Line drawing via saliency map and ETF

2022 ◽  
Vol 16 (5) ◽  
Author(s):  
Shiguang Liu ◽  
Ziqi Liu
Keyword(s):  
Line Drawing ◽  
Saliency Map

In situ study of electron beam-induced chemical vapor deposition of Au in an environmental TEM

1995 ◽  
Vol 53 ◽  
pp. 256-257
Author(s):  
J. Drucker ◽  
R. Sharma ◽  
J. Kouvetakis ◽  
K.H.J. Weiss
Keyword(s):  
Electron Beam ◽  
Chemical Vapor Deposition ◽  
Vapor Deposition ◽  
Quantum Effect ◽  
Line Drawing ◽  
Chemical Vapor ◽  
Environmental Cell ◽  
Engineering Changes ◽  
Kinetics Of

Patterning of metals is a key element in the fabrication of integrated microelectronics. For circuit repair and engineering changes constructive lithography, writing techniques, based on electron, ion or photon beam-induced decomposition of precursor molecule and its deposition on top of a structure have gained wide acceptance Recently, scanning probe techniques have been used for line drawing and wire growth of W on a silicon substrate for quantum effect devices. The kinetics of electron beam induced W deposition from WF6 gas has been studied by adsorbing the gas on SiO2 surface and measuring the growth in a TEM for various exposure times. Our environmental cell allows us to control not only electron exposure time but also the gas pressure flow and the temperature. We have studied the growth kinetics of Au Chemical vapor deposition (CVD), in situ, at different temperatures with/without the electron beam on highly clean Si surfaces in an environmental cell fitted inside a TEM column.

Saliency Map based Multi-View Rendering for Autostereoscopic Displays

2019 ◽  
Vol 2019 (3) ◽  
pp. 656-1-656-6
Author(s):  
Yuzhong Jiao ◽  
Chan Man Chi ◽  
Mark, P. C. Mok
Keyword(s):  
Saliency Map ◽  
Autostereoscopic Displays

POLYGONAL APPROXIMATION OF DIGITAL CURVE BASED ON REVERSE ENGINEERING CONCEPT

2013 ◽  
Vol 13 (04) ◽  
pp. 1350017 ◽  
Author(s):  
KUMAR S. RAY ◽  
BIMAL KUMAR RAY
Keyword(s):  
Reverse Engineering ◽  
Linear Time ◽  
Line Drawing ◽  
Approximation Error ◽  
Zero Approximation ◽  
Polygonal Approximation ◽  
Digital Curve ◽  
Symmetric Approximation ◽  
Engineering Concept ◽  
Automatic Algorithm

This paper applies reverse engineering on the Bresenham's line drawing algorithm [J. E. Bresenham, IBM System Journal, 4, 106–111 (1965)] for polygonal approximation of digital curve. The proposed method has a number of features, namely, it is sequential and runs in linear time, produces symmetric approximation from symmetric digital curve, is an automatic algorithm and the approximating polygon has the least non-zero approximation error as compared to other algorithms.

scholarly journals On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

2021 ◽  
Vol 11 (14) ◽  
pp. 6269
Author(s):  
Wang Jing ◽  
Wang Leqi ◽  
Han Yanling ◽  
Zhang Yun ◽  
Zhou Ruyan
Keyword(s):  
Saliency Detection ◽  
Color Difference ◽  
Saliency Map ◽  
Apple Fruit ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Natural Environments ◽  
Fast Detection ◽  
Single Target ◽  
Training Samples

For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an important impact on the subsequent edge prediction, this paper proposed an automatic detection method for apple fruit targets in natural environments based on saliency detection and traditional color difference methods. Combined with the original image, the histogram backprojection algorithm was used to further optimize the salient image results. A dynamic adaptive overlapping target separation algorithm was proposed to locate the single target fruit and further to determine the initial contour for DeepSnake, in view of the possible overlapping fruit regions in the saliency map. Finally, the target fruit was labeled based on the segmentation results of the examples. In the experiment, 300 training datasets were used to train the DeepSnake model, and the self-built dataset containing 1036 pictures of apples in various situations under natural environment was tested. The detection accuracy of target fruits under non-overlapping shaded fruits, overlapping fruits, shaded branches and leaves, and poor illumination conditions were 99.12%, 94.78%, 90.71%, and 94.46% respectively. The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images, which showed that the proposed algorithm can effectively separate the overlapping fruits through a not-very-large training samples and realize the rapid and accurate detection of apple targets.

scholarly journals Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1280
Author(s):  
Hyeonseok Lee ◽  
Sungchan Kim
Keyword(s):  
Neural Networks ◽  
Model Prediction ◽  
Saliency Map ◽  
Model Parameters ◽  
Learning Capability ◽  
Fine Grained ◽  
Network Layers ◽  
Saliency Maps ◽  
Novel Approach ◽  
Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

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