2016
If an image has been preprocessed appropriately to remove noise and artifacts, segmentation is often the key step in interpreting the image. Image segmentation is a process in which regions or features sharing similar characteristics are identified and grouped together. Image segmentation may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. The output of the segmentation step is usually a set of classified elements, Segmentation techniques are either region-based or edge-based. • Region-based techniques rely on common patterns in intensity values within a cluster of neighboring pixels. The cluster is referred to as the region, and the goal of the segmentation algorithm is to group regions according to their anatomical or functional roles.
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christinelwj · Zenodo (CERN European Organization for Nuclear Research) · 2025
codes for 'Endoplasmic reticulum junctions serve as a platform for endosome-lysosome interactions through their stop-and-go motion switching'
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Hadi Shokati · Zenodo (CERN European Organization for Nuclear Research) · 2026
Segmenting flood-affected areas from aerial images acquired after severe floods using transfer learning techniques
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Hasitha Adikari · Zenodo (CERN European Organization for Nuclear Research) · 2025
No description provided.
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Hadi Shokati · Zenodo (CERN European Organization for Nuclear Research) · 2026
Segmenting flood-affected areas from aerial images acquired after severe floods using transfer learning techniques
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Grace Panganiban · Harvard University Press eBooks · 2006
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GRACE PANGANIBAN · Harvard University Press eBooks · 2006
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Layer-cake Representation · 2011
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Dimitri Vayanos, Bob Wilson, Canice Prendergast et al. · 2001
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Mário Centeno, Álvaro A. Novo · RePEc: Research Papers in Economics
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Andreas Doncic (261227), Umut Eser (387160), Oguzhan Atay (387161) et al. · Figshare · 2015
<p>(<b>A</b>) A phase image region around the previously selected seed is opened. (<b>B</b>) The phase image is transformed into a series of binary images by applying thresholds such that any pixel that is lower than the threshold is assigned as ‘putative cell’ (red regions) or ‘non-cell’ (blue regions). In total 256 ( = 2<sup>8</sup>) thresholds are applied corresponsing to all possible values the phase image can take (example thresholds 2, 50 and 130 are shown here). (<b>C</b>) The watershed algorithm is applied to all thresholded images from (B). The result is a set of distinct regions. Each region is compared with the seed (inset) and the phase image and classified as ‘cell’ or ‘non-cell’. (<b>D</b>). All ‘cell’ areas, from all thresholds, are summed together (<b>E</b>). The composite image allows for more accurate segmentation than an optimized single threshold.</p>
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Number Plate, Recognition Using Improved · 2013
Automatic Number Plate Recognition (ANPR) is a real time embedded system. It is an active area of research. Now a day the requirement of ANPR system is increased due to different formats of number plates. In this paper Indian vehicle number plate recognition system is proposed. The proposed algorithm uses region props function in Mat lab for localization and segmentation of number plates. By using only captured images this system is used in car parking, in car theft issues and many more.
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Wolfgang Birkfellner · 2024
We have already learned that medical images map certain physical properties of tissue and store the resulting discrete mathematical function as an image. It is a straightforward and understandable desire that anyone who uses medical images wants to identify certain anatomical structures for further examination; identifying organs or other anatomical structures is an inevitable prerequisite for many operations that retrieve quantitative data from images as well as many visualization tasks.
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Martin H. Weik · 2000
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Gerhard Sagerer, Heinrich Niemann · 1997
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Josef A. Mazanec, Sara Dolničar · 2024
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2006
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Richard Boire · RePEc: Research Papers in Economics
Abstract To understand customers, the first step should be to segment customers. Although consultants agree on this first step, they do differ on the approach to segmentation. There are practical approaches and also scientific ones to segmentation. Rather than simply choosing to adopt one approach over the other, the correct approach will depend on the complexity of the given customer base and on the requirements of the current business challenge. For instance, a bank’s customer database containing 1 million customer records may require a much more sophisticated approach than a retail customer database containing 50000 records. Identifying students as a customer segment for a student program in a bank is going to be far less complex than trying to identify unique customer segments where the objective is to create distinct corporate strategies for these segments.
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D. ; https://orcid.org/0000-0002-0460-5344 Tautz · MPG.PuRe (Max Planck Society) · 2004
The three major taxa with metameric segmentation (annelids, arthropods, and chordates) appear to use three very different molecular strategies to generate segments. However, unexpected similarities are starting to emerge from characterization of pair-rule patterning and segmental border formation. Moreover, the existence of an ancestral segmentation clock based on Notch signaling has become likely. An old concept of comparative anatomy, the enterocoele theory, is compatible with a single origin of segmentation mechanisms and could therefore provide a conceptual framework for assessing these molecular similarities.
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Zack P. Pedersen, Antonio S. Williams · Edward Elgar Publishing eBooks · 2024
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