Seaweed Growth Detection in Aquaculture Environment Using Simple Linear Iterative Clustering Method

2020 ◽  
Author(s):  
Çağdaş Doğan
Keyword(s):  
Total Mass ◽  
Current Practice ◽  
Target Object ◽  
Detection Methods ◽  
Total Biomass ◽  
Underwater Environment ◽  
Simple Linear Iterative Clustering ◽  
Image Segmentation Algorithm ◽  
Growth Detection ◽  
Actual Mass

Estimating the total biomass of cultivates in aquaculture plantations (fisheries, mussel plants, seaweed farms and compound sites) remains to be an issue for the industry and the researchers alike. There has been a diverse array of approaches towards this issue, like using markers, manually stapling the leaflets, weighting the actual mass of the organism and calculating the total mass by extrapolation. Seaweed growth detection is a subset of this problem. Our goal is to introduce a solution by automatically detecting the ratio of the target object in images of seaweed taken from an underwater environment. Researchers/operators then can evaluate the total mass of seaweed. This study aimed to function as a decision support system. The system is built based on an image segmentation algorithm named Simple Linear Iterative Clustering (SLIC) which is a kind of superpixel segmentation. This paper conveys the results obtained from our approach towards the seaweed growth detection, elaborates on the usage and feasibility of our solution in seaweed sites and showcase the economic impact in the industry. Other dimensions of the growth detection methods in current practice for seaweed growth is also discussed, such as lack of automation in the current best-practices while focusing on the difficulties accompanying this status-quo.

scholarly journals Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3595 ◽  
Author(s):  
Anderson Aparecido dos Santos ◽  
José Marcato Junior ◽  
Márcio Santos Araújo ◽  
David Robledo Di Martini ◽  
Everton Castelão Tetila ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Spatial Resolution ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Target Object ◽  
Detection Methods ◽  
Individual Tree ◽  
Art Object

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

scholarly journals Target Contour Recovering for Tracking People in Complex Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Jianhua Zhang ◽  
Sheng Liu ◽  
Y. F. Li ◽  
Jianwei Zhang
Keyword(s):  
Visual Tracking ◽  
Video Sequence ◽  
Tracking System ◽  
Target Object ◽  
Detection Methods ◽  
Partial Occlusion ◽  
Occlusion Detection ◽  
Challenging Problem ◽  
Complex Environments ◽  
Novel Method

Recovering people contours from partial occlusion is a challenging problem in a visual tracking system. Partial occlusions would bring about unreasonable contour changes of the target object. In this paper, a novel method is presented to detect partial occlusion on people contours and recover occluded portions. Unlike other occlusion detection methods, the proposed method is only based on contours, which makes itself more flexible to be extended for further applications. Experiments with synthetic images demonstrate the accuracy of the method for detecting partial occlusions, and experiments on real-world video sequence are also carried out to prove that the method is also good enough to be used to recover target contours.

scholarly journals Automatic Fruit Detection and Couting System Using Neural Network

2021 ◽  
pp. 164-169
Author(s):  
Vaishnavi R Padiyar ◽  
Nagaraja Hebbar N ◽  
Shreya G Shetty
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Current Practice ◽  
Detection Methods ◽  
Yield Estimation ◽  
Counting System ◽  
The Neural Network ◽  
Manual Counting ◽  
Network Object ◽  
Expensive Process

In the field of agriculture, Identification and counting the number of fruits from the image helps the farmers in crop estimation. At present manual counting of fruits present in many places. The current practice of yield estimation based on the manual counting of fruits has many drawbacks as it is time consuming and expensive process. while considering the progress of fruit detection, estimating proper and accurate fruit counts from images in real-world scenarios such as orchards is still a challenging problem. The focus of this paper is on the web application of fruit yield estimation. This web application helps the farmers to count the number of fruits easily. This system provides an automated and efficient fruit counting system using computer vision techniques. This paper provides the progress towards in-field fruit counting using neural network object detection methods. So this process is done by recognizing each fruit in the image and taking the count. In the neural network, we have used YOLO architecture for recognizing the fruits.

DNA nick end labeling: a reliable marker for apoptosis?

1995 ◽  
Vol 53 ◽  
pp. 962-963
Author(s):  
Anne F. Bushnell ◽  
Sarah Webster ◽  
Lynn S. Perlmutter
Keyword(s):  
Cell Death ◽  
Cultured Cells ◽  
Apoptotic Cell ◽  
Morphological Characteristics ◽  
Detection Methods ◽  
Tissue Preparation ◽  
Preparation Methods ◽  
Dna Laddering ◽  
Disease States ◽  
Reliable Marker

Apoptosis, or programmed cell death, is an important mechanism in development and in diverse disease states. The morphological characteristics of apoptosis were first identified using the electron microscope. Since then, DNA laddering on agarose gels was found to correlate well with apoptotic cell death in cultured cells of dissimilar origins. Recently numerous DNA nick end labeling methods have been developed in an attempt to visualize, at the light microscopic level, the apoptotic cells responsible for DNA laddering.The present studies were designed to compare various tissue processing techniques and staining methods to assess the occurrence of apoptosis in post mortem tissue from Alzheimer's diseased (AD) and control human brains by DNA nick end labeling methods. Three tissue preparation methods and two commercial DNA nick end labeling kits were evaluated: the Apoptag kit from Oncor and the Biotin-21 dUTP 3' end labeling kit from Clontech. The detection methods of the two kits differed in that the Oncor kit used digoxigenin dUTP and anti-digoxigenin-peroxidase and the Clontech used biotinylated dUTP and avidinperoxidase. Both used 3-3' diaminobenzidine (DAB) for final color development.

Translating Principles of Speech Science to Clinical Practice: Current and Future Trends in Craniofacial Disorders

2008 ◽  
Vol 18 (1) ◽  
pp. 31-40 ◽  
Author(s):  
David J. Zajac
Keyword(s):  
Clinical Practice ◽  
Speech Intelligibility ◽  
Current Practice ◽  
Computer Assisted ◽  
Behavioral Management ◽  
Single Word ◽  
Future Directions ◽  
Speech Science ◽  
The Impact ◽  
Opinion Article

Abstract The purpose of this opinion article is to review the impact of the principles and technology of speech science on clinical practice in the area of craniofacial disorders. Current practice relative to (a) speech aerodynamic assessment, (b) computer-assisted single-word speech intelligibility testing, and (c) behavioral management of hypernasal resonance are reviewed. Future directions and/or refinement of each area are also identified. It is suggested that both challenging and rewarding times are in store for clinical researchers in craniofacial disorders.

Principles of Solution Focused Therapy and Their Application to Audiology

2014 ◽  
Vol 15 (1) ◽  
pp. 27-33
Author(s):  
James C. Blair
Keyword(s):  
Basic Assumption ◽  
Current Practice ◽  
Helping Professions ◽  
Patient Centered ◽  
Therapy Approach ◽  
Agents Of Change ◽  
Centered Care ◽  
Client Centered Therapy ◽  
Solution Focused ◽  
Solution Focused Therapy

The concept of client-centered therapy (Rogers, 1951) has influenced many professions to refocus their treatment of clients from assessment outcomes to the person who uses the information from this assessment. The term adopted for use in the professions of Communication Sciences and Disorders and encouraged by The American Speech-Language-Hearing Association (ASHA) is patient-centered care, with the goal of helping professions, like audiology, focus more centrally on the patient. The purpose of this paper is to examine some of the principles used in a patient-centered therapy approach first described by de Shazer (1985) named Solution-Focused Therapy and how these principles might apply to the practice of audiology. The basic assumption behind this model is that people are the agents of change and the professional is there to help guide and enable clients to make the change the client wants to make. This model then is focused on solutions, not on the problems. It is postulated that by using the assumptions in this model audiologists will be more effective in a shorter time than current practice may allow.

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