scholarly journals Image Segmentation Techniques with Machine Learning

2021 ◽  
pp. 232-235
Author(s):  
Raksha S Kale ◽  
Dr. Surabhi Thorat
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Digital Image ◽  
Colour Intensity ◽  
Fundamental Step

Image segmentation is the fundamental step to analyze images and extract data from them. This paper concentrates on the idea behind the basic methods used. Image segmentation is the process of partitioning a digital image into multiple segments(i. e. sets of pixels). The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Segmentation is typically used to locate objects and boundaries(line, curve) in image. The Image segmentation is a set of segments that collectively cover the entire image. Each of pixels in a region are similar with respect to some characteristics colour, intensity or texture. The various segmentation techniques discussed in this paper.

scholarly journals AUTOMATIZING DEGRADATION MAPPING OF ANCIENT STELAE BY DUAL-BAND IMAGING AND MACHINE LEARNING-BASED CLASSIFICATION

2021 ◽  
Vol VIII-M-1-2021 ◽  
pp. 9-16
Author(s):  
E. Adamopoulos ◽  
F. Rinaudo ◽  
D. Adamopoulou
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Environmental Factors ◽  
Digital Image ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Biological Agents ◽  
Dual Band ◽  
Classification Methods ◽  
Near Infrared Imaging

Abstract. Degradation patterns are the visible consequence of the impacts of environmental factors and biological agents on stone heritage. Accurately documenting them is a key requisite when studying exposed stone antiquities to interpret weathering causes, identify conservation needs, and plan cleaning interventions. However, a significant gap can be identified in practical automatized procedures for mapping patterns on stone antiquities, such as ancient stelae. This work evaluates a workflow that uses visible and near-infrared imaging, combined with machine learning-based digital image segmentation tools, to classify degradation patterns on marble stelae correctly and cost-effectively. For this work, different classification methods are considered. Results are analyzed using error matrixes and reference degradation maps. The application cases include stelae displayed in the courtyard of the Archaeological Museum of Eretria (Euboea, Greece). The proposed methodology aims at being easily adapted to facilitate the conservators’ work.

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

scholarly journals Using machine learning to automate image segmentation for analysis of in vitro osteoclast formation and bone resorption

Bone Reports ◽  
2021 ◽  
Vol 14 ◽  
pp. 100865
Author(s):  
B.K. Davies ◽  
Andrew Hibbert ◽  
Mark Hopkinson ◽  
Gill Holdsworth ◽  
Isabel Orriss
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Bone Resorption ◽  
Osteoclast Formation

scholarly journals A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4893 ◽  
Author(s):  
Hejar Shahabi ◽  
Ben Jarihani ◽  
Sepideh Tavakkoli Piralilou ◽  
David Chittleborough ◽  
Mohammadtaghi Avand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Vegetation Index ◽  
Scale Parameter ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Slope Length ◽  
Optimal Scale ◽  
Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

Digital Image Soil Analysis based on Machine Learning

2021 ◽  
Author(s):  
Quchao Cheng ◽  
Jiaojie Li ◽  
Guochao Shen ◽  
Qingmin Du
Keyword(s):  
Machine Learning ◽  
Digital Image ◽  
Soil Analysis
Sign in / Sign up

Export Citation Format

Share Document