Computer Vision Algorithms for Quantifying the Growth and Behavior of Neurons Cultured on Nanofabricated Surfaces

2003 ◽  
Author(s):  
Muhammad-Amri Abdul Karim ◽  
Khalid Al-Kofahi ◽  
Badrinath Roysam ◽  
Natalie Dowell-Mesfin ◽  
Rifat J. Hussain ◽  
...  
Keyword(s):  
Computer Vision ◽  
And Behavior

Computer Vision Based Privacy Protected Fall Detection and Behavior Monitoring System for the Care of the Elderly

2021 ◽  
Author(s):  
Yugma P.N. Fernando ◽  
Kasun D.B. Gunasekara ◽  
Kumary P. Sirikumara ◽  
Upeksha E. Galappaththi ◽  
Thusithanjana Thilakarathna ◽  
...  
Keyword(s):  
Computer Vision ◽  
Monitoring System ◽  
Fall Detection ◽  
The Elderly ◽  
Behavior Monitoring ◽  
And Behavior

Stress-Free Detection Technologies for Pig Growth Based on Welfare Farming: A Review

2020 ◽  
Vol 36 (3) ◽  
pp. 357-373
Author(s):  
Feiyan Yuan ◽  
Hang Zhang ◽  
Tonghai Liu
Keyword(s):  
Computer Vision ◽  
Body Size ◽  
Body Mass ◽  
Abnormal Behavior ◽  
Complex Data ◽  
Behavior Monitoring ◽  
Conventional Methods ◽  
And Behavior ◽  
Abnormal Behaviors ◽  
Growth Detection

Abstract. The detection of pig growth and monitoring of abnormal behaviors are key steps in pig breeding management. Using conventional methods to obtain information on growth and abnormal behavior causes stress to pigs, directly affects the number of live pigs for market, and decreases the quality of the pork. Moreover, this approach requires considerable labor, reduces economic returns, and does not meet the requirements of high-welfare farming. Compared to the conventional methods for obtaining growth parameters and data on abnormal behaviors, modern information technology provides a new method for stress-free growth detection and behavior monitoring in farmed pigs. This article first summarizes the importance of body size, body mass, and abnormal behaviors as well as the correlations among these factors. For the research on growth detection and behavior monitoring based on computer vision, radio frequency identification (RFID) and sensor technology, methods of detecting increases in body size and body mass and methods of monitoring abnormal behaviors are summarized separately. Through computer-computer vision technology, we found that the data sampling for growth and abnormal behaviors of the pigs was achieved without contact monitoring but, rather, occurred at the expense of complex data calculation and a higher illumination requirement during data collection. However, with the development of depth camera technology and improved product performance, technology based on high-precision depth cameras reduces the amount of data processing and complexity, making it possible to obtain real-time data on pig growth and abnormal behaviors. Moreover, with the advantages of no contact and no stress, the method conforms to the requirements of welfare farming. Keywords: Abnormal behaviors, Stress-free detection, Welfare farming.

Multi-modal Computer Vision for the Detection of Multi-scale Crowd Physical Motions and Behavior in Confined Spaces

2015 ◽  
pp. 162-173 ◽  
Author(s):  
Zoheir Sabeur ◽  
Nikolaos Doulamis ◽  
Lee Middleton ◽  
Banafshe Arbab-Zavar ◽  
Gianluca Correndo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Multi Scale ◽  
Confined Spaces ◽  
And Behavior

scholarly journals Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

2020 ◽  
pp. 1-15 ◽  
Author(s):  
Grace W. Lindsay
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Computational Neuroscience ◽  
Neural Activity ◽  
Vision Research ◽  
State Of The Art ◽  
Biological Vision ◽  
Visual Tasks ◽  
And Behavior

Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNs in vision research beyond basic object recognition.

scholarly journals Generalized Omnipresence Detection (GOD)

2020 ◽  
Vol 2 (2) ◽  
pp. 85-92
Author(s):  
Haretha Winmalar D ◽  
Vani A K ◽  
Sudharsan R ◽  
Hari Krishna R
Keyword(s):  
Computer Vision ◽  
Local Binary Pattern ◽  
Depth Information ◽  
Facial Features ◽  
Human Face ◽  
Detection And Tracking ◽  
Illumination Changes ◽  
Human Object ◽  
And Behavior ◽  
Pattern Histogram

Identification and Tracking of a person in a video are useful in applications such as video surveillance. Two levels of tracking are carried out. They are Classification and monitoring of individuals. The human body’s color histogram is used as the basis for monitoring individuals. Our project can detect a human face in a video and store the detected facial features of the Local Binary Pattern Histogram (LBPH). In a video, once a person is detected, it automatically track that individual and assigns a label to that individual. We use the stored LBPH features to track him in any other videos. In this paper, we proposed and compared the efficiency of two algorithms. One constantly updates the background to make it suitable for illumination changes and other uses depth information with RGB. This is the first step in many complex algorithms in computer vision, such as identification of human activity and behavior recognition. The main challenges in human/object detection and tracking are changing illumination and background. Our work is based on image processing and also it learns the activities and stores them using machine learning with the help of OpenCV, an open source computer vision library.

scholarly journals Fish tracking in vertical slot fishways using computer vision techniques

2014 ◽  
Vol 17 (2) ◽  
pp. 275-292 ◽  
Author(s):  
Álvaro Rodríguez ◽  
María Bermúdez ◽  
Juan R. Rabuñal ◽  
Jerónimo Puertas
Keyword(s):  
Computer Vision ◽  
Kalman Filtering ◽  
Fish Passage ◽  
Hydraulic Structures ◽  
Upstream Migration ◽  
Camera System ◽  
Vertical Slot ◽  
Fish Tracking ◽  
Target Fish ◽  
And Behavior

Vertical slot fishways are hydraulic structures which allow the upstream migration of fish through obstructions in rivers. The appropriate design of these devices should take into account the behavior and biological requirements of the target fish species. However, little is known at the present time about fish behavior in these artificial conditions, which hinders the development of more effective fishway design criteria. In this work, an efficient technique to study fish trajectories and behavior in vertical slot fishways is proposed. It uses computer vision techniques to analyze images collected from a camera system and effectively track fish inside the fishway. Edge and region analysis algorithms are employed to detect fish in extreme image conditions and Kalman filtering is used to track fish along time. The proposed solution has been extensively validated through several experiments, obtaining promising results which may help to improve the design of fish passage devices.

scholarly journals A general deep learning model for bird detection in high resolution airborne imagery

2021 ◽  
Author(s):  
Ben G Weinstein ◽  
Lindsey Gardner ◽  
Vienna Saccomanno ◽  
Ashley Steinkraus ◽  
Andrew Ortega ◽  
...  
Keyword(s):  
Computer Vision ◽  
Ecological Monitoring ◽  
Training Data ◽  
Fine Tuning ◽  
Great Promise ◽  
Detection Model ◽  
Bird Detection ◽  
Model Training ◽  
Computational Resources ◽  
And Behavior

Advances in artificial intelligence for image processing hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing computer vision for ecological monitoring is challenging because it needs large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

How Homo economicus lost her mind and how we can revive her

2018 ◽  
Vol 41 ◽  
Author(s):  
Peter DeScioli
Keyword(s):  
Economic Thought ◽  
History Of Economic Thought ◽  
Homo Economicus ◽  
Economic Theories ◽  
Target Article ◽  
History Of ◽  
The Mind ◽  
And Behavior

AbstractThe target article by Boyer & Petersen (B&P) contributes a vital message: that people have folk economic theories that shape their thoughts and behavior in the marketplace. This message is all the more important because, in the history of economic thought, Homo economicus was increasingly stripped of mental capacities. Intuitive theories can help restore the mind of Homo economicus.

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