Two-stream Network with Phase Map for Few-shot Classification

2021 ◽  
Author(s):  
Jiahao Wang ◽  
Bin Song ◽  
Dan Wang ◽  
Hao Qin
Keyword(s):  
Stream Network ◽  
Shot Classification ◽  
Phase Map

Actual issues of systematization of scientific knowledge on the conditions of shot in the forensic ballistics

2020 ◽  
Vol 2020 (3) ◽  
pp. 164-169
Author(s):  
Igor' Latyshov ◽  
Fedor Samuylenko
Keyword(s):  
Scientific Knowledge ◽  
Communication Systems ◽  
Production Method ◽  
Natural Phenomenon ◽  
Projectile Energy ◽  
Energy Characteristics ◽  
Shot Classification ◽  
Forensic Ballistics

In this research, there was considered a challenge of constructing a system of scientific knowledge of the shot conditions in judicial ballistics. It was observed that there are underlying factors that are intended to ensureits [scientific knowledge] consistency: identification of the list of shot conditions, which require consideration when solving expert-level research tasks on weapons, cartridges and traces of their action; determination of the communication systems in the course of objects’ interaction, which present the result of exposure to the conditions of the shot; classification of the shot conditions based on the grounds significant for solving scientific and practical problems. The article contains the characteristics of a constructive, functional factor (condition) of weapons and cartridges influence, environmental and fire factors, the structure of the target and its physical properties, situational and spatial factors, and projectile energy characteristics. Highlighted are the forms of connections formed in the course of objects’ interaction, proposed are the author’s classifications of forensically significant shooting conditions with them being divided on the basis of the following criteria: production from the object of interaction, production from a natural phenomenon, production method, results weapon operation and utilization, duration of exposure, type of structural connections between interaction objects, number of conditions that apply when firing and the forming traces.

scholarly journals Geomorphology and Landslide-Prone Area in Cisolok District, Sukabumi Regency

2019 ◽  
Vol 125 ◽  
pp. 01005 ◽  
Author(s):  
Mochamad Seandy Alfarabi ◽  
Supriatna ◽  
Masita Dwi Mandini Manessa ◽  
Andry Rustanto ◽  
Yoanna Ristya
Keyword(s):  
Land Use ◽  
Road Network ◽  
Geological Structure ◽  
Overlay Analysis ◽  
Stream Network ◽  
Prone Area ◽  
Steep Topography ◽  
Physical Variables ◽  
Natural Characteristics ◽  
Very High

Sukabumi District located in Southern West Java known as a region that has diverse natural characteristics, however, it is vulnerable to disasters, especially landslides. Moreover, this study focuses on Cisolok District because this region always occurred landslides every year due to topography aspect. The aim of this study is to analyze the influence of geomorphology to landslide-prone area in Cisolok District to reduce landslides. This study used overlay analysis for geomorphology mapping, while the Frequency Ratio (FR) method used for landslide-prone area mapping. Several physical variables used in this study such as slope, elevation, lithology, geological structure, road network, stream network, land use, soil type, rainfall, and landslide location. The result shows that the study areas have diverse geomorphology units dominated by volcanic slope with steep topography. While landslide-prone area consist of four classes : namely 17,03% low, 62,05% medium, 14,4% high, and 6,51% very high. Variety of landslide vulnerability in study area influenced by terrain form, land genesis, and geomorphic process.

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

A Color-Doppler Shear-Wave-Imaging Phase-reconstruction Method Using Four Color Flow Images

2016 ◽  
Vol 39 (3) ◽  
pp. 172-188
Author(s):  
Naoki Sunaguchi ◽  
Yoshiki Yamakoshi ◽  
Takahito Nakajima
Keyword(s):  
Image Quality ◽  
Shear Wave ◽  
Initial Phase ◽  
Color Doppler ◽  
Reconstruction Method ◽  
Phase Reconstruction ◽  
Color Flow ◽  
Shear Wave Imaging ◽  
Wave Imaging ◽  
Phase Map

This study investigates shear wave phase map reconstruction using a limited number of color flow images (CFIs) acquired with a color Doppler ultrasound imaging instrument. We propose an efficient reconstruction method to considerably reduce the number of CFIs required for reconstruction and compare this method with Fourier analysis-based color Doppler shear wave imaging. The proposed method uses a two-step phase reconstruction process, including an initial phase map derived from four CFIs using an advanced iterative algorithm of optical interferometry. The second step reduces phase artifacts in the initial phase map using an iterative correction procedure that cycles between the Fourier and inverse Fourier domains while imposing directional filtering and total variation regularization. We demonstrate the efficacy of this method using synthetic and experimental data of a breast phantom and human breast tissue. Our results show that the proposed method maintains image quality and reduces the number of CFIs required to four; previous methods have required at least 32 CFIs to achieve equivalent image quality. The proposed method is applicable to real-time shear wave elastography using a continuous shear wave produced by a mechanical vibrator.

scholarly journals 3D Reconstruction with Single-Shot Structured Light RGB Line Pattern

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4819
Author(s):  
Yikang Li ◽  
Zhenzhou Wang
Keyword(s):  
3D Reconstruction ◽  
Moving Objects ◽  
Structured Light ◽  
Color Space ◽  
Reconstruction Technique ◽  
Single Shot ◽  
Line Pattern ◽  
Hsv Color Space ◽  
Phase Map ◽  
Line Segmentation

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

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