scholarly journals Development of segmentation algorithm for determining planktonic objects from microscopic images

2021 ◽  
Vol 944 (1) ◽  
pp. 012025
Author(s):  
E Prakasa ◽  
A Rachman ◽  
D R Noerdjito ◽  
R Wardoyo
Keyword(s):  
Computer Vision ◽  
Energy Transfer ◽  
Accurate Method ◽  
Biogeochemical Cycles ◽  
Segmentation Algorithm ◽  
Segmentation Result ◽  
Performance Parameters ◽  
Microscopic Images ◽  
Segmentation Methods ◽  
Planktonic Species

Abstract Plankton are free-floating organisms that live, grow, and move along with the ocean currents. This free-floating organism plays important roles as primary producers, they serve as a link to energy transfer, and a factor that regulates the biogeochemical cycles. Indonesia, with almost 60% of its territory covered by the ocean, harbours a wide variety of planktonic species. However, one of the issues within usual planktonic studies is the lack of a fast and accurate method for identifying and classifying the plankton type. Thus, the computer vision methods on microscopic images were proposed to deal with the problem. The classification follows two main steps, detecting plankton location and followed by plankton differentiation. The segmentation algorithm is required to limit the determination area. The present study describes the segmentation methods on fifteen plankton types. The U-Net based architecture was implemented to segment the plankton texture from other objects. The segmentation result was also compared with the manual assessment to compute the performance parameters. The accuracy, 0.970±0.025, gives the highest value whereas the smallest value is found in the precision parameter, 0.761±0.156.

scholarly journals A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 683
Author(s):  
José L. Escalona ◽  
Pedro Urda ◽  
Sergio Muñoz
Keyword(s):  
Computer Vision ◽  
Inertial Sensors ◽  
Vision System ◽  
Accurate Method ◽  
Sensor Data ◽  
Measuring System ◽  
Railway Vehicles ◽  
Track Geometry ◽  
Video Cameras ◽  
Measuring Unit

This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.

scholarly journals Object segmentation methods for online model acquisition to guide robotic grasping

2021 ◽  
Author(s):  
Dmitri Ignakov
Keyword(s):  
Object Segmentation ◽  
Vision System ◽  
Geometric Model ◽  
Graph Cuts ◽  
Segmentation Algorithm ◽  
Service Robots ◽  
Segmentation Method ◽  
Robotic Grasping ◽  
Model Acquisition ◽  
Segmentation Methods

A vision system is an integral component of many autonomous robots. It enables the robot to perform essential tasks such as mapping, localization, or path planning. A vision system also assists with guiding the robot's grasping and manipulation tasks. As an increased demand is placed on service robots to operate in uncontrolled environments, advanced vision systems must be created that can function effectively in visually complex and cluttered settings. This thesis presents the development of segmentation algorithms to assist in online model acquisition for guiding robotic manipulation tasks. Specifically, the focus is placed on localizing door handles to assist in robotic door opening, and on acquiring partial object models to guide robotic grasping. . First, a method for localizing a door handle of unknown geometry based on a proposed 3D segmentation method is presented. Following segmentation, localization is performed by fitting a simple box model to the segmented handle. The proposed method functions without requiring assumptions about the appearance of the handle or the door, and without a geometric model of the handle. Next, an object segmentation algorithm is developed, which combines multiple appearance (intensity and texture) and geometric (depth and curvature) cues. The algorithm is able to segment objects without utilizing any a priori appearance or geometric information in visually complex and cluttered environments. The segmentation method is based on the Conditional Random Fields (CRF) framework, and the graph cuts energy minimization technique. A simple and efficient method for initializing the proposed algorithm which overcomes graph cuts' reliance on user interaction is also developed. Finally, an improved segmentation algorithm is developed which incorporates a distance metric learning (DML) step as a means of weighing various appearance and geometric segmentation cues, allowing the method to better adapt to the available data. The improved method also models the distribution of 3D points in space as a distribution of algebraic distances from an ellipsoid fitted to the object, improving the method's ability to predict which points are likely to belong to the object or the background. Experimental validation of all methods is performed. Each method is evaluated in a realistic setting, utilizing scenarios of various complexities. Experimental results have demonstrated the effectiveness of the handle localization method, and the object segmentation methods.

scholarly journals Image Segmentation Methods for Flood Monitoring System

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1825
Author(s):  
Nur Muhadi ◽  
Ahmad Abdullah ◽  
Siti Bejo ◽  
Muhammad Mahadi ◽  
Ana Mijic
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Digital Images ◽  
Rapid Development ◽  
Jaccard Index ◽  
Monitoring Systems ◽  
Dice Similarity Coefficient ◽  
Hybrid Technique ◽  
Segmentation Methods ◽  
Flood Monitoring

Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

CT Images Segmentation Based on Dynamic Relative Fuzzy Region Growing Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 4233-4236
Author(s):  
Zhen You Zhang ◽  
Guo Huan Lou
Keyword(s):  
Confidence Interval ◽  
Region Growing ◽  
Ct Images ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Segmentation Result ◽  
Ct Image ◽  
Initial Segmentation ◽  
Fuzzy Region

Segmentation algorithm of CT Image is discussed in this paper. Dynamic relative fuzzy region growing algorithm is used for CT. At the beginning of the segmentation, the confidence interval region growing algorithm is used. The overlapping parts in the initial segmentation result is segmented again with the improved fuzzy connected, and then determine which region the overlapping parts belong to. Thus, the final segmentation result can be obtained. Since the algorithm contains the advantages of region growing algorithm, fuzzy connected algorithm and the region competition, the runtime of segmentation is greatly reduced and better experimental results are obtained.

Computational Healthcare System With Image Analysis

2019 ◽  
pp. 89-127 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computer Vision ◽  
Image Analysis ◽  
Social Insurance ◽  
Valuable Data ◽  
Clinical Notes ◽  
Information Reporting ◽  
Segmentation Methods ◽  
Crucial Part

The quickly extending field of huge information examination has begun to assume a crucial part in the advancement of human services practices and research. In this chapter, challenges like gathering information from complex heterogeneous patient sources, utilizing the patient/information relationships in longitudinal records, understanding unstructured clinical notes in the correct setting and efficiently dealing with expansive volumes of medicinal imaging information, and removing conceivably valuable data is shown. Healthcare and IoT and machine learning along with data mining are also discussed. Image analysis and segmentation methods comparative study is given for the examination of computer vision, imaging handling, and example acknowledgment has gained considerable ground amid the previous quite a few years. Examiners have distributed an abundance of essential science and information reporting the advance and social insurance application on medicinal imaging.

Computer Vision Based Pre-processing of Microscopic Images

2021 ◽  
Author(s):  
Kurmangazy Kongratbayev ◽  
Cemil Turan ◽  
Bekzot Tursunmamatov
Keyword(s):  
Computer Vision ◽  
Microscopic Images

I-RIPRAP Computer Vision Software for Automated Size and Shape Characterization of Riprap in Stockpile Images

2021 ◽  
pp. 036119812110013
Author(s):  
Haohang Huang ◽  
Jiayi Luo ◽  
Issam Qamhia ◽  
Erol Tutumluer ◽  
John M. Hart ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Aggregate Size ◽  
Advanced Characterization ◽  
Accurate Method ◽  
Analysis Tool ◽  
Sediment Control ◽  
Size And Shape ◽  
Volume Weight

Riprap rocks and large-sized aggregates have been used extensively in geotechnical and hydraulic engineering applications to serve as a key component for erosion/sediment control and scour protection. Toward sustainable and reliable use of riprap rocks, an efficient and accurate method to characterize the size and shape properties is deemed necessary. Current state-of-the-practice methods mostly assess riprap properties with labor-intensive and time-consuming inspection routines that involve manual size and weight measurements. Recent advances in the field of computer vision have been leveraged in this paper to apply deep learning for the development of an innovative image analysis tool for quantitative and efficient riprap characterization. Based on the single-aggregate and stockpile-aggregates studies conducted on this topic, this paper introduces a newly developed computer program, named I-RIPRAP, for the advanced characterization of aggregate size and shape from images of riprap stockpile(s). The essential features and software workflow, as well as the function and mechanism of each module, are described and discussed in detail. In addition to the deep learning methods for segmentation, I-RIPRAP also improves the morphological analyses with a volume/weight estimation module and a riprap category reference module to generate useful results that can facilitate quality assurance/quality control tasks. The I-RIPRAP software is envisioned to serve as an efficient and innovative tool for field and in-place evaluations of riprap and large-sized aggregates.

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