Название: Image Processing, Computer Vision, and Pattern Recognition and Information and Knowledge EngineeringАвтор: Leonidas Deligiannidis, Farid Ghareh Mohammadi, Farzan Shenavarmasouleh, Soheyla Amirian
Издательство: Springer
Серия: Communications in Computer and Information Science
Год: 2025
Страниц: 397
Язык: английский
Формат: pdf (true)
Размер: 48.3 MB
This book constitutes the proceedings of the 28th International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and the 23rd International Conference on Information and Knowledge Engineering, IKE 2024, held as part of the 2024 World Congress in Computer Science, Computer Engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024. The 19 IPCV 2024 papers included in these proceedings were carefully reviewed and selected from 98 submissions. IKE 2024 received 40 submissions and accepted 10 papers for inclusion in the proceedings.
The papers have been organized in topical sections as follows: Image processing, computer vision and pattern recognition; image processing, computer vision and pattern recognition - detection methods; and information and knowledge engineering.
Computer Science (CS) is the study of computational systems, data processing, information management, and automation. Many applications in CS focus on solving problems that would be impossible or extremely difficult to address without the use of computers. It serves as a bridge between computational science and other scientific fields. The interdisciplinary nature of CS involves leveraging computers to understand and solve complex challenges, making it the science of using computers to advance scientific discovery. Computer Engineering (CE), on the other hand, integrates aspects of Computer Science, electronic engineering, and electrical engineering. It encompasses the design and production of computer hardware, such as chips, servers, supercomputers, embedded systems, and communication systems, among others.
Image clustering is an essential analysis tool in machine learning and computer vision. Many applications such as content-based image annotation and image retrieval can be viewed as different instances of image clustering. Traditionally, various clustering methods have been explored, including K-means, agglomerative clustering, and so on. When the number of clusters is unknown initially, validity indices can be used to find a cluster number where they are supposed to be independent of clustering algorithms. Many cluster validity indices had been proposed in the literature, such as Bayesian information criterion (BIC), Akaike information criterion (AIC), Dunn's index, Davies-Bouldin index (DB), Silhouette Width (SW), Calinski and Harabasz index (CH), Gap statistic, generalized Dunn's index (DNg), modified Dunn's index (DNs), extended k-means called X-means, and Elbow method.
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