scholarly journals Digital Refocusing: All-in-Focus Image Rendering Based on Holoscopic 3D Camera

2016 ◽  
Vol 04 (06) ◽  
pp. 24-35
Author(s):  
Obaidullah Abdul Fatah ◽  
Peter Lanigan ◽  
Amar Aggoun ◽  
Mohammad Rafiq Swash
Keyword(s):  
Image Rendering ◽  
Digital Refocusing ◽  
3D Camera ◽  
Focus Image

Search mode for rapid detection of virus particles

1991 ◽  
Vol 49 ◽  
pp. 286-287
Author(s):  
O. E. Bradfute
Keyword(s):  
Electron Microscopy ◽  
Rapid Detection ◽  
Plant Virus ◽  
Low Dose ◽  
Search Mode ◽  
Virus Diseases ◽  
Virus Particles ◽  
Focus Image ◽  
Time Required ◽  
Rapid Switching

Electron microscopy is frequently used in preliminary diagnosis of plant virus diseases by surveying negatively stained preparations of crude extracts of leaf samples. A major limitation of this method is the time required to survey grids when the concentration of virus particles (VPs) is low. A rapid survey of grids for VPs is reported here; the method employs a low magnification, out-of-focus Search Mode similar to that used for low dose electron microscopy of radiation sensitive specimens. A higher magnification, in-focus Confirm Mode is used to photograph or confirm the detection of VPs. Setting up the Search Mode by obtaining an out-of-focus image of the specimen in diffraction (K. H. Downing and W. Chiu, private communications) and pre-aligning the image in Search Mode with the image in Confirm Mode facilitates rapid switching between Modes.

scholarly journals Advanced Statistical Analysis of 3D Kinect Data: Mimetic Muscle Rehabilitation Following Head and Neck Surgeries Causing Facial Paresis

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 103
Author(s):  
Jan Kohout ◽  
Ludmila Verešpejová ◽  
Pavel Kříž ◽  
Lenka Červená ◽  
Karel Štícha ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Functional Data ◽  
Muscle Function ◽  
Evaluation Process ◽  
Multiple Logistic Regression ◽  
Facial Paresis ◽  
Kinect Camera ◽  
3D Camera ◽  
Mimetic Muscles ◽  
Data Analytic

An advanced statistical analysis of patients’ faces after specific surgical procedures that temporarily negatively affect the patient’s mimetic muscles is presented. For effective planning of rehabilitation, which typically lasts several months, it is crucial to correctly evaluate the improvement of the mimetic muscle function. The current way of describing the development of rehabilitation depends on the subjective opinion and expertise of the clinician and is not very precise concerning when the most common classification (House–Brackmann scale) is used. Our system is based on a stereovision Kinect camera and an advanced mathematical approach that objectively quantifies the mimetic muscle function independently of the clinician’s opinion. To effectively deal with the complexity of the 3D camera input data and uncertainty of the evaluation process, we designed a three-stage data-analytic procedure combining the calculation of indicators determined by clinicians with advanced statistical methods including functional data analysis and ordinal (multiple) logistic regression. We worked with a dataset of 93 distinct patients and 122 sets of measurements. In comparison to the classification with the House–Brackmann scale the developed system is able to automatically monitor reinnervation of mimetic muscles giving us opportunity to discriminate even small improvements during the course of rehabilitation.

scholarly journals Semantic 3D Mapping from Deep Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 1953
Author(s):  
Francisco Martín ◽  
Fernando González ◽  
José Miguel Guerrero ◽  
Manuel Fernández ◽  
Jonatan Ginés
Keyword(s):  
Point Cloud ◽  
Autonomous Mobile Robots ◽  
Semantic Mapping ◽  
Indoor Environments ◽  
3D Mapping ◽  
Direct Mapping ◽  
Learning Techniques ◽  
3D Camera ◽  
Deep Image ◽  
Boundary Pixel

The perception and identification of visual stimuli from the environment is a fundamental capacity of autonomous mobile robots. Current deep learning techniques make it possible to identify and segment objects of interest in an image. This paper presents a novel algorithm to segment the object’s space from a deep segmentation of an image taken by a 3D camera. The proposed approach solves the boundary pixel problem that appears when a direct mapping from segmented pixels to their correspondence in the point cloud is used. We validate our approach by comparing baseline approaches using real images taken by a 3D camera, showing that our method outperforms their results in terms of accuracy and reliability. As an application of the proposed algorithm, we present a semantic mapping approach for a mobile robot’s indoor environments.

Multi-focus Image Fusion Based on Multi-scale Gradients and Image Matting

2021 ◽  
pp. 1-1
Author(s):  
Jun Chen ◽  
Xuejiao Li ◽  
Linbo Luo ◽  
Jiayi Ma
Keyword(s):  
Image Fusion ◽  
Image Matting ◽  
Multi Scale ◽  
Focus Image
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