- Segmenting Multimodal Images

2015 ◽  
pp. 164-193
Keyword(s):  
Multimodal Images

scholarly journals Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions

2021 ◽  
Vol 13 (7) ◽  
pp. 1380
Author(s):  
Sébastien Dandrifosse ◽  
Alexis Carlier ◽  
Benjamin Dumont ◽  
Benoît Mercatoris
Keyword(s):  
Accurate Method ◽  
Field Conditions ◽  
Plant Phenotyping ◽  
Average Error ◽  
Multiple Sources ◽  
Registration Method ◽  
Pixel Intensity ◽  
Local Distortion ◽  
Close Range ◽  
Multimodal Images

Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras’ positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy.

NSCT-DCT based Fourier Analysis for Fusion of Multimodal Images

2021 ◽  
Author(s):  
Mayukhmala Jana ◽  
Subhosri Basu ◽  
Arpita Das
Keyword(s):  
Fourier Analysis ◽  
Multimodal Images

Review of Fuzzy Image Segmentation Techniques

2011 ◽  
pp. 282-313 ◽  
Author(s):  
Gour C. Karmakar ◽  
Laurence Dooley ◽  
Mahbubhur Rahman Syed
Keyword(s):  
Image Segmentation ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Clustering Methods ◽  
Comprehensive Overview ◽  
Domain Specific ◽  
Soft Computing Techniques ◽  
Fuzzy Clustering Methods ◽  
Fuzzy Image ◽  
Multimodal Images

This chapter provides a comprehensive overview of various methods of fuzzy logic-based image segmentation techniques. Fuzzy image segmentation techniques outperform conventional techniques, as they are able to evaluate imprecise data as well as being more robust in noisy environment. Fuzzy clustering methods need to set the number of clusters prior to segmentation and are sensitive to the initialization of cluster centers. Fuzzy rule-based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. It is also capable of drawing partial inference using fuzzy IF-THEN rules. It has been also intensively applied in medical imaging. These rules are, however, application-domain specific and very difficult to define either manually or automatically that can complete the segmentation alone. Fuzzy geometry and thresholding-based image segmentation techniques are suitable only for bimodal images and can be applied in multimodal images, but they don’t produce a good result for the images that contain a significant amount of overlapping pixels between background and foreground regions. A few techniques on image segmentation based on fuzzy integral and soft computing techniques have been published and appear to offer considerable promise.

Visualization of multimodal images for the planning of skull base surgery

1992 ◽  
Author(s):  
Derek L. Hill ◽  
S. E. Green ◽  
J. E. Crossman ◽  
David J. Hawkes ◽  
Glynn P. Robinson ◽  
...  
Keyword(s):  
Skull Base ◽  
Skull Base Surgery ◽  
Multimodal Images

Multimodal images in the brain

2010 ◽  
pp. 3-16 ◽  
Author(s):  
Stephen M. Kosslyn ◽  
Giorgio Ganis ◽  
William L. Thompson
Keyword(s):  
The Brain ◽  
Multimodal Images

scholarly journals Experimental Fire Measurement with UAV Multimodal Stereovision

2020 ◽  
Vol 12 (21) ◽  
pp. 3546
Author(s):  
Vito Ciullo ◽  
Lucile Rossi ◽  
Antoine Pieri
Keyword(s):  
Vision System ◽  
Processing Technologies ◽  
Geometric Characteristics ◽  
The Past ◽  
Spectral Bands ◽  
Camera Systems ◽  
Research Systems ◽  
Infrared Spectral ◽  
Aerial Vehicle ◽  
Multimodal Images

In wildfire research, systems that are able to estimate the geometric characteristics of fire, in order to understand and model the behavior of this spreading and dangerous phenomenon, are required. Over the past decade, there has been a growing interest in the use of computer vision and image processing technologies. The majority of these works have considered multiple mono-camera systems, merging the information obtained from each camera. Recent studies have introduced the use of stereovision in this field; for example, a framework with multiple ground stereo pairs of cameras has been developed to measure fires spreading for about 10 meters. This work proposes an unmanned aerial vehicle multimodal stereovision framework which allows for estimation of the geometric characteristics of fires propagating over long distances. The vision system is composed of two cameras operating simultaneously in the visible and infrared spectral bands. The main result of this work is the development of a portable drone system which is able to obtain georeferenced stereoscopic multimodal images associated with a method for the estimation of fire geometric characteristics. The performance of the proposed system is tested through various experiments, which reveal its efficiency and potential for use in monitoring wildfires.

scholarly journals Semantic Segmentation Of Hands In Multimodal Images: A Region New-Based CNN Approach

2019 ◽  
Author(s):  
Akila Pemasiri ◽  
David Ahmedt-Aristizabal ◽  
Kien Nguyen ◽  
Sridha Sridharan ◽  
Sasha Dionisio ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Multimodal Images
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