A multimodal Parkinson quantification by fusing eye and gait motion patterns, using covariance descriptors, from non-invasive computer vision

2021 ◽  
pp. 106607
Author(s):  
J. Archila ◽  
A. Manzanera ◽  
F. Martinez
Keyword(s):  
Computer Vision ◽  
Motion Patterns ◽  
Non Invasive

scholarly journals A computer vision approach for studying fossorial and cryptic crabs

2020 ◽  
Author(s):  
César Herrera ◽  
Janine Sheaves ◽  
Ronald Baker ◽  
Marcus Sheaves
Keyword(s):  
Computer Vision ◽  
Natural Environment ◽  
New Technologies ◽  
Fiddler Crab ◽  
Analytical Framework ◽  
Machine Learning Algorithms ◽  
Intraspecific Interactions ◽  
Motion Patterns ◽  
Non Invasive ◽  
Crab Abundance

SummaryDespite the increasing need to catalogue and describe biodiversity and the ecosystem processes it underpins, these tasks remain inherently challenging. This is particularly true for species that are difficult to observe in their natural environment, such as fossorial and cryptic crabs that inhabit intertidal sediments. Traditional sampling techniques for intertidal crabs are often invasive, labour intensive and/or inconsistent. These factors can limit the amount and type of data that can be collected which in turn hinders our ability to obtain reliable ecological estimates and compare findings between studies. Computer vision and machine learning algorithms present an opportunity to innovate and improve sampling approaches. Moreover, cheaper and tougher recording devices and the diversity of open source software further boost the possibilities of achieving rigorous image-based sampling, which can broaden the range of questions that can be addressed from the data collected. Despite its significant potential, the software and algorithms associated with image-based sampling may be daunting to researchers without expertise in computer vision. Therefore, there is a need to develop protocols and data processing workflows to showcase the value of embracing new technologies. This paper presents a non-invasive computer vision and learning protocol for sampling fossorial and cryptic crabs in their natural environment. The image-based protocol is underpinned by fit-for-purpose and off-the-shelf software. We demonstrate this approach using fiddler crab and sediment recordings to study and quantify crab abundance, motion patterns, behaviour, intraspecific interactions, and estimate bioturbation rates. We discuss current limitations in this protocol and identify opportunities for improvement and additional data stream options that can be obtained from this approach. We conclude that this protocol can overcome some of the limitations associated with traditional techniques for sampling intertidal crabs, and could be applied to other taxa or ecosystems that present similar challenges. We believe this sampling and analytical framework represents an important step forward in understanding the ecology of species and their functional role within ecosystems.

scholarly journals Close-Range Photogrammetry and Infrared Imaging for Non-Invasive Honeybee Hive Population Assessment

2018 ◽  
Vol 7 (9) ◽  
pp. 350 ◽  
Author(s):  
Luis López-Fernández ◽  
Susana Lagüela ◽  
Pablo Rodríguez-Gonzálvez ◽  
José Martín-Jiménez ◽  
Diego González-Aguilera
Keyword(s):  
Computer Vision ◽  
Infrared Imaging ◽  
Imaging Techniques ◽  
Population Level ◽  
Non Invasive ◽  
Close Range Photogrammetry ◽  
3D Geometry ◽  
Thermographic Imaging ◽  
Close Range ◽  
Bee Colonies

Close-range photogrammetry and thermographic imaging techniques are used for the acquisition of all the data needed for the non-invasive assessment of a honeybee hive population. Temperature values complemented with precise 3D geometry generated using novel close-range photogrammetric and computer vision algorithms are used for the computation of the inner beehive temperature at each point of its surface. The methodology was validated through its application to three reference beehives with different population levels. The temperatures reached by the exterior surfaces of the hives showed a direct correlation with the population level. In addition, the knowledge of the 3D reality of the hives and the position of each temperature value allowed the positioning of the bee colonies without the need to open the hives. This way, the state of honeybee hives regarding the growth of population can be estimated without disturbing its natural development.

scholarly journals Non-Contact Pulse Detector using Video Analytics

2020 ◽  
Vol 9 (5) ◽  
pp. 1597-1600
Keyword(s):  
Computer Vision ◽  
Heart Rate ◽  
Computational Cost ◽  
Heart Beat ◽  
Signal Frequency ◽  
Waiting Room ◽  
Non Invasive ◽  
Safe Side ◽  
Patient Will ◽  
Low Computational Cost

Non-contact pulse detector used for heart beat measurement based on computer vision, where a standard color camera captures the plethysmographic signal and the heart rates are processed and estimated dynamically. It is important that the quantities are taken in a non-invasive manner, which is invisible to the patient. Presently, many methods have been proposed for non-contact measurement. The proposed method based on the computer vision technique is enhanced to overcome the above drawbacks and it requires low computational cost. Many of the hospitals are using surveillance cameras, from these cameras we can monitor the video of the patients waiting in the queue. The camera is attached in the patients’ waiting room and the faces of the patients are monitored. Many factors are considered in the phases of image acquisition, as well as in the plethysmographic signal development, pre-processing and filtering. The pre-filter step uses numerical analysis techniques to cut the signal offset. The proposed method decouples the heart rate from the plethysmographic signal frequency. The proposed system helps in detecting the heart rate of a Patient who is waiting in queue for longer time. Based on the heart rate the seriousness of patient is identified and giving the preference to the patient and treatment will be started, with this the patient will be in safe side.

scholarly journals Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts

2020 ◽  
Vol 48 ◽  
pp. 1020-1028 ◽  
Author(s):  
Aditya M. Deshpande ◽  
Anil Kumar Telikicherla ◽  
Vinay Jakkali ◽  
David A. Wickelhaus ◽  
Manish Kumar ◽  
...  
Keyword(s):  
Computer Vision ◽  
Invasive Monitoring ◽  
Non Invasive ◽  
Factory Floor

scholarly journals A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3799 ◽  
Author(s):  
Palacios ◽  
Diago ◽  
Tardaguila
Keyword(s):  
Computer Vision ◽  
Cross Validation ◽  
Image Acquisition ◽  
Mobile Sensing ◽  
Wine Industry ◽  
Non Invasive ◽  
Sensing Platform ◽  
Invasive Method ◽  
Cluster Compactness ◽  
Leave One Out

Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a new, non-invasive method based on the combination of computer vision and machine learning technology for cluster compactness assessment under field conditions from on-the-go red, green, blue (RGB) image acquisition. A mobile sensing platform was used to automatically capture RGB images of grapevine canopies and fruiting zones at night using artificial illumination. Likewise, a set of 195 clusters of four red grapevine varieties of three commercial vineyards were photographed during several years one week prior to harvest. After image acquisition, cluster compactness was evaluated by a group of 15 experts in the laboratory following the International Organization of Vine and Wine (OIV) 204 standard as a reference method. The developed algorithm comprises several steps, including an initial, semi-supervised image segmentation, followed by automated cluster detection and automated compactness estimation using a Gaussian process regression model. Calibration (95 clusters were used as a training set and 100 clusters as the test set) and leave-one-out cross-validation models (LOOCV; performed on the whole 195 clusters set) were elaborated. For these, determination coefficient (R2) of 0.68 and a root mean squared error (RMSE) of 0.96 were obtained on the test set between the image-based compactness estimated values and the average of the evaluators’ ratings (in the range from 1–9). Additionally, the leave-one-out cross-validation yielded a R2 of 0.70 and an RMSE of 1.11. The results show that the newly developed computer vision based method could be commercially applied by the wine industry for efficient cluster compactness estimation from RGB on-the-go image acquisition platforms in commercial vineyards.

scholarly journals Computer Vision for Brain Disorders Based Primarily on Ocular Responses

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaotao Li ◽  
Fangfang Fan ◽  
Xuejing Chen ◽  
Juan Li ◽  
Li Ning ◽  
...  
Keyword(s):  
Computer Vision ◽  
Cognitive Processing ◽  
Autism Spectrum ◽  
Brain Disorders ◽  
Invasive Approach ◽  
Non Invasive ◽  
Pupillary Responses ◽  
Retinal Microvasculature ◽  
Ophthalmic Imaging ◽  
The Central Nervous System

Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and spontaneous blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. In this review, we outline multiple techniques in which ocular assessments may serve as a non-invasive approach for the early detections of various brain disorders, such as autism spectrum disorder (ASD), Alzheimer's disease (AD), schizophrenia (SZ), and major depressive disorder (MDD). In addition, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine learning-based AI, especially computer vision (CV) with deep-learning neural networks, to shed new light on the field of cognitive neuroscience, which is most likely to lead to novel evaluations and interventions for brain disorders. Hence, we highlight the potential of using AI to evaluate brain disorders based primarily on ocular features.

Comparison of the spine kinematics by defining lumbar as single and multi-segmental in completing critical daily task

2020 ◽  
Vol 14 (4) ◽  
pp. 7600-7608
Author(s):  
W. A. A Saad ◽  
Mohd Azuwan Mat Dzahir ◽  
Yamamoto Shinichirou ◽  
Mohamed Hussein ◽  
Maziah Mohamad ◽  
...  
Keyword(s):  
Independent Living ◽  
Early Stage ◽  
In Vitro Study ◽  
Spinal Curvature ◽  
Invasive Approach ◽  
Motion Patterns ◽  
Non Invasive ◽  
Spine Kinematics ◽  
Significant Difference

The change of the spinal curvature in completing a variety of daily tasks is essential to independent living. There is still a lack of studies highlighting the lumbar segmental contribution during sit-to-stand (STS) and stand-to-flexion (STF) using non-invasive study. The purpose of this study is to compare the spine kinematics by defining lumbar as a single and multi-segmental during continuous daily motion in healthy Asian adults using a non-invasive approach. During STS, most subjects implemented kyphotic lumbar curve during the early stage of motion which revealed poor posture implementation and significant differences in the lumbar kinematics which were only noticeable at specific phases between both approaches. A significant difference in multi-segmental behaviour was observed only at the end of the motion. All three segments displayed different time responses during the transition from kyphotic to lordotic curve. Passive/delayed behavior within the lower lumbar segment was observed between 0-50% of motion completion. During STF, statistically significant differences were found between assuming lumbar as a single and multi-segment in all phases. This in vitro study identified characteristic motion patterns in the lumbar spine during daily motions. The results provided a clear description of the healthy spinal condition of adults and may serve to identify specific multi-segmental contribution.

Computer Vision and Spectral Analysis Technologies for Non-Invasive Plant Studying

2020 ◽  
Vol 67 (2) ◽  
pp. 107-114
Author(s):  
Yuriy A. Proshkin
Keyword(s):  
Computer Vision ◽  
Spectral Analysis ◽  
Digital Image ◽  
Invasive Plant ◽  
Research Purpose ◽  
Plant Phenotyping ◽  
Optical Range ◽  
Vision Systems ◽  
Non Invasive ◽  
Computer Vision Systems

Computer vision and spectral analysis of digital images are technologies that allow the use of automated and robotic systems for non-invasive plant studying, production and harvesting of agricultural products, phenotyping and selection of new plant species. (Research purpose) The research purpose is in analyzing the application of modern digital non-invasive methods of plant research using computer (technical) vision and prospects for their implementation. (Materials and methods) Authors have reviewed the works on the use of non-invasive methods for obtaining information about the state of plants. The article presents classification and analyze of the collected materials according to the criteria for collecting and analyzing digital data, the scope of application and prospects for implementation. Authors used the methods of a systematic approach to the research problem. (Results and discussion) The article presents the main directions of using computer vision systems and digital image analysis. The use of computer vision technologies in plant phenotyping and selection reduces the labor cost of research, allowing the formation of digital databases with a clear structure and classification by morphological features. It was found that the introduction of neural networks in the process of digital image processing increases the accuracy of plant recognition up to 99.9 percent, and infectious diseases up to 80 percent on average. (Conclusions) The article shows that in studies using hyperspectral optical cameras and sensors are used cameras with an optical range from 400 to 1000 nanometers, and in rare cases, hyperspectral camera systems with a total coverage of the optical range from 350 to 2000 nanometers. These optical systems are mainly installed on unmanned aerial vehicles to determine vegetation indices, foci of infection and the fertility of agricultural fields. It was found that computer vision systems with hyperspectral cameras could be used in conjunction with fluorescent plant markers, which makes it possible to solve complex problems of crop recognition without involving computational resources.

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