Estimation of disparity maps through an evolutionary algorithm and global image features as descriptors

Reynosa-Guerrero, Jaqueline; García-Huerta, Juan-M.; Vázquez-Cervantes, Alberto; Reyes-Santos, E.; Perez-Ramos, J.-L.; Hernandez, H.

doi:10.1016/j.eswa.2020.113900

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…V m is the texture feature quantity of works of art within the m search range. V n is the amount of texture features of works of art within the n search range [19]. Combining corner detection and 3D edge contour feature detection methods, the texture filling and automatic rendering of art color extraction are realized, and the matching points of mesh model of art are obtained.…”

Section: Feature Information Fusion Of Work Ofmentioning

confidence: 99%

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

Yao

Sohail

2022

Mathematical Problems in Engineering

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In order to improve the style extraction and extraction ability of works of art, a color extraction method based on color features is proposed. The color feature extraction method is used to extract the style of visual works of art. The color feature region of works of art is segmented combined with a sparse scattered point reorganization method. Texture tracking and matching method is used for information fusion of works of art, combined with corner detection, three-dimensional edge contour feature detection method to realize texture filling and automatic rendering of art color extraction to improve art graphics’ color visual feature expression ability. The color feature data method is used for visual feature sampling and equalizing works of art. According to the equilibrium configuration results, the fuzzy clustering method is used to extract the color style of works of art to improve the style extraction and extraction identification ability of works of art. The simulation results show that this method has high accuracy in color extraction of works of art. It has a good effect on the extraction of art creation style and improves the ability of three-dimensional extraction and automatic extraction of art.

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Section: Feature Information Fusion Of Work Ofmentioning

confidence: 99%

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

Yao

Sohail

2022

Mathematical Problems in Engineering

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“…However, locating corresponding pixels is difficult in reality. Some variables may cause problems in the non-occlusion pixels, such as non-textured-homogeneous, repeated-texture, and camera noise [ 42 , 43 , 44 ]. The calculation of the disparity is done by block matching (BM) for all pixels and the validity of the disparity significance is defined as follows.…”

Section: Online Shop In Society 50mentioning

confidence: 99%

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Muslikhin

Horng

Yang

et al. 2021

Sensors

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In this study, an Artificial Intelligence of Things (AIoT)-based automated picking system was proposed for the development of an online shop and the services for automated shipping systems. Speed and convenience are two key points in Industry 4.0 and Society 5.0. In the context of online shopping, speed and convenience can be provided by integrating e-commerce platforms with AIoT systems and robots that are following consumers’ needs. Therefore, this proposed system diverts consumers who are moved by AIoT, while robotic manipulators replace human tasks to pick. To prove this idea, we implemented a modified YOLO (You Only Look Once) algorithm as a detection and localization tool for items purchased by consumers. At the same time, the modified YOLOv2 with data-driven mode was used for the process of taking goods from unstructured shop shelves. Our system performance is proven by experiments to meet the expectations in evaluating efficiency, speed, and convenience of the system in Society 5.0’s context.

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“…By employing advanced data analysis technology, key feature information in the cultural relics image database can be effectively extracted, involving artistic style, creation time, author, etc. [ 21 ]. The results of these analyses are presented in an intuitive visualization that provides the administrator with a holistic view of the cultural relics collection.…”

Section: Digital Retrieval System Integrating Improved Ga and Intelli...mentioning

confidence: 99%

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Lin,

Hu,

Cheng

et al. 2024

PLoS ONE

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This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection’s images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system’s superiority. The results show that in all models, the sample output results’ actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model’s predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

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Estimation of disparity maps through an evolutionary algorithm and global image features as descriptors

Cited by 4 publications

References 8 publications

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

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