10.14445/22312803/ijctt-v8p114

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Rahman and Tasnim [10] provide a review of the dynamic texture analysis methods using block motion. It is used for dynamic texture analysis using the block motion includes recognition, motion computation, segmentation, and synthesis.…”

Section: Related Workmentioning

confidence: 99%

Physics‐based dynamic texture analysis and synthesis model using GPU

Ghadekar

2020

IET image process

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The texture is a repetition of a particular structure. Textures classified into static texture and dynamic texture. There are two approaches for synthesising dynamic textures: image‐based approach and physics‐based approach. In the proposed work, the synthesis of different dynamic textures is shown. The physics‐based approach is used to synthesise dynamic texture videos. Different mathematical models are proposed which are suitable to give appropriate motion to the dynamic textures. The raw frames are created, and these frames are further used to synthesise the dynamic textures using physics laws and mathematical formulae. The flexibility of each model is demonstrated. The proposed model has less specificity and less computational complexity. The proposed algorithms are implemented on the graphics processing unit to reduce the overall execution time and time complexity. High‐quality videos are produced, and the evaluation of every frame assures the quality.

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Section: Related Workmentioning

confidence: 99%

Physics‐based dynamic texture analysis and synthesis model using GPU

Ghadekar

2020

IET image process

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A Comprehensive Taxonomy of Dynamic Texture Representation

Nguyen-Thanh

Phuong

2021

ACM Comput. Surv.

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Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities.

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Block Motion Based Dynamic Texture Analysis: A Review

Cited by 2 publications

References 19 publications

Physics‐based dynamic texture analysis and synthesis model using GPU

Physics‐based dynamic texture analysis and synthesis model using GPU

A Comprehensive Taxonomy of Dynamic Texture Representation

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