scholarly journals Detection of the Deep-Sea Plankton Community in Marine Ecosystem with Underwater Robotic Platform

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6720
Author(s):  
Jiaxing Wang ◽  
Mingqiang Yang ◽  
Zhongjun Ding ◽  
Qinghe Zheng ◽  
Deqiang Wang ◽  
...  
Keyword(s):  
Optical Flow ◽  
Deep Sea ◽  
Global Climate ◽  
Focal Length ◽  
Marine Ecosystem ◽  
Flow Fields ◽  
Plankton Community ◽  
Experimental Comparison ◽  
Robotic Platform ◽  
Dynamic Background

Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5–2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches.

Monocular Structure from Motion Based on Angular BA

2015 ◽  
Vol 742 ◽  
pp. 290-293
Author(s):  
Xiu Zhi Li ◽  
Ai Lin Yang ◽  
Huan Qiu ◽  
Song Min Jia
Keyword(s):  
Optical Flow ◽  
Structure From Motion ◽  
Structure Refinement ◽  
3D Structure ◽  
Flow Fields ◽  
Image Point ◽  
Pixel Pair ◽  
Pair Matching ◽  
Dense Correspondence

This paper presents a technique for monocular Structure from Motion (SFM) that reconstructs 3D world shape. The technique proposed uses optical flow for 2D pixel pair matching and Angular Bundle Ajustment (ABA) for 3D structure refinement. The proposed strategy has two main advantages. Firstly, optical flow fields provide sufficient dense correspondence of image point pairs and secondly, ABA outperforms classic BA variants, especially for the points relatively far from camera. The reconstruction results obtained in realistic scenario demonstrate the effectiveness and accuracy of the proposed algorithm.

Locating the singular point in first-order optical flow fields.

1998 ◽  
Vol 24 (5) ◽  
pp. 1415-1430 ◽  
Author(s):  
Susan F. te Pas ◽  
Astrid M. L. Kappers ◽  
Jan J. Koenderink
Keyword(s):  
Singular Point ◽  
Optical Flow ◽  
Flow Fields ◽  
First Order

scholarly journals Experimental comparison of two methods to study barley responses to partial submergence

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Alexandra Miricescu ◽  
Tomás Byrne ◽  
Catherine M. Doorly ◽  
Carl K. Y. Ng ◽  
Susanne Barth ◽  
...  
Keyword(s):  
Global Climate ◽  
Experimental Comparison ◽  
Soil Conditions ◽  
Early Developmental Stage ◽  
Starch Solution ◽  
Waterlogging Tolerance ◽  
Climate Conditions ◽  
Flooding Events ◽  
Staple Crop ◽  
Early Developmental

Abstract Background Crop yield is dependent on climate conditions, which are becoming both more variable and extreme in some areas of the world as a consequence of global climate change. Increased precipitation and flooding events are the cause of important yield losses due to waterlogging or (partial) submergence of crops in the field. Our ability to screen efficiently and quickly for varieties that have increased tolerance to waterlogging or (partial) submergence is important. Barley, a staple crop worldwide, is particularly sensitive to waterlogging. Screening for waterlogging tolerant barley varieties has been ongoing for many years, but methods used to screen vary greatly, from the type of soil used to the time at which the treatment is applied. This variation makes it difficult to cross-compare results. Results Here, we have devised a scoring system to assess barley tolerance to waterlogging and compare two different methods when partial submergence is applied with either water or a starch solution at an early developmental stage, which is particularly sensitive to waterlogging or partial submergence. The use of a starch solution has been previously shown to result in more reducing soil conditions and has been used to screen for waterlogging tolerance. Conclusions Our results show that the two methods provide similar results to qualitatively rank varieties as tolerant or sensitive, while also affecting plants differently, in that application of a starch solution results in stronger and earlier symptoms than applying partial submergence with water.

scholarly journals MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING

2018 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Muhamad Soleh ◽  
Grafika Jati ◽  
Muhammad Hafizhuddin Hilman
Keyword(s):  
Kalman Filter ◽  
Object Detection ◽  
Optical Flow ◽  
Motion Blur ◽  
Research Topic ◽  
Vehicle Tracking ◽  
Flow Density ◽  
Illumination Change ◽  
Dynamic Background ◽  
Detection And Tracking

Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.

Moving object detection based on optical flow and neural network fusion

2016 ◽  
Vol 9 (4) ◽  
pp. 325-335 ◽  
Author(s):  
Haiqun Qin ◽  
Ziyang Zhen ◽  
Kun Ma
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Optical Flow ◽  
Target Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Monitoring Systems ◽  
Dynamic Background ◽  
Content Type ◽  
New Generation

Purpose The purpose of this paper is to meet the large demand for the new-generation intelligence monitoring systems that are used to detect targets within a dynamic background. Design/methodology/approach A dynamic target detection method based on the fusion of optical flow and neural network is proposed. Findings Simulation results verify the accuracy of the moving object detection based on optical flow and neural network fusion. The method eliminates the influence caused by the movement of the camera to detect the target and has the ability to extract a complete moving target. Practical implications It provides a powerful safeguard for target detection and targets the tracking application. Originality/value The proposed method represents the fusion of optical flow and neural network to detect the moving object, and it can be used in new-generation intelligent monitoring systems.

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