Identification of image feature edge gradients during visual scanning to improve edge profile accuracy

2004 ◽  
Author(s):  
D. Hanafi ◽  
G. Rodnay ◽  
M. Tordon ◽  
J. Katupitiya
Keyword(s):  
Image Feature ◽  
Visual Scanning ◽  
Edge Profile ◽  
Profile Accuracy

scholarly journals About the Profile Accuracy of the Involute Gear Hob

2017 ◽  
Vol 9 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Márton Máté ◽  
Dénes Hollanda
Keyword(s):  
Mathematical Model ◽  
Cutting Tools ◽  
Helical Gear ◽  
Gear Cutting ◽  
Involute Gear ◽  
Edge Profile ◽  
Design Solutions ◽  
The Moment ◽  
Profile Accuracy

Abstract Gear hobs are the most widely and frequently used gear cutting tools. During the time passed between the moment of invention (Schiele, 1876) and the present, gear hobs reached a considerable evolution regarding the geometry, the profile of the edge, the relieving technologies finalizing in the latest constructive and design solutions. This paper deals with the calculus of the edge profile in the case the basic worm of the hob has involute helicoid surfaces. In order to obtain a constant grinding allowance on the relief faces of the gear hob teeth it is necessary to compute the edge of the roughing relieving cutter. The equations are deduced considering that the provenience involute worm is a one teethed helical gear with shifted profile. The presented mathematical model proves that linearizing the relieving cutter profile is not an adequate solution if aspiring to higher precision.

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

Visual scanning: Comparisons between student and instructor pilots.

1976 ◽  
Author(s):  
Joseph DeMaio ◽  
Stanley Parkinson ◽  
Barry Leshowitz ◽  
John Crosby
Keyword(s):  
Visual Scanning

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

Sub-20nm Device Voltage-Sensitive SRAM Characterization and Failure Analysis

2018 ◽  
Author(s):  
Seng Nguon Ting ◽  
Hsien-Ching Lo ◽  
Donald Nedeau ◽  
Aaron Sinnott ◽  
Felix Beaudoin
Keyword(s):  
Failure Analysis ◽  
High Performance ◽  
Yield Loss ◽  
Technology Development ◽  
High Density ◽  
High K ◽  
Yield Learning ◽  
Oxide Deposition ◽  
Edge Profile ◽  
High K Dielectric

Abstract With rapid scaling of semiconductor devices, new and more complicated challenges emerge as technology development progresses. In SRAM yield learning vehicles, it is becoming increasingly difficult to differentiate the voltage-sensitive SRAM yield loss from the expected hard bit-cells failures. It can only be accomplished by extensively leveraging yield, layout analysis and fault localization in sub-micron devices. In this paper, we describe the successful debugging of the yield gap observed between the High Density and the High Performance bit-cells. The SRAM yield loss is observed to be strongly modulated by different active sizing between two pull up (PU) bit-cells. Failure analysis focused at the weak point vicinity successfully identified abnormal poly edge profile with systematic High k Dielectric shorts. Tight active space on High Density cells led to limitation of complete trench gap-fill creating void filled with gate material. Thanks to this knowledge, the process was optimized with “Skip Active Atomic Level Oxide Deposition” step improving trench gap-fill margin.

Consonant Classification in Mandarin Based on the Depth Image Feature: A Pilot Study

2019 ◽  
Author(s):  
Han-Chi Hsieh ◽  
Wei-Zhong Zheng ◽  
Ko-Chiang Chen ◽  
Ying-Hui Lai
Keyword(s):  
Pilot Study ◽  
Image Feature ◽  
Depth Image

Carpal Bone Segmentation Using Fully Convolutional Neural Network

2019 ◽  
Vol 15 (10) ◽  
pp. 983-989
Author(s):  
Liang Kim Meng ◽  
Azira Khalil ◽  
Muhamad Hanif Ahmad Nizar ◽  
Maryam Kamarun Nisham ◽  
Belinda Pingguan-Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Bone Age ◽  
Image Feature ◽  
Qualitative Assessment ◽  
Carpal Bone ◽  
Radiographic Images ◽  
Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

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