Image retrieval with SNN-based multi-level thresholding

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Image retrieval is defined as indexing similar or identical images in a digital image database. Various feature vectors obtained from the images are used while searching for a similar digital image. However, processing all pixels of the images requires costly algorithms. In addition, it is a possible issue that the images used in retrieval approaches are of different sizes. For this reason, pixel-level operations are insufficient when comparing images. Therefore, it requires vectorial structures that represent images. The process of obtaining these vectorial structures is called feature extrac ...Daha fazlası

Makale 2022 Gümüşhane Üniversitesi Fen Bilimleri Dergisi 1 Görüntülenme

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Multilevel image thresholding with multimodal optimization

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Thresholding method is one of the most popular approaches for image segmentation where an objective function is defined in terms of threshold numbers and their locations in a histogram. If only a single threshold is considered, a segmented image with two classes is achieved. On the other hand, multiple classes in the output image are created with multilevel thresholding. Otsu and Kapur's procedures have been conventional steps for defining objective functions. Nevertheless, the fundamental problem with thresholding techniques is the determination of threshold numbers, which must be selected by ...Daha fazlası

Makale 2021 Multimedia Tools and Applications80 ( 10 ) , pp. 1 - 15289 121 Görüntülenme Süresiz Ambargo

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A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding

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There are many techniques for conducting image analysis and pattern recognition. This papers explores a way to optimize one of these techniques-image segmentation-with the help of a novel hybrid optimization algorithm. Image segmentation is mostly used for a semantic segmentation of images, and thresholding is one the most common techniques for performing this segmentation. Otsu's and Kapur's thresholding methods are two well-known approaches, both of which maximize the between-class variance and the entropy measure, respectively, in a gray image histogram. Both techniques were developed for b ...Daha fazlası

Makale 2021 Multimedia Systems27 ( 1 ) , pp. 1 - 142 52 Görüntülenme Süresiz Ambargo

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Battle royale optimizer for multilevel image thresholding

Taymaz Akan

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu’s and Kapur’s methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques we ...Daha fazlası

Makale 2024 Journal of Supercomputing80 ( 4 ) , pp. 5 - 5340 36 Görüntülenme