Object Scale Selection of Hierarchical Image Segmentation Using Reliable Regions

2019 ◽  
Author(s):  
Zaid Al-Huda ◽  
Bo Peng ◽  
Yan Yang ◽  
Muqeet Ahmed
Keyword(s):  
Image Segmentation ◽  
Scale Selection ◽  
Selection Of

scholarly journals Object scale selection of hierarchical image segmentation with deep seeds

2020 ◽  
Author(s):  
Zaid Al‐Huda ◽  
Bo Peng ◽  
Yan Yang ◽  
Riyadh Nazar Ali Algburi
Keyword(s):  
Image Segmentation ◽  
Scale Selection ◽  
Selection Of

scholarly journals Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7935
Author(s):  
Shuang Hao ◽  
Yuhuan Cui ◽  
Jie Wang
Keyword(s):  
Image Segmentation ◽  
Image Classification ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Selection Method ◽  
Scale Selection ◽  
Average Area ◽  
Optimal Segmentation ◽  
Selection Of ◽  
Segmentation Parameters

High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

scholarly journals Planetary Scale Selection of the Madden–Julian Oscillation*

2009 ◽  
Vol 66 (8) ◽  
pp. 2429-2443 ◽  
Author(s):  
Tim Li ◽  
Chunhua Zhou
Keyword(s):  
Boundary Layer ◽  
Rossby Wave ◽  
Initial Perturbation ◽  
Madden Julian Oscillation ◽  
Kelvin Wave ◽  
Scale Selection ◽  
Planetary Scale ◽  
Zonal Wavenumber ◽  
Nonlinear Heating ◽  
Selection Of

Abstract Numerical experiments with a 2.5-layer and a 2-level model are conducted to examine the mechanism for the planetary scale selection of the Madden–Julian oscillation (MJO). The strategy here is to examine the evolution of an initial perturbation that has a form of the equatorial Kelvin wave at zonal wavenumbers of 1 to 15. In the presence of a frictional boundary layer, the most unstable mode prefers a short wavelength under a linear heating; but with a nonlinear heating, the zonal wavenumber 1 grows fastest. This differs significantly from a model without the boundary layer, in which neither linear nor nonlinear heating leads to the long wave selection. Thus, the numerical simulations point out the crucial importance of the combined effect of the nonlinear heating and the frictional boundary layer in the MJO planetary scale selection. The cause of this scale selection under the nonlinear heating is attributed to the distinctive phase speeds between the dry Kelvin wave and the wet Kelvin–Rossby wave couplet. The faster dry Kelvin wave triggered by a convective branch may catch up and suppress another convective branch, which travels ahead of it at the phase speed of the wet Kelvin–Rossby wave couplet if the distance between the two neighboring convective branches is smaller than a critical distance (about 16 000 km). The interference between the dry Kelvin wave and the wet Kelvin–Rossby wave couplet eventually dissipates and “filters out” shorter wavelength perturbations, leading to a longwave selection. The boundary layer plays an important role in destabilizing the MJO through frictional moisture convergences and in retaining the in-phase zonal wind–pressure structure.

scholarly journals Ruler Detection for Autoscaling Forensic Images

2014 ◽  
Vol 6 (1) ◽  
pp. 9-27 ◽  
Author(s):  
Abhir Bhalerao ◽  
Gregory Reynolds
Keyword(s):  
Spectral Analysis ◽  
Image Segmentation ◽  
Crime Scene ◽  
Poor Quality ◽  
Fingerprint Matching ◽  
Standard Measurement ◽  
Automated Method ◽  
Manual Selection ◽  
Measurement Units ◽  
Selection Of

The assessment of forensic photographs often requires the calibration of the resolution of the image so that accurate measurements can be taken of crime-scene exhibits or latent marks. In the case of latent marks, such as fingerprints, image calibration to a given dots-per-inch is a necessary step for image segmentation, preprocessing, extraction of feature minutiae and subsequent fingerprint matching. To enable scaling, such photographs are taken with forensic rulers in the frame so that image pixel distances can be converted to standard measurement units (metric or imperial). In forensic bureaus, this is commonly achieved by manual selection of two or more points on the ruler within the image, and entering the units of the measure distance. The process can be laborious and inaccurate, especially when the ruler graduations are indistinct because of poor contrast, noise or insufficient resolution. Here the authors present a fully automated method for detecting and estimating the direction and graduation spacing of rulers in forensic photographs. The method detects the location of the ruler in the image and then uses spectral analysis to estimate the direction and wavelength of the ruler graduations. The authors detail the steps of the algorithm and demonstrate the accuracy of the estimation on both a calibrated set of test images and a wide collection of good and poor quality crime-scene images. The method is shown to be fast and accurate and has wider application in other imaging disciplines, such as radiography, archaeology and surveying.

Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

2020 ◽  
Vol 20 (03) ◽  
pp. 2050018
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Seed Selection ◽  
Seeded Region Growing ◽  
Processing Strategies ◽  
Edge Based ◽  
Seed Points ◽  
Selection Of

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

Least-Squares Halftoning Algorithm Based on Image Segmentation

2012 ◽  
Vol 263-266 ◽  
pp. 2082-2087
Author(s):  
Zi Fen He ◽  
Zhao Lin Zhan ◽  
Yin Hui Zhang
Keyword(s):  
Image Segmentation ◽  
Least Squares ◽  
Performance Measure ◽  
Distance Metrics ◽  
Digital Halftoning ◽  
Segmentation Method ◽  
Halftone Image ◽  
Printer Model ◽  
Halftone Images ◽  
Selection Of

This work presents a method based on the image content for digital halftoning using K-means clustering theory. Our algorithm applies to both a printer model and a model for the human visual system (HVS). The method strives to minimize the perceived error between the continuous original image and the halftone image. First, the gray image is partitioned into two, three and four regions using K-means image segmentation method, whose performance depends on the selection of distance metrics. Next, the statistics of average gray value of each clustering is calculated. Each clustering uses the least-squares model-based(Lsmb) algorithm to obtain halftone image. Finally, analysis and simulation results show that the proposed algorithm produces better gray-scale halftone image quality when we increase the number of clustering with a certain range. A performance measure for halftone images is used to evaluate our algorithm. The value of MSEv, WSNR and PSNR for two partitions is almost the same as that of the Lsmb algorithm, but for three and four partitions that the proposed algorithm achieves consistently better values of MSEv, WSNR and PSNR than the Lsmb algorithm.

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