scholarly journals Measuring and Modeling the Feature Detection Threshold Functions of Colormaps

2019 ◽  
Vol 25 (9) ◽  
pp. 2777-2790 ◽  
Author(s):  
Colin Ware ◽  
Terece L. Turton ◽  
Roxana Bujack ◽  
Francesca Samsel ◽  
Piyush Shrivastava ◽  
...  
Keyword(s):  
Feature Detection ◽  
Detection Threshold ◽  
Threshold Functions

scholarly journals Characteristics of CALIOP attenuated backscatter noise: implication for cloud/aerosol detection

2010 ◽  
Vol 10 (7) ◽  
pp. 17263-17305 ◽  
Author(s):  
D. L. Wu ◽  
J. H. Chae ◽  
A. Lambert ◽  
F. F. Zhang
Keyword(s):  
Feature Detection ◽  
Detection Threshold ◽  
Horizontal Resolution ◽  
Occurrence Frequency ◽  
Orthogonal Polarization ◽  
Color Ratio ◽  
Gridded Dataset ◽  
L1 Data ◽  
Measurement Noises ◽  
532 Nm

Abstract. To study cloud/aerosol features in the upper troposphere and lower stratosphere (UT/LS) with the NASA's A-Train sensors, a research algorithm is developed for a re-gridded CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Level 1 (L1) backscatter dataset. This paper provides a detailed analysis of the measurement noise of this re-gridded dataset in order to compare the lidar measurements with other collocated measurements (e.g., CloudSat, Microwave Limb Sounder). The re-gridded dataset has a manageable data volume for multi-year analysis. It has a fixed (5 km) horizontal resolution, and the measurement error is derived empirically from the background-corrected backscatter profile on a profile-by-profile basis. The 532-nm and 1064-nm measurement noises, determined from the data at altitudes above 19 km, are analyzed and characterized in terms of the mean (μ), standard deviation (σ), and normalized probability density function (PDF). These noises show a larger variance over landmasses and bright surfaces during day, and in regions with enhanced flux of energetic particles during night, where the instrument's ability for feature detection is slightly degraded. An increasing trend in the nighttime 1064-nm σ appears to be significant, which likely causes the increasing differences in cloud occurrence frequency between the 532-nm and 1064-nm channels. Most of the CALIOP backscatter noise distributions exhibit a Gaussian-like behavior but the nighttime 532-nm perpendicular measurements show multi-Gaussian characteristics. We apply σ – based thresholds to detect cloud/aerosol features in the UT/LS from the subset L1 data. The observed morphology is similar to that from the Level 2 (L2) 05km_CLAY+05km_ALAY product, but the occurrence frequency obtained in this study is slightly lower than the L2 product due to differences in spatial averaging and detection threshold. In the case where the measurement noises of two data sets are different, the normalized PDF has proven useful for quantifying the day-night difference of the CALIOP backscatters, showing higher daytime cloud occurrence frequency in the tropical UT/LS. Other cloud/aerosol properties, such as depolarization ratio and color ratio, can be also evaluated with the PDF method.

scholarly journals Simultaneous tailoring of pulsed thermography experimental and processing parameters for enhanced defect detection and sizing in adhesive bonds in carbon fibre composites

2022 ◽  
Author(s):  
Rachael C Tighe ◽  
Jonathon Hill ◽  
Tom Vosper ◽  
Cody Taylor ◽  
Tairongo Tuhiwai
Keyword(s):  
Edge Detection ◽  
Defect Detection ◽  
Feature Detection ◽  
Signal Reconstruction ◽  
Detection Threshold ◽  
Bond Line ◽  
Minimum Diameter ◽  
Adhesive Bonds ◽  
Detection Depth ◽  
Carbon Fibre Composites

Abstract Thermographic inspection provides opportunity to tailor non-destructive evaluation to specific applications. The paper discusses the opportunities this presents through consideration of adhesive bonds between composites, such as those joining structural members and outer skins, where access is restricted to a single side. To date, literature focusses on the development of either an experimental procedure or data processing approach. This research aims to demonstrate the importance of tailoring both of these aspects to an application to obtain improved defect detection and robust quantification. Firstly, the heating stimulus is optimised to maximise the thermal contrast created between defect and non-defect regions using a development panel. Traditional flash heating is compared to longer square pulse heating, using a developed shutter system, compromising between experimental duration and heat input. A pulse duration of 4 seconds using two 130 W halogen bulbs was found double the detection depth from 1 mm to 2 mm, revealing all defects in the development panel. Temporal processing was maintained for all data using thermal signal reconstruction. Spatial defect detection routines were then implemented to provide robust defect/feature detection. Spatial defect detection encompassed a combination of image enhancement and edge detection algorithms. A two-stage kernel filter/binary enhancement method followed by the use of Canny edge detection was found most robust, providing a sizing error of 1.8 % on the development panel data. This process was then implemented on adhesive bonds with simulated bond line defects. The simulated defects are based on target detection threshold of 10 mm diameter void found at 1- 2 mm depth. All simulated void defects were detected in the representative bonded joint down to the minimum diameter tested of 5 mm. By considering the tailoring of multiple aspects of the inspection routine independently, an overall optimised approach for the application of interest has been defined.

Feature Detection Requires Spatial Attention

2007 ◽  
Author(s):  
Jan Theeuwes ◽  
Erik van der Burg ◽  
Artem V. Belopolsky
Keyword(s):  
Spatial Attention ◽  
Feature Detection

Detection threshold for electric shock in psychopaths.

1968 ◽  
Vol 73 (3, Pt.1) ◽  
pp. 268-272 ◽  
Author(s):  
Robert D. Hare
Keyword(s):  
Electric Shock ◽  
Detection Threshold

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

Do Sequential Lineups Impair Discriminability?

2020 ◽  
Author(s):  
Matthew Philip Kaesler ◽  
John C Dunn ◽  
Keith Ransom ◽  
Carolyn Semmler
Keyword(s):  
Human Behavior ◽  
Feature Detection ◽  
Eyewitness Memory ◽  
Simultaneous Presentation ◽  
Diagnostic Feature ◽  
Sequential Presentation ◽  
Sequential Lineups ◽  
Measurement Models ◽  
Sequential Lineup ◽  
Lineup Procedures

The debate regarding the best way to test and measure eyewitness memory has dominated the eyewitness literature for more than thirty years. We argue that to resolve this debate requires the development and application of appropriate measurement models. In this study we develop models of simultaneous and sequential lineup presentations and use these to compare the procedures in terms of discriminability and response bias. We tested a key prediction of the diagnostic feature detection hypothesis that discriminability should be greater for simultaneous than sequential lineups. We fit the models to the corpus of studies originally described by Palmer and Brewer (2012, Law and Human Behavior, 36(3), 247-255) and to data from a new experiment. The results of both investigations showed that discriminability did not differ between the two procedures, while responses were more conservative for sequential presentation compared to simultaneous presentation. We conclude that the two procedures do not differ in the efficiency with which they allow eyewitness memory to be expressed. We discuss the implications of this for the diagnostic feature detection hypothesis and other sequential lineup procedures used in current jurisdictions.

Absolute pitch ability is not associated with advantage in musical tension processing

2020 ◽  
Author(s):  
Linshu Zhou ◽  
Fang Liu ◽  
Tang Hai ◽  
Jun Jiang ◽  
Dongrui Man ◽  
...  
Keyword(s):  
Detection Threshold ◽  
Absolute Pitch ◽  
Discrimination Threshold ◽  
Pitch Change ◽  
Letter Naming ◽  
Direction Discrimination ◽  
Pitch Direction ◽  
Lower Accuracy ◽  
Tonal Hierarchy ◽  
The Stability

Absolute pitch (AP), a superior ability of pitch letter naming in the absence of a reference note, has long been viewed as an indicator of human musical talent and thus as evidence for the adaptationist hypothesis of music evolution. Little is known, however, whether AP possessors are superior to non-AP possessors in music processing. The present study investigated whether the AP ability facilitates musical tension processing in perceptual and experienced tasks. Twenty-one AP possessors and 21 matched non-AP possessors were tested using novel melodies in C and non-C contexts. Results indicated that the two groups provided comparable ratings of perceived and felt tension for melodies in both contexts. While AP possessors demonstrated lower accuracy with longer reaction time than non-AP possessors in naming movable solfège syllables for pitch in the pretest, their tension rating profiles showed a similar tonal hierarchy as non-AP possessors in regard to the stability of the ending tones of the melodies in both major and minor keys. Correlation analyses suggested that musical tension ratings were not significantly related to performance in pitch letter, movable solfège syllable naming, pitch change detection threshold, or pitch direction discrimination threshold for either group. These findings suggest that pitch naming abilities (either pitch letter or movable solfège syllable naming) do not benefit processing of perceived or felt musical tension, providing evidence to support the hypothesis that AP ability is not associated with advantage in music processing.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

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