scholarly journals Computer vision based individual fish identification using skin dot pattern

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Petr Cisar ◽  
Dinara Bekkozhayeva ◽  
Oleksandr Movchan ◽  
Mohammadmehdi Saberioon ◽  
Rudolf Schraml
Keyword(s):  
New Technologies ◽  
Fish Farming ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Biomass Estimation ◽  
Invasive Fish ◽  
Fish School ◽  
Non Invasive ◽  
Individual Fish ◽  
Fish Tagging

AbstractPrecision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging.

scholarly journals Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sougata Sadhukhan ◽  
Holly Root-Gutteridge ◽  
Bilal Habib
Keyword(s):  
Reproductive Behaviour ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Survey Method ◽  
Reliable Estimate ◽  
Mark Recapture ◽  
Test Dataset ◽  
Individual Level ◽  
Non Invasive ◽  
Supervised Classification Methods

AbstractPrevious studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

2021 ◽  
Author(s):  
Feng He ◽  
Hongjiang Liu ◽  
Chunxue Liu ◽  
Guangjing Bao
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Convolutional Neural Network ◽  
New Technologies ◽  
Hazard Identification ◽  
Identification Accuracy ◽  
Radar Image ◽  
Geologic Hazard ◽  
Continuous Increase ◽  
Identification Model

Abstract To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) technology is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built, which is based on CNN’s image identification and big data algorithm calculation, and it can effectively improve the geologic hazard identification accuracy. By designing experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent Identification under CNN and big data technology is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase of training samples, the identification accuracy of various network models is also improved. By means of radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is introduced into data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. This research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

scholarly journals Cow Rump Identification Based on Lightweight Convolutional Neural Networks

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 361
Author(s):  
Handan Hou ◽  
Wei Shi ◽  
Jinyan Guo ◽  
Zhe Zhang ◽  
Weizheng Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Dairy Cow ◽  
Individual Recognition ◽  
Animal Husbandry ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Production Environment ◽  
Accurate Identification ◽  
Computing Device

Individual identification of dairy cows based on computer vision technology shows strong performance and practicality. Accurate identification of each dairy cow is the prerequisite of artificial intelligence technology applied in smart animal husbandry. While the rump of each dairy cow also has lots of important features, so do the back and head, which are also important for individual recognition. In this paper, we propose a non-contact cow rump identification method based on convolutional neural networks. First, the rump image sequences of the cows while feeding were collected. Then, an object detection model was applied to detect the cow rump object in each frame of image. Finally, a fine-tuned convolutional neural network model was trained to identify cow rumps. An image dataset containing 195 different cows was created to validate the proposed method. The method achieved an identification accuracy of 99.76%, which showed a better performance compared to other related methods and a good potential in the actual production environment of cow husbandry, and the model is light enough to be deployed in an edge-computing device.

The archaeological site of chellah, new technologies for investigation, modeling, and mapping.

2021 ◽  
Author(s):  
Iman el guertet ◽  
Abdellatif aarab ◽  
Abdelkader larabi ◽  
Mohammed Jemmal ◽  
Sabah benchekroun
Keyword(s):  
New Technologies ◽  
Low Cost ◽  
Archaeological Site ◽  
Archaeological Sites ◽  
Electrical Tomography ◽  
Geophysical Investigation ◽  
Islamic Period ◽  
Non Invasive ◽  
Aerial Vehicle ◽  
Modern Technologies

<p>archaeological sites have been always a subject of curiosity and search, the archaeologists and scientists from different specialties have been wondering about the origins of the man civilization, about the way our forefathers lived, how they nourished, dressed, and housed themselves, what techniques were used for the transport, the fishing, and the business, about the culture and the spiritual practices. in fact, the modern technologies, practices, and innovations are only a continuation of what was once; this is why the human being believes it is imperative to revive and understand the heritage and to discover its secrets. in the present work which pours in the same direction, we decided to revive and explore a wealthy site located in rabat, the Moroccan capital, this site is named chellah, which represents the summing up of historical eras from the antiquity to the Islamic period and which is marked by the presence of antique and Islamic constructions which reflect this continuity. our research aims to build a model for the detection of areas that are not yet excavated but are already mentioned by archaeologists, geographers, and historians to validate their hypothesis and to find out where exactly these areas are located. our methodology is based on the processing of unmanned aerial vehicle<strong> (uav)</strong> images to generate high-resolution photogrammetric products with low cost, those datasets will be analyzed with a technique that has been in use since the '80s and which is using crop, soil, and shadow marks visualized on images taken by aerial photography. this analysis gave us the vision to select the zones on which a geophysical investigation by electrical tomography was carried out to approve the presence of the archeological components that require future excavation. our study focused on the importance of non-invasive methodologies for the study, preservation, and valorization of archaeological sites.</p>

Spot on: using camera traps to individually monitor one of the world’s largest lizards

2020 ◽  
Vol 47 (4) ◽  
pp. 326 ◽  
Author(s):  
Harry A. Moore ◽  
Jacob L. Champney ◽  
Judy A. Dunlop ◽  
Leonie E. Valentine ◽  
Dale G. Nimmo
Keyword(s):  
Activity Patterns ◽  
Image Data ◽  
Dorsal Surface ◽  
Camera Trap ◽  
Camera Traps ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Lizard Species ◽  
Late Afternoon ◽  
Large Animals

Abstract ContextEstimating animal abundance often relies on being able to identify individuals; however, this can be challenging, especially when applied to large animals that are difficult to trap and handle. Camera traps have provided a non-invasive alternative by using natural markings to individually identify animals within image data. Although camera traps have been used to individually identify mammals, they are yet to be widely applied to other taxa, such as reptiles. AimsWe assessed the capacity of camera traps to provide images that allow for individual identification of the world’s fourth-largest lizard species, the perentie (Varanus giganteus), and demonstrate other basic morphological and behavioural data that can be gleaned from camera-trap images. MethodsVertically orientated cameras were deployed at 115 sites across a 10000km2 area in north-western Australia for an average of 216 days. We used spot patterning located on the dorsal surface of perenties to identify individuals from camera-trap imagery, with the assistance of freely available spot ID software. We also measured snout-to-vent length (SVL) by using image-analysis software, and collected image time-stamp data to analyse temporal activity patterns. ResultsNinety-two individuals were identified, and individuals were recorded moving distances of up to 1975m. Confidence in identification accuracy was generally high (91%), and estimated SVL measurements varied by an average of 6.7% (min=1.8%, max=21.3%) of individual SVL averages. Larger perenties (SVL of >45cm) were detected mostly between dawn and noon, and in the late afternoon and early evening, whereas small perenties (SVL of <30cm) were rarely recorded in the evening. ConclusionsCamera traps can be used to individually identify large reptiles with unique markings, and can also provide data on movement, morphology and temporal activity. Accounting for uneven substrates under cameras could improve the accuracy of morphological estimates. Given that camera traps struggle to detect small, nocturnal reptiles, further research is required to examine whether cameras miss smaller individuals in the late afternoon and evening. ImplicationsCamera traps are increasingly being used to monitor reptile species. The ability to individually identify animals provides another tool for herpetological research worldwide.

scholarly journals Recent Advances in Ultrasound Diagnosis of Carpal Tunnel Syndrome

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 596 ◽  
Author(s):  
Yuichi Yoshii ◽  
Chunfeng Zhao ◽  
Peter C. Amadio
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
New Technologies ◽  
Cross Sectional ◽  
Ultrasonic Examination ◽  
Non Invasive ◽  
Syndrome Diagnosis ◽  
Tunnel Syndrome ◽  
Property Changes ◽  
High Resolution Ultrasonography

With the widespread use of high-resolution ultrasonography, ultrasonic examination has been shown to be useful as a diagnostic method for carpal tunnel syndrome. The main advantages of ultrasonography are that it is simple, quick, non-invasive, and economical. Another advantage is that tissue dynamics can be observed with real-time imaging. In recent reports, it has been shown that ultrasonic examination can provide similar diagnostic accuracy as nerve conduction study in the diagnosis of carpal tunnel syndrome. It has been expected that ultrasound demand in daily medical care will continue to increase. Ultrasonography in carpal tunnel syndrome shows an enlarged median nerve in proximal carpal tunnel, thickening of the flexor retinaculum, and edema around flexor tendons in cross-sectional images. In addition, with the introduction of new technologies such as ultrasonic elastography and speckle tracking, it has become possible to quantify dynamics and material property changes of nerves, tendons, and their surrounding structures. In this review, we describe recent advancements of carpal tunnel syndrome diagnosis based on ultrasound dynamic images, and discuss its pathology.

scholarly journals Potential for the use of vocal individuality as a conservation research tool in two threatened Philippine hornbill species, the Visayan Hornbill and the Rufous-headed Hornbill

2009 ◽  
Vol 19 (1) ◽  
pp. 83-97 ◽  
Author(s):  
RICHARD POLICHT ◽  
MILADA PETRŮ ◽  
LUCIA LASTIMOZA ◽  
LEO SUAREZ
Keyword(s):  
Census Data ◽  
Maximum Amplitude ◽  
Individual Identification ◽  
Frequency Range ◽  
Harmonic Frequency ◽  
Loud Calls ◽  
Vocal Individuality ◽  
Non Invasive ◽  
Temporal Variables ◽  
Marking Technique

SummaryThis study presents the first multivariate analysis of hornbill vocalizations and the first bioacoustic study of any Philippine hornbill species. We analyzed loud calls of two Philippine hornbill species, the Rufous-headed Hornbill Aceros waldeni and the Visayan Hornbill Penelopides panini panini, to assess the possibility for their use in individual identification.Our study showed that individuals of the two studied hornbill species can be identified on the basis of their loud calls, which means that these calls potentially contain information about the caller. Discriminant analysis classified 89% of individual Rufous-headed Hornbills and 90% of individual Visayan Hornbills correctly. The acoustic variables describing the most variation among individual Visayan Hornbills were spectral variables (second amplitude peak) and temporal variables (location of the maximum amplitude and call duration). The calls of individual Rufous-headed Hornbill were differentiated mainly by spectral variables (the fundamental and the first harmonic frequency, and additionally the upper quartile of the frequency range). Frequency parameters in Rufous-headed Hornbill calls were significantly lower than those in Visayan Hornbills. The use of acoustic monitoring of individuals as a non-invasive marking technique could help to monitor hornbill individual life history and to improve census data using capture-mark-recapture technique.

scholarly journals Point-of-Care Pathology with Miniature Microscopes

2011 ◽  
Vol 34 (3) ◽  
pp. 81-98 ◽  
Author(s):  
Jonathan T. C. Liu ◽  
Nathan O. Loewke ◽  
Michael J. Mandella ◽  
Richard M. Levenson ◽  
James M. Crawford ◽  
...  
Keyword(s):  
Real Time ◽  
New Technologies ◽  
Point Of Care ◽  
Definitive Treatment ◽  
Clinical Tool ◽  
Training Requirements ◽  
Non Invasive ◽  
Complex Process ◽  
Molecular Indicators

Advances in optical designs are enabling the development of miniature microscopes that can examine tissue in situ for early anatomic and molecular indicators of disease, in real time, and at cellular resolution. These new devices will lead to major changes in how diseases are detected and managed, driving a shift from today's diagnostic paradigm of biopsy followed by histopathology and recommended therapy, to non-invasive point-of-care diagnosis with possible same-session definitive treatment. This shift may have major implications for the training requirements of future physicians to enable them to interpret real-timein vivomicroscopic data, and will also shape the emerging fields of telepathology and telemedicine. Implementation of new technologies into clinical practice is a complex process that requires bridging gaps between clinicians, engineers and scientists. This article provides a forward-looking discussion of these issues, with a focus on malignant and pre-malignant lesions, by first highlighting some of the clinical areas where point-of-carein vivomicroscopy could address unmet needs, and then by reviewing the technological challenges that are being addressed, or need to be addressed, forin vivomicroscopy to become a standard clinical tool.

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