scholarly journals Mercator-Independent rover localization using stereophotoclinometry and panoramic images

2016 ◽  
Vol 3 (12) ◽  
pp. 488-509 ◽  
Author(s):  
Eric E. Palmer ◽  
James N. Head ◽  
Robert W. Gaskell ◽  
Mark V. Sykes ◽  
Brian McComas
Keyword(s):  
Panoramic Images ◽  
Rover Localization

scholarly journals Novel approach for quantification of multiple immunofluorescent signals using histograms and 2D plot profiling of whole-section panoramic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Roko Duplancic ◽  
Darko Kero
Keyword(s):  
Histological Section ◽  
Cell Counting ◽  
Tissue Samples ◽  
Spatial Gradients ◽  
Multiple Markers ◽  
Novel Approach ◽  
Academic Settings ◽  
Panoramic Images ◽  
Multiple Regions ◽  
Staining Protocol

AbstractWe describe a novel approach for quantification and colocalization of immunofluorescence (IF) signals of multiple markers on high-resolution panoramic images of serial histological sections utilizing standard staining techniques and readily available software for image processing and analysis. Human gingiva samples stained with primary antibodies against the common leukocyte antigen CD45 and factors related to heparan sulfate glycosaminoglycans (HS GAG) were used. Expression domains and spatial gradients of IF signals were quantified by histograms and 2D plot profiles, respectively. The importance of histomorphometric profiling of tissue samples and IF signal thresholding is elaborated. This approach to quantification of IF staining utilizes pixel (px) counts and comparison of px grey value (GV) or luminance. No cell counting is applied either to determine the cellular content of a given histological section nor the number of cells positive to the primary antibody of interest. There is no selection of multiple Regions-Of-Interest (ROIs) since the entire histological section is quantified. Although the standard IF staining protocol is applied, the data output enables colocalization of multiple markers (up to 30) from a given histological sample. This can serve as an alternative for colocalization of IF staining of multiple primary antibodies based on repeating cycles of staining of the same histological section since those techniques require non standard staining protocols and sophisticated equipment that can be out of reach for small laboratories in academic settings. Combined with the data from ontological bases, this approach to quantification of IF enables creation of in silico virtual disease models.

scholarly journals Joint Multimodal Embedding and Backtracking Search in Vision-and-Language Navigation

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1012
Author(s):  
Jisu Hwang ◽  
Incheol Kim
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Input Data ◽  
Language Instruction ◽  
Scoring Method ◽  
Processing Technologies ◽  
Backtracking Search ◽  
Panoramic Images ◽  
Benchmark Datasets ◽  
Vision And Language

Due to the development of computer vision and natural language processing technologies in recent years, there has been a growing interest in multimodal intelligent tasks that require the ability to concurrently understand various forms of input data such as images and text. Vision-and-language navigation (VLN) require the alignment and grounding of multimodal input data to enable real-time perception of the task status on panoramic images and natural language instruction. This study proposes a novel deep neural network model (JMEBS), with joint multimodal embedding and backtracking search for VLN tasks. The proposed JMEBS model uses a transformer-based joint multimodal embedding module. JMEBS uses both multimodal context and temporal context. It also employs backtracking-enabled greedy local search (BGLS), a novel algorithm with a backtracking feature designed to improve the task success rate and optimize the navigation path, based on the local and global scores related to candidate actions. A novel global scoring method is also used for performance improvement by comparing the partial trajectories searched thus far with a plurality of natural language instructions. The performance of the proposed model on various operations was then experimentally demonstrated and compared with other models using the Matterport3D Simulator and room-to-room (R2R) benchmark datasets.

scholarly journals Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry

2021 ◽  
Vol 11 (12) ◽  
pp. 5490
Author(s):  
Anna Maria Gargiulo ◽  
Ivan di Stefano ◽  
Antonio Genova
Keyword(s):  
Inertial Measurement Unit ◽  
Correction Method ◽  
Harsh Environments ◽  
Measurement Unit ◽  
Planetary Rovers ◽  
Model Based ◽  
Accurate Knowledge ◽  
And Control ◽  
Wheeled Vehicles ◽  
Rover Localization

The exploration of planetary surfaces with unmanned wheeled vehicles will require sophisticated software for guidance, navigation and control. Future missions will be designed to study harsh environments that are characterized by rough terrains and extreme conditions. An accurate knowledge of the trajectory of planetary rovers is fundamental to accomplish the scientific goals of these missions. This paper presents a method to improve rover localization through the processing of wheel odometry (WO) and inertial measurement unit (IMU) data only. By accurately defining the dynamic model of both a rover’s wheels and the terrain, we provide a model-based estimate of the wheel slippage to correct the WO measurements. Numerical simulations are carried out to better understand the evolution of the rover’s trajectory across different terrain types and to determine the benefits of the proposed WO correction method.

scholarly journals Predictors of root resorption associated with maxillary canine impaction in panoramic images

2015 ◽  
Vol 38 (3) ◽  
pp. 292-299 ◽  
Author(s):  
Ali Alqerban ◽  
Reinhilde Jacobs ◽  
Steffen Fieuws ◽  
Guy Willems
Keyword(s):  
Root Resorption ◽  
Maxillary Canine ◽  
Panoramic Images

Acquisition of the 3D boundary of the Triglav glacier from archived non-metric panoramic images

2011 ◽  
Vol 26 (133) ◽  
pp. 111-129 ◽  
Author(s):  
Mihaela Triglav-čekada ◽  
Dalibor Radovan ◽  
Matej Gabrovec ◽  
Mojca Kosmatin-Fras
Keyword(s):  
Panoramic Images

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

2021 ◽  
pp. 20200172
Author(s):  
Münevver Coruh Kılıc ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
Kaan Orhan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Forensic Identification ◽  
Deciduous Teeth ◽  
Time Saving ◽  
Panoramic Radiographs ◽  
Intelligence System ◽  
Promising Tool ◽  
Panoramic Images

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

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