A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields
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TLDR
In this paper , an unsupervised and graph-based method of image segmentation is proposed, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal.About:
This article is published in Pattern Recognition.The article was published on 2022-09-01 and is currently open access. It has received 5 citations till now. The article focuses on the topics: Computer science & Image segmentation.read more
Citations
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A Novel Hybrid Retinal Blood Vessel Segmentation Algorithm for Enlarging the Measuring Range of Dual-Wavelength Retinal Oximetry
TL;DR: In this paper , a hybrid vessel segmentation algorithm was proposed to segment thick and thin retinal vessels and extend the measuring range of dual-wavelength retinal oximetry.
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MS-CANet: Multi-Scale Subtraction Network with Coordinate Attention for Retinal Vessel Segmentation
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale subtraction network (MS-CANet) with residual coordinate attention to segment the vessels in retinal vessel images, which captures long-range spatial dependencies while preserving precise position information.
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Fine-grained image processing based on convolutional neural networks
TL;DR: The fine-grained image segmentation, image super-resolution reconstruction, and image edge detection methods based on convolutional neural networks (CNNs) are summarized and analyzed in this paper .
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Segmentation of butterflies from complex agro-ecological images using quantum mechanics and spatial refinement
TL;DR: In this article , a segmentation method composed of a sequence of three steps was proposed to locate roughly butterfly areas, and an adaptation of the Schrödinger equation on a graph propagation process was deployed to check regional similarities to those areas.
References
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Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models.
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Active contours without edges
Tony F. Chan,Luminita A. Vese +1 more
TL;DR: A new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets is proposed, which can detect objects whose boundaries are not necessarily defined by the gradient.
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: DeepLab as discussed by the authors proposes atrous spatial pyramid pooling (ASPP) to segment objects at multiple scales by probing an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views.
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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.