scholarly journals Sub-Frame Evaluation of Frame Synchronization for Camera Network Using Linearly Oscillating Light Spot

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6148
Author(s):  
Hyuno Kim ◽  
Masatoshi Ishikawa
Keyword(s):  
Visual Information ◽  
Evaluation Method ◽  
Measurement Instrument ◽  
Light Spot ◽  
Estimation Accuracy ◽  
Accurate Data ◽  
Frame Synchronization ◽  
Camera Network ◽  
Novel Method ◽  
Synchronization Accuracy

Precisely evaluating the frame synchronization of the camera network is often required for accurate data fusion from multiple visual information. This paper presents a novel method to estimate the synchronization accuracy by using inherent visual information of linearly oscillating light spot captured in the camera images instead of using luminescence information or depending on external measurement instrument. The suggested method is compared to the conventional evaluation method to prove the feasibility. Our experiment result implies that the estimation accuracy of the frame synchronization can be achieved in sub-millisecond order.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6387
Author(s):  
Li Xu ◽  
Jianzhong Hu
Keyword(s):  
Infrared Thermography ◽  
Visual Information ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Principal Component ◽  
Defect Depth ◽  
Model Learning ◽  
Flat Bottom ◽  
Bp Network ◽  
Active Infrared Thermography

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

scholarly journals The Strasbourg Visual Scale: A Novel Method to Assess Visual Hallucinations

2021 ◽  
Vol 12 ◽  
Author(s):  
Anne Giersch ◽  
Thomas Huard ◽  
Sohee Park ◽  
Cherise Rosen
Keyword(s):  
Visual Information ◽  
Subjective Experience ◽  
Physical Property ◽  
Visual Hallucinations ◽  
Visual Scale ◽  
The World ◽  
Novel Method ◽  
Internal Experience ◽  
The Relationship ◽  
Disorders Of The Self

The experience of oneself in the world is based on sensory afferences, enabling us to reach a first-perspective perception of our environment and to differentiate oneself from the world. Visual hallucinations may arise from a difficulty in differentiating one's own mental imagery from externally-induced perceptions. To specify the relationship between hallucinations and the disorders of the self, we need to understand the mechanisms of hallucinations. However, visual hallucinations are often under reported in individuals with psychosis, who sometimes appear to experience difficulties describing them. We developed the “Strasbourg Visual Scale (SVS),” a novel computerized tool that allows us to explore and capture the subjective experience of visual hallucinations by circumventing the difficulties associated with verbal descriptions. This scale reconstructs the hallucinated image of the participants by presenting distinct physical properties of visual information, step-by-step to help them communicate their internal experience. The strategy that underlies the SVS is to present a sequence of images to the participants whose choice at each step provides a feedback toward re-creating the internal image held by them. The SVS displays simple images on a computer screen that provide choices for the participants. Each step focuses on one physical property of an image, and the successive choices made by the participants help them to progressively build an image close to his/her hallucination, similar to the tools commonly used to generate facial composites. The SVS was constructed based on our knowledge of the visual pathways leading to an integrated perception of our environment. We discuss the rationale for the successive steps of the scale, and to which extent it could complement existing scales.

Evaluation of ontology structural metrics based on public repository data

2019 ◽  
Vol 21 (2) ◽  
pp. 473-485
Author(s):  
Manuel Franco ◽  
Juana María Vivo ◽  
Manuel Quesada-Martínez ◽  
Astrid Duque-Ramos ◽  
Jesualdo Tomás Fernández-Breis
Keyword(s):  
Evaluation Method ◽  
Measurement Instrument ◽  
Public Repository ◽  
Standard Methods ◽  
The Past ◽  
Biological Ontologies ◽  
Quality Aspects ◽  
Ontology Metrics ◽  
The Stability ◽  
Structural Metrics

Abstract The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications. Availability: http://sele.inf.um.es/ontology-metrics

scholarly journals Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 38 ◽  
Author(s):  
Incheol Kim ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Convolutional Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Input Image ◽  
Visual Interpretation ◽  
Feature Maps ◽  
Novel Method ◽  
Series Of Experiments

Deep learning (DL) methods are increasingly being applied for developing reliable computer-aided detection (CADe), diagnosis (CADx), and information retrieval algorithms. However, challenges in interpreting and explaining the learned behavior of the DL models hinders their adoption and use in real-world systems. In this study, we propose a novel method called “Class-selective Relevance Mapping” (CRM) for localizing and visualizing discriminative regions of interest (ROI) within a medical image. Such visualizations offer improved explanation of the convolutional neural network (CNN)-based DL model predictions. We demonstrate CRM effectiveness in classifying medical imaging modalities toward automatically labeling them for visual information retrieval applications. The CRM is based on linear sum of incremental mean squared errors (MSE) calculated at the output layer of the CNN model. It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image. A series of experiments on a “multi-modality” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Further, to visualize its effectiveness we generate “class-specific” ROI maps by averaging the CRM scores of images in each modality class, and characterize the visual explanation through their different size, shape, and location for our multi-modality CNN model that achieved over 98% performance on a dataset constructed from publicly available images.

Study on evaluation method of tube—fin thermal contact conductance

2010 ◽  
Vol 225 (2) ◽  
pp. 390-396
Author(s):  
D Tang ◽  
D Li ◽  
Y Peng ◽  
Z Du
Keyword(s):  
Contact Pressure ◽  
Evaluation Method ◽  
Thermal Contact ◽  
Element Model ◽  
Thermal Contact Conductance ◽  
Forming Process ◽  
Contact Conductance ◽  
Die Geometry ◽  
Novel Method ◽  
Tube Expansion

The thermal contact conductance (TCC) is one of the principal parameter in heat transfer mechanism of tube—fin heat exchangers. Because of the difficulties in experimental measurements, the tube—fin TCC has not been focused deeply. This article presents a novel method in evaluating the TCC of tube—fin heatexchanger. First, the tube—fin contact status is investigated with a finite-element model of tube expansion process. Distribution of contact pressure along the tube—fin interface is obtained from the simulation results. Then, experiments are carried out for the relationship between the contact pressure and the TCC. Combining the experiment result with the contact pressure distribution, the tube—fin TCC can be evaluated. Based on the method, effect of processing factors of the expansion forming process, such as expanding ratio and die geometry, are examined.

The Time Course of Visual Letter Perception

2012 ◽  
Vol 24 (7) ◽  
pp. 1645-1655 ◽  
Author(s):  
Sylvain Madec ◽  
Arnaud Rey ◽  
Stéphane Dufau ◽  
Michael Klein ◽  
Jonathan Grainger
Keyword(s):  
Visual Information ◽  
Time Course ◽  
Time Windows ◽  
Behavioral Measure ◽  
Letter Identification ◽  
Letter Perception ◽  
Visual Identification ◽  
Feature Processing ◽  
Novel Method ◽  
Identification Processes

We describe a novel method for tracking the time course of visual identification processes, here applied to the specific case of letter perception. We combine a new behavioral measure of letter identification times with single-letter ERP recordings. Letter identification processes are considered to take place in those time windows in which the behavioral measure and ERPs are correlated. A first significant correlation was found at occipital electrode sites around 100 msec poststimulus onset that most likely reflects the contribution of low-level feature processing to letter identification. It was followed by a significant correlation at fronto-central sites around 170 msec, which we take to reflect letter-specific identification processes, including retrieval of a phonological code corresponding to the letter name. Finally, significant correlations were obtained around 220 msec at occipital electrode sites that may well be due to the kind of recurrent processing that has been revealed recently by TMS studies. Overall, these results suggest that visual identification processes are likely to be composed of a first (and probably preconscious) burst of visual information processing followed by a second reentrant processing on visual areas that could be critical for the conscious identification of the visual target.

scholarly journals Illumination Invariant Facial Expression Recognition using Convolutional Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 6140-6144
Keyword(s):  
Facial Expression ◽  
Visual Information ◽  
Facial Expression Recognition ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Expression Recognition ◽  
Illumination Invariant ◽  
Novel Method ◽  
Machine Interface ◽  
Interface Modules

In this work, we propose a prospective novel method to address illumination invariant system for facial expression recognition. Facial expressions are used to convey nonverbal visual information among humans. This also plays a vital role in human-machine interface modules that have invoked attention of many researchers. Earlier machine learning algorithms require complex feature extraction algorithms and are relying on the size and uniqueness of features related to the subjects. In this paper, a deep convolutional neural network is proposed for facial expression recognition and it is trained on two publicly available datasets such as JAFFE and Yale databases under different illumination conditions. Furthermore, transfer learning is used with pre-trained networks such as AlexNet and ResNet-101 trained on ImageNet database. Experimental results show that the designed network could recognize up to 30% variation in the illumination and it achieves an accuracy of 92%.

Crater Edge-based Flexible Autonomous Navigation for Planetary Landing

2018 ◽  
Vol 72 (3) ◽  
pp. 649-668
Author(s):  
Yang Tian ◽  
Meng Yu ◽  
Meibao Yao ◽  
Xiangyu Huang
Keyword(s):  
Pose Estimation ◽  
Autonomous Navigation ◽  
State Of The Art ◽  
Estimation Accuracy ◽  
Illumination Direction ◽  
Crater Detection ◽  
Planetary Landing ◽  
Novel Method ◽  
Edge Based ◽  
Navigation Method

In this paper, a novel method for autonomous navigation for an extra-terrestrial body landing mission is proposed. Based on state-of-the-art crater detection and matching algorithms, a crater edge-based navigation method is formulated, in which solar illumination direction is adopted as a complementary optical cue to aid crater edge-based navigation when only one crater is available. To improve the pose estimation accuracy, a distributed Extended Kalman Filter (EKF) is developed to encapsulate the crater edge-based estimation approach. Finally, the effectiveness of proposed approach is validated by Monte Carlo simulations using a specifically designed planetary landing simulation toolbox.

scholarly journals Ratios of CD64 expressed on neutrophils, monocytes, and lymphocytes may be a novel method for diagnosis of neonatal sepsis

2015 ◽  
Vol 9 (02) ◽  
pp. 175-181 ◽  
Author(s):  
Dai-Hua Fang ◽  
Cong-Hai Fan ◽  
Juan Li ◽  
Qi An ◽  
Hong Yao ◽  
...  
Keyword(s):  
Neonatal Sepsis ◽  
Evaluation Method ◽  
High Sensitivity ◽  
Receiver Operating Curve ◽  
The Novel ◽  
Cd64 Expression ◽  
Neutrophil Cd64 ◽  
Significant Difference ◽  
Novel Method ◽  
And Control

Introduction: Neutrophil CD64 expression has been demonstrated as an improved diagnostic marker of infection and sepsis. The purpose of this study was to develop a new method to evaluate neutrophil CD64 expression for diagnosis of neonatal sepsis. Methodology: Eighty neonates with neonatal sepsis (21 culture positive, 59 negative) were enrolled in this prospective study along with 19 neonates with no symptoms or signs of infection as controls. Expressions of CD64 on monocytes, lymphocytes, and neutrophils were evaluated with flow cytometry (FCM). Ratios were calculated with these levels of CD64 expression. Blood culture and other laboratory exams were done at the same time for the diagnosis of neonatal sepsis. Results were compared between the neonatal sepsis and control groups. Results: CD64 ratios showed significant difference between the groups (p < 0.01). Receiver operating curve (ROC) analysis showed that the CD64 ratios possessed high sensitivity (90%) and specificity (89.5%) in neonatal sepsis identification. Conclusions: The novel CD64 evaluation method, CD64 ratio, can be used as a supplementary method for diagnosis of neonatal sepsis.

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