scholarly journals Shoreline Detection and Land Segmentation for Autonomous Surface Vehicle Navigation with the Use of an Optical System

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2799
Author(s):  
Stanisław Hożyń ◽  
Jacek Zalewski
Keyword(s):  
Image Processing ◽  
Adaptive Filtering ◽  
Weather Conditions ◽  
Optical Systems ◽  
Probabilistic Clustering ◽  
Autonomous Surface Vehicles ◽  
Autonomous Surface Vehicle ◽  
Clustering Model ◽  
Surrounding Landscape ◽  
Traditional Image

Autonomous surface vehicles (ASVs) are a critical part of recent progressive marine technologies. Their development demands the capability of optical systems to understand and interpret the surrounding landscape. This capability plays an important role in the navigation of coastal areas a safe distance from land, which demands sophisticated image segmentation algorithms. For this purpose, some solutions, based on traditional image processing and neural networks, have been introduced. However, the solution of traditional image processing methods requires a set of parameters before execution, while the solution of a neural network demands a large database of labelled images. Our new solution, which avoids these drawbacks, is based on adaptive filtering and progressive segmentation. The adaptive filtering is deployed to suppress weak edges in the image, which is convenient for shoreline detection. Progressive segmentation is devoted to distinguishing the sky and land areas, using a probabilistic clustering model to improve performance. To verify the effectiveness of the proposed method, a set of images acquired from the vehicle’s operative camera were utilised. The results demonstrate that the proposed method performs with high accuracy regardless of distance from land or weather conditions.

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

SOLVING THE PROBLEM OF INTER-AIRCRAFT NAVIGATION TO ENSURE FLYING IN FORMATION USING A TECHNICAL VISION SYSTEM

2021 ◽  
pp. 32-39
Author(s):  
Д.А. Смирнов ◽  
В.Г. Бондарев ◽  
А.В. Николенко
Keyword(s):  
Image Processing ◽  
Flight Control ◽  
Vision System ◽  
Weather Conditions ◽  
Infrared Range ◽  
Light Sources ◽  
Navigation Systems ◽  
Aircraft Structure ◽  
Aircraft Navigation ◽  
Technical Vision

Проведен краткий анализ как отечественных, так и зарубежных систем межсамолетной навигации. В ходе анализа были отражены недостатки систем межсамолетной навигации и представлен актуальный подход повышения точности системы навигации за счет применения системы технического зрения. Для определения местоположения ведущего самолета предлагается рассмотреть в качестве измерительного комплекса систему технического зрения, которая способна решать большой круг задач на различных этапах, в частности, и полет строем. Систему технического зрения предлагается установить на ведомом самолете с целью измерения всех параметров, необходимых для формирования автоматического управления полетом летательного аппарата. Обработка изображений ведущего самолета выполняется с целью определения координат трех идентичных точек на фоточувствительных матрицах. Причем в качестве этих точек выбираются оптически контрастные элементы конструкции летательного аппарата, например окончания крыла, хвостового оперения и т.д. Для упрощения процедуры обработки изображений возможно использование полупроводниковых источников света в инфракрасном диапазоне (например, с длиной волны λ = 1,54 мкм), что позволяет работать даже в сложных метеоусловиях. Такой подход может быть использован при автоматизации полета строем более чем двух летательных аппаратов, при этом необходимо только оборудовать системой технического зрения все ведомые самолеты группы The article provides a brief analysis of both domestic and foreign inter-aircraft navigation systems. In the course of the analysis, we found the shortcomings of inter-aircraft navigation systems and presented an up-to-date approach to improving the accuracy of the navigation system through the use of a technical vision system. To determine the location of the leading aircraft, we proposed to consider a technical vision system as a measuring complex, which is able to solve a large range of tasks at various stages, in particular, flight in formation. We proposed to install the technical vision system on the slave aircraft in order to measure all the parameters necessary for the formation of automatic flight control of the aircraft. We performed an image processing of the leading aircraft to determine the coordinates of three identical points on photosensitive matrices. Moreover, we selected optically contrasting elements of the aircraft structure as these points, for example, the end of the wing, tail, etc. To simplify the image processing procedure, it is possible to use semiconductor light sources in the infrared range (for example, with a wavelength of λ = 1.54 microns), which allows us to work even in difficult weather conditions. This approach can be used when automating a flight in formation of more than two aircraft, while it is only necessary to equip all the guided aircraft of the group with a technical vision system

Detection of Blood-Related Diseases Using Deep Neural Nets

2019 ◽  
pp. 1-15
Author(s):  
Rajithkumar B. K. ◽  
Shilpa D. R. ◽  
Uma B. V.
Keyword(s):  
Image Processing ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Neural Nets ◽  
Laboratory Methods ◽  
Intelligent Algorithms ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

scholarly journals Sky Monitoring System for Flying Object Detection Using 4K Resolution Camera

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7071
Author(s):  
Takehiro Kashiyama ◽  
Hideaki Sobue ◽  
Yoshihide Sekimoto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Monitoring System ◽  
State Of The Art ◽  
Weather Conditions ◽  
Network Technology ◽  
Real Time Processing ◽  
Time Processing ◽  
High Rise ◽  
Neural Network Technology

The use of drones and other unmanned aerial vehicles has expanded rapidly in recent years. These devices are expected to enter practical use in various fields, such as taking measurements through aerial photography and transporting small and lightweight objects. Simultaneously, concerns over these devices being misused for terrorism or other criminal activities have increased. In response, several sensor systems have been developed to monitor drone flights. In particular, with the recent progress of deep neural network technology, the monitoring of systems using image processing has been proposed. This study developed a monitoring system for flying objects using a 4K camera and a state-of-the-art convolutional neural network model to achieve real-time processing. We installed a monitoring system in a high-rise building in an urban area during this study and evaluated the precision with which it could detect flying objects at different distances under different weather conditions. The results obtained provide important information for determining the accuracy of monitoring systems with image processing in practice.

Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design

2021 ◽  
Vol 40 (2) ◽  
pp. 1-19
Author(s):  
Ethan Tseng ◽  
Ali Mosleh ◽  
Fahim Mannan ◽  
Karl St-Arnaud ◽  
Avinash Sharma ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Optical Design ◽  
Neural Nets ◽  
Fine Tuning ◽  
Optical Systems ◽  
Design Tools ◽  
End To End ◽  
Application Specific ◽  
Network Processing

Most modern commodity imaging systems we use directly for photography—or indirectly rely on for downstream applications—employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax . We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting—together with hardware ISP post processing and detection—outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.

On the limit of the spectral distribution of large-dimensional random quaternion covariance matrices

2017 ◽  
Vol 06 (02) ◽  
pp. 1750004 ◽  
Author(s):  
Yanqing Yin ◽  
Jiang Hu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Adaptive Filtering ◽  
Random Matrix Theory ◽  
Random Matrix ◽  
Spectral Distribution ◽  
Matrix Theory ◽  
Covariance Matrices ◽  
Moment Condition ◽  
Quaternion Matrices

The use of quaternions and quaternion matrices in practice, such as in machine learning, adaptive filtering, vector sensing and image processing, has recently been rapidly gaining in popularity. In this paper, by applying random matrix theory, we investigate the spectral distribution of large-dimensional quaternion covariance matrices when the quaternion samples are drawn from a population that satisfies a mild moment condition. We also apply the result to several common models.

Nonlinear optical systems for image processing and complex field monitoring

1999 ◽  
Author(s):  
Anna D. Kudryavtseva ◽  
Albina I. Sokolovskaya ◽  
Nicolaii V. Tcherniega
Keyword(s):  
Image Processing ◽  
Nonlinear Optical ◽  
Field Monitoring ◽  
Optical Systems ◽  
Complex Field

Image Fingerprinting Based on Improved Image Normalization and DFT

2014 ◽  
Vol 556-562 ◽  
pp. 4759-4764
Author(s):  
Hu Guan ◽  
Zhi Zeng ◽  
Jie Liu ◽  
Shu Wu Zhang
Keyword(s):  
Image Processing ◽  
Aspect Ratio ◽  
Copyright Protection ◽  
Normalization Method ◽  
Image Normalization ◽  
Angle Estimation ◽  
Geometric Distortions ◽  
Estimation And Correction ◽  
Traditional Image

As a new image copyright protection technique, a novel image fingerprinting algorithm based on improved image normalization and DFT is proposed in this paper. We first research the DFT amplitude coefficients deeply and improve the traditional image normalization method to fit for more general images, including aspect ratio and rotated angle estimation and correction schemes before normalizing the image. Then combine the improved image normalization method and DFT together to extract the image fingerprint according to the DFT amplitude coefficients. Compared with others’ achievements, our method performs better in resisting common image processing attacks and geometric distortions, also some kinds of combined distortions.

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