Gibbs-based unsupervised segmentation approach to partitioning hyperspectral imagery for terrain applications

2001 ◽  
Author(s):  
Robert S. Rand ◽  
Daniel M. Keenan
Keyword(s):  
Hyperspectral Imagery ◽  
Unsupervised Segmentation ◽  
Segmentation Approach

TEXTURE SEGMENTATION: AN UNSUPERVISED APPROACH

1995 ◽  
Vol 03 (04) ◽  
pp. 431-449
Author(s):  
MUKUL V. SHIRVAIKAR ◽  
MOHAN M. TRIVEDI
Keyword(s):  
Basic Problem ◽  
Image Understanding ◽  
Search Process ◽  
Seed Region ◽  
Image Search ◽  
Unsupervised Segmentation ◽  
Segmentation Strategy ◽  
Texture Model ◽  
Segmentation Approach ◽  
Unsupervised Approach

The segmentation of scenes into perceptually meaningful partitions has been a basic problem in image understanding, especially when unsupervised methodology has been desired. A novel unsupervised segmentation approach based on texture is developed. The texture model is based on sets of gray level cooccurence (GLC) matrices rather than measures extracted from them. The algorithmic constituents for the segmentation scheme: choice of seed regions, normalized match distances between texture models, region homogeneity, and aggregation criteria are systematically developed. The unsupervised algorithm works so that “seed” regions are discovered by an image search process. Initial estimates of the texture model prototypes are automatically computed for each “seed” region, and classification thresholds are based on the variance of the model over the “seed” region. An aggregation process then results in regions being successively classified and segmented “out” of the image. This recursive process of segmentation is continued until all pixels are classified. The segmentation strategy was tested successfully on natural texture mosaics. The results are analytically presented. These experiments demonstrate that the unsupervised process can correctly identify the perceptual constituents of the image based on texture.

scholarly journals A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

2016 ◽  
Vol 19 (3) ◽  
pp. 391-397 ◽  
Author(s):  
Prateek Katiyar ◽  
Mathew R. Divine ◽  
Ursula Kohlhofer ◽  
Leticia Quintanilla-Martinez ◽  
Bernhard Schölkopf ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Multiparametric Mri ◽  
Unsupervised Segmentation ◽  
Segmentation Approach ◽  
First Results ◽  
Histological Validation

scholarly journals An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 308 ◽  
Author(s):  
Salvatore Filippo Di Gennaro ◽  
Carla Nati ◽  
Riccardo Dainelli ◽  
Laura Pastonchi ◽  
Andrea Berton ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Central Italy ◽  
Cost Effective ◽  
Biomass Estimation ◽  
Geometric Features ◽  
Unsupervised Segmentation ◽  
Segmentation Approach ◽  
Pruning Residues ◽  
Strategic Support

The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees’ geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 < R2 < 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain.

scholarly journals CPIDM: A Clustering-Based Profound Iterating Deep Learning Model for HSI Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kriti Mahajan ◽  
Urvashi Garg ◽  
Mohammad Shabaz
Keyword(s):  
Deep Learning ◽  
Interplanetary Space ◽  
Direct Method ◽  
Three Dimensional ◽  
Learning Model ◽  
Unsupervised Segmentation ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
Segmentation Approach ◽  
Deep Learning Model

The existing work on unsupervised segmentation frequently does not present any statistical extent to estimating and equating procedures, gratifying a qualitative calculation. Furthermore, regardless of the datum that enormous research is dedicated to the advancement of a novel segmentation approach and upgrading the deep learning techniques, there is an absence of research comprehending the assessment of eminent conventional segmentation methodologies for HSI. In this paper, to moderately fill this gap, we propose a direct method that diminishes the issues to some extent with the deep learning methods in the arena of a HSI space and evaluate the proposed segmentation techniques based on the method of the clustering-based profound iterating deep learning model for HSI segmentation termed as CPIDM. The proposed model is an unsupervised HSI clustering technique centered on the density of pixels in the spectral interplanetary space and the distance concerning the pixels. Furthermore, CPIDM is a fully convolutional neural network. In general, fully convolutional nets remain spatially invariant preventing them from modeling position-reliant outlines. The proposed network maneuvers this by encompassing an innovative position inclined convolutional stratum. The anticipated unique edifice of deep unsupervised segmentation deciphers the delinquency of oversegmentation and nonlinearity of data due to noise and outliers. The spectrum efficacy is erudite and incidental from united feedback via deep hierarchy with pooling and convolutional strata; as a consequence, it formulates an affiliation among class dissemination and spectra along with three-dimensional features. Moreover, the anticipated deep learning model has revealed that it is conceivable to expressively accelerate the segmentation process without substantive quality loss due to the existence of noise and outliers. The proposed CPIDM approach outperforms many state-of-the-art segmentation approaches that include watershed transform and neuro-fuzzy approach as validated by the experimental consequences.

scholarly journals Image-to-Image Translation for Simplified MRI Muscle Segmentation

2021 ◽  
Vol 1 ◽  
Author(s):  
Michael Gadermayr ◽  
Lotte Heckmann ◽  
Kexin Li ◽  
Friederike Bähr ◽  
Madlaine Müller ◽  
...  
Keyword(s):  
High Performance ◽  
Unsupervised Segmentation ◽  
Practical Applications ◽  
Domain Specific ◽  
Supervised Segmentation ◽  
Image Translation ◽  
Segmentation Approach ◽  
Intermediate Image ◽  
Improved Performance ◽  
Muscle Segmentation

Deep neural networks recently showed high performance and gained popularity in the field of radiology. However, the fact that large amounts of labeled data are required for training these architectures inhibits practical applications. We take advantage of an unpaired image-to-image translation approach in combination with a novel domain specific loss formulation to create an “easier-to-segment” intermediate image representation without requiring any label data. The requirement here is that the task can be translated from a hard to a related but simplified task for which unlabeled data are available. In the experimental evaluation, we investigate fully automated approaches for segmentation of pathological muscle tissue in T1-weighted magnetic resonance (MR) images of human thighs. The results show clearly improved performance in case of supervised segmentation techniques. Even more impressively, we obtain similar results with a basic completely unsupervised segmentation approach.

Analysis of the Nascar Hall of Fame Exhibition Event: A Generation-Based Market Segmentation Approach

2019 ◽  
Vol 23 (6) ◽  
pp. 913-926
Author(s):  
Kakyom Kim ◽  
Giri Jogaratnam
Keyword(s):  
Generation X ◽  
Market Segmentation ◽  
Generation Y ◽  
Generational Differences ◽  
The Past ◽  
The Us ◽  
Segmentation Approach ◽  
Research Findings ◽  
Hall Of Fame ◽  
Southeastern Region

Research findings on generations have been becoming useful for event organizers and destination developers over the past decades. The current study investigated generational differences in exhibition dimensions, satisfaction, and future intentions along with trip characteristics of visitors to the NASCAR Hall of Fame Exhibition event held in a medium-sized city in the southeastern region of the US. Analysis confirmed the existence of six exhibition dimensions labeled as "exhibits," "staff," "facility," "concessions," "audio tours," and "hard cards" on the event. As part of the most substantial results, there were both dissimilarities and similarities in the exhibition dimensions across four generations including "Matures," "Baby Boomers," "Generation X," and "Generation Y." Analysis also suggested significant differences in exhibition visitors' overall satisfaction, future intentions, and trip characteristics across the generations. Some useful implications are discussed for exhibition event managers and organizers.

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