The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Neural networks for precise measurement in computer vision systems

1995 ◽  
Vol 27 (3) ◽  
pp. 225-236 ◽  
Author(s):  
Chao-Ton Su ◽  
C.Alec Chang ◽  
Fang-Chih Tien
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Precise Measurement ◽  
Vision Systems ◽  
Computer Vision Systems

Training neural networks for automotive computer vision systems considering types of false estimations

Trudy NAMI ◽  
2021 ◽  
pp. 37-47
Author(s):  
P. A. Vasin ◽  
I. A. Kulikov
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
False Positive ◽  
False Positive Rate ◽  
Loss Functions ◽  
Jaccard Coefficient ◽  
Vision Systems ◽  
Positive Rate ◽  
Surrounding Space ◽  
Computer Vision Systems

Introduction (problem statement and relevance). This article deals with the problem of training artificial neural networks intended to analyze images of the surrounding space in automotive computer vision systems. The conventional training approach implies using loss functions that only improve the overall identification quality making no distinction between types of possible false predictions. However, traffic safety risks associated with different types of prediction errors are unequal being higher for false positive estimations.The purpose of this work is to propose improved loss functions, which include penalties for false positive predictions, and to study how using these functions affects the behavior of a convolutional neural network when estimating the drivable space.Methodology and research methods. The proposed loss functions are based on the Sørensen-Dice coefficient differing from each other in the approaches to penalizing false positive errors. The performance of the trained neural networks is evaluated using three metrics, namely, the Jaccard coefficient, False Positive Rate and False Negative Rate. The proposed solutions are compared with the conventional one by calculating the ratios of their respective metrics.Scientific novelty and results. The improved loss functions have been proposed to train computer vision algorithms featuring penalties for false positive estimations. The experimental study of the trained neural networks using a test dataset has shown that the improved loss functions allow reducing the False Positive Rate by 21%.The practical significance of this work is constituted by the proposed method of training neural networks that allows to increase the safety of automated driving through an improved accuracy of analyzing the surrounding space using computer vision systems.

scholarly journals SemifreddoNets: Partially Frozen Neural Networks for Efficient Computer Vision Systems

2020 ◽  
pp. 193-208
Author(s):  
Leo F. Isikdogan ◽  
Bhavin V. Nayak ◽  
Chyuan-Tyng Wu ◽  
Joao Peralta Moreira ◽  
Sushma Rao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Vision Systems ◽  
Computer Vision Systems

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

scholarly journals Improving skeleton algorithm for helping Caenorhabditis elegans trackers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pablo E. Layana Castro ◽  
Joan Carles Puchalt ◽  
Antonio-José Sánchez-Salmerón
Keyword(s):  
Computer Vision ◽  
Caenorhabditis Elegans ◽  
Transformation Function ◽  
Simple Solution ◽  
New Method ◽  
Vision Systems ◽  
Distance Transformation ◽  
Automatic Computer ◽  
C Elegans ◽  
Computer Vision Systems

AbstractOne of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.

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