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Relevant bibliographies by topics / Bird recognition

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Academic literature on the topic 'Bird recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Bird recognition"

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A. Tayal, Madhuri. "Bird Identification by Image Recognition." HELIX 8, no. 6 (October 31, 2018): 4349–52. http://dx.doi.org/10.29042/2018-4349-4352.

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Zhao, Zhicheng, Ze Luo, Jian Li, Kaihua Wang, and Bingying Shi. "Large-Scale Fine-Grained Bird Recognition Based on a Triplet Network and Bilinear Model." Applied Sciences 8, no. 10 (October 13, 2018): 1906. http://dx.doi.org/10.3390/app8101906.

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The main purpose of fine-grained classification is to distinguish among many subcategories of a single basic category, such as birds or flowers. We propose a model based on a triple network and bilinear methods for fine-grained bird identification. Our proposed model can be trained in an end-to-end manner, which effectively increases the inter-class distance of the network extraction features and improves the accuracy of bird recognition. When experimentally tested on 1096 birds in a custom-built dataset and on Caltech-UCSD (a public bird dataset), the model achieved an accuracy of 88.91% and 85.58%, respectively. The experimental results confirm the high generalization ability of our model in fine-grained image classification. Moreover, our model requires no additional manual annotation information such as object-labeling frames and part-labeling points, which guarantees good versatility and robustness in fine-grained bird recognition.

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Heller, Jason R., and John D. Pinezich. "Automatic recognition of harmonic bird sounds." Journal of the Acoustical Society of America 118, no. 3 (September 2005): 2000. http://dx.doi.org/10.1121/1.4785665.

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Mohanty, Ricky, Bandi Kumar Mallik, and Sandeep Singh Solanki. "Recognition of bird species based on spike model using bird dataset." Data in Brief 29 (April 2020): 105301. http://dx.doi.org/10.1016/j.dib.2020.105301.

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Dawkins, Marian Stamp. "How Do Hens View Other Hens? the Use of Lateral and Binocular Visual Fields in Social Recognition." Behaviour 132, no. 7-8 (1995): 591–606. http://dx.doi.org/10.1163/156853995x00225.

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AbstractWhen shown familiar and unfamiliar birds at different distances, hens viewed birds 0.7 m or 1.4 m away with modal head angles between 54° and 72° from the midline, using the lateral visual field but viewed birds closer (less than 20 cm) binocularly, with the head within 18° either side of the midline (Expt. 1 When faced with a choice between a familiar and an unfamiliar bird at different distances, hens chose the familiar bird if the choice could be made 8 cm away but their choices were random if they had to chose 66 or 124 cm away (Expt. 2). This suggests that hens may be unable to discriminate familiar from unfamiliar birds except when they are very close to them. Observations of freely moving birds suddenly confronted with another hen (Expt. 3) showed that even when the object bird was familiar, it was in all cases initially scrutinized from a close distance (26 cm or less), which is consistent with the hypothesis that hens are unable to recognize other birds except when close enough to view them with the myopic lower frontal field. Reasons for this constraint on social recognition are discussed.

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Et. al., Chandra B,. "Automated Bird Species Recognition System Based on Image Processing and Svm Classifier." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 351–56. http://dx.doi.org/10.17762/turcomat.v12i2.813.

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Here, in this study we can learn about Bird species recognition. In forest areas cameras are fixed at various locations which capture images periodically. From those images the birds living in such dense forest areas can be identified. It would be useful if we can able to classify the species of birds with the help of those images. But that is not an easy task because of the variations in the light effects, illumination and camera viewpoints. So we need to involve image processing techniques for preprocessing the captured image and also deep learning techniques are to be implemented for classifying the images. For classification purpose training is to be done with the help of image data set. Here we propose a method of discriminating birds by means of the ratio of the distance between eye and beak to that of the beak width. By combining this mythology with image processing and SVM classification technique a new bird species recognition algorithm is proposed. The proposed new methodology will improve the accuracy in classifying.

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Wang, Zhaojun, Jiangning Wang, Congtian Lin, Yan Han, Zhaosheng Wang, and Liqiang Ji. "Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks." Animals 11, no. 5 (April 27, 2021): 1263. http://dx.doi.org/10.3390/ani11051263.

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With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

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Sharp, Stuart P., Andrew McGowan, Matthew J. Wood, and Ben J. Hatchwell. "Learned kin recognition cues in a social bird." Nature 434, no. 7037 (April 2005): 1127–30. http://dx.doi.org/10.1038/nature03522.

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Strickler, Stephanie A. "Recognition of Young in A Colonially Nesting Bird." Ethology 119, no. 2 (December 6, 2012): 130–37. http://dx.doi.org/10.1111/eth.12041.

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Schimmel, Leah, and Frederick Wasserman. "An Interspecific Comparison of Individual and Species Recognition in the Passerines Turdus Migratorius and Cyanocitta Cristata." Behaviour 118, no. 1-2 (1991): 115–26. http://dx.doi.org/10.1163/156853991x00238.

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AbstractRobins (Turdus migratorius) and blue jays (Cyanocitta cristata) were raised in heterospecific and conspecific pairs to observe the impact of early social experience on the later ability to recognize and associate with conspecifics. Birds were also tested to determine if they could distinguish between a 'nestmate' (the bird they were raised with), versus an unfamiliar bird of the nestmate's species. All choices involved combinations of the two species. After thirty days of being raised with another individual (approximately day 10 to day 40 post-hatch), each experimental subject was tested in a weight-sensitive electronic 'choice' apparatus. Blue jays preferred the company of a nestmate over a non-nestmate. Blue jays also chose the nestmate's species when given a choice between two unfamiliar birds, robins chose the alternative to the nestmate's species and did not discriminate between the nestmate and its conspecific.

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Dissertations / Theses on the topic "Bird recognition"

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Van, der Merwe Hugo Jacobus. "Bird song recognition with Hidden Markov Models /." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/914.

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Bastas, Selin A. "Nocturnal Bird Call Recognition System for Wind Farm Applications." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1325803309.

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Reyes, Elsa. "A Comparison of Image Processing Techniques for Bird Detection." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1239.

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Orchard fruits and vegetable crops are vulnerable to wild birds and animals. These wild birds and animals can cause critical damage to the produce. Traditional methods of scaring away birds such as scarecrows are not long-term solutions but short-term solutions. This is a huge problem especially near areas like San Luis Obispo where there are vineyards. Bird damage can be as high as 50% for grapes being grown in vineyards. The total estimated revenue lost annually in the 10 counties in California due to bird and rodent damage to 22 selected crops ranged from $168 million to $504 million (in 2009 dollars). A more effective and permanent system needs to be put into place. Monitoring systems in agricultural settings could potentially provide a lot of data for image processing. Most current monitoring systems however don’t focus on image processing but instead really heavily on sensors. Just having sensors for certain systems work, but for birds, monitoring it is not an option because they are not domesticated like pigs, cows etc. in which most these agricultural monitoring systems work on. Birds can fly in and out of the area whereas domesticated animals can be confined to certain physical regions. The most crucial step in a smart scarecrow system would be how a threat would v be detected. Image processing methods can be effectively applied to detecting items in video footage. This paper will focus on bird detection and will analyze motion detection with image subtraction, bird detection with template matching, and bird detection with the Viola-Jones Algorithm. Of the methods considered, bird detection with the Viola-Jones Algorithm had the highest accuracy (87%) with a somewhat low false positive rate. This image processing step would ideally be incorporated with hardware (such as a microcontroller or FPGA, sensors, a camera etc.) to form a smart scarecrow system.

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Patton, Tadd B. "Altered features of female pigeons (Columba livia) elicit preference behavior in male pigeons." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001656.

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Kahl, Stefan. "Identifying Birds by Sound: Large-scale Acoustic Event Recognition for Avian Activity Monitoring." Universitätsverlag Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A36986.

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Automated observation of avian vocal activity and species diversity can be a transformative tool for ornithologists, conservation biologists, and bird watchers to assist in long-term monitoring of critical environmental niches. Deep artificial neural networks have surpassed traditional classifiers in the field of visual recognition and acoustic event classification. Still, deep neural networks require expert knowledge to design, train, and test powerful models. With this constraint and the requirements of future applications in mind, an extensive research platform for automated avian activity monitoring was developed: BirdNET. The resulting benchmark system yields state-of-the-art scores across various acoustic domains and was used to develop expert tools and public demonstrators that can help to advance the democratization of scientific progress and future conservation efforts.
Die automatisierte Überwachung der Vogelstimmenaktivität und der Artenvielfalt kann ein revolutionäres Werkzeug für Ornithologen, Naturschützer und Vogelbeobachter sein, um bei der langfristigen Überwachung kritischer Umweltnischen zu helfen. Tiefe künstliche neuronale Netzwerke haben die traditionellen Klassifikatoren im Bereich der visuellen Erkennung und akustische Ereignisklassifizierung übertroffen. Dennoch erfordern tiefe neuronale Netze Expertenwissen, um leistungsstarke Modelle zu entwickeln, trainieren und testen. Mit dieser Einschränkung und unter Berücksichtigung der Anforderungen zukünftiger Anwendungen wurde eine umfangreiche Forschungsplattform zur automatisierten Überwachung der Vogelaktivität entwickelt: BirdNET. Das daraus resultierende Benchmark-System liefert state-of-the-art Ergebnisse in verschiedenen akustischen Bereichen und wurde verwendet, um Expertenwerkzeuge und öffentliche Demonstratoren zu entwickeln, die dazu beitragen können, die Demokratisierung des wissenschaftlichen Fortschritts und zukünftige Naturschutzbemühungen voranzutreiben.

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Whitwell, Sarah Margaret. "The impact of isolation from mammalian predators on the anti-predator behaviours of the North Island robin (Petroica longipes) : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Conservation Biology at Massey University, Auckland, New Zealand." Massey University, 2009. http://hdl.handle.net/10179/1142.

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Conservation in New Zealand has begun to focus heavily on the restoration of degraded mainland ecosystems and the reintroduction of native species that have become locally extinct. In many cases the individuals that are selected for reintroduction are harvested from ‘mammal-free’ offshore islands. This thesis examines the effects of isolation from mammalian predators on the predator avoidance behaviours and predator recognition abilities of New Zealand birds using the North Island robin as a model. It also investigates whether any effects of isolation from mammalian predators has a lasting impact on mainland populations founded by individuals from offshore islands. Nest site selection behaviours were compared across three populations that are exposed to different suites of predators and have differing translocation histories; Benneydale, Tiritiri Matangi and Wenderholm. Point height intercept and point-centred quarter surveys were used to compare habitat availability between the sites and to compare nest sites with the available habitat. Eight nest characteristic variables were also compared across the three sites using a principle component analysis. Benneydale nests were located higher in the trees and were more concealed than nests at the other two sites. Nests on Tiritiri Matangi were supported by large numbers of thin branches and were located toward the periphery of the nest tree. Unfortunately these differences are very difficult to interpret due to a high degree of variation in the habitat types present at the three sites. The anti-predator behaviours initiated in response to a model stoat, model morepork and control were used to test the ability of nesting robins to recognise the threat that each of these treatments might pose to nest success. Behavioural variables were compared between Benneydale, Tiritiri Matangi and Wenderholm using a response intensity scoring system and a principle component analysis. The results indicated that isolation from mammalian predators on Tiritiri Matangi has suppressed the ability of robins on the island to recognise the predatory threat posed by a stoat. They also suggest that the intense mammal control carried out at Wenderholm may have inhibited the ability of local robins to produce strong anti-predator responses when faced with a stoat.

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Movin, Andreas, and Jonathan Jilg. "Kan datorer höra fåglar?" Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254800.

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Ljudigenkänning möjliggörs genom spektralanalys, som beräknas av den snabba fouriertransformen (FFT), och har under senare år nått stora genombrott i samband med ökningen av datorprestanda och artificiell intelligens. Tekniken är nu allmänt förekommande, i synnerhet inom bioakustik för identifiering av djurarter, en viktig del av miljöövervakning. Det är fortfarande ett växande vetenskapsområde och särskilt igenkänning av fågelsång som återstår som en svårlöst utmaning. Även de främsta algoritmer i området är långt ifrån felfria. I detta kandidatexamensarbete implementerades och utvärderades enkla algoritmer för att para ihop ljud med en ljuddatabas. En filtreringsmetod utvecklades för att urskilja de karaktäristiska frekvenserna vid fem tidsramar som utgjorde basen för jämförelsen och proceduren för ihopparning. Ljuden som användes var förinspelad fågelsång (koltrast, näktergal, kråka och fiskmås) så väl som egeninspelad mänsklig röst (4 unga svenska män). Våra resultat visar att framgångsgraden normalt är 50–70%, den lägsta var fiskmåsen med 30% för en liten databas och den högsta var koltrasten med 90% för en stor databas. Rösterna var svårare för algoritmen att särskilja, men de hade överlag framgångsgrader mellan 50% och 80%. Dock gav en ökning av databasstorleken generellt inte en ökning av framgångsgraden. Sammanfattningsvis visar detta kandidatexamensarbete konceptbeviset bakom fågelsångigenkänning och illustrerar såväl styrkorna som bristerna av dessa enkla algoritmer som har utvecklats. Algoritmerna gav högre framgångsgrad än slumpen (25%) men det finns ändå utrymme för förbättring eftersom algoritmen vilseleddes av ljud av samma frekvenser. Ytterligare studier behövs för att bedöma den utvecklade algoritmens förmåga att identifiera ännu fler fåglar och röster.
Sound recognition is made possible through spectral analysis, computed by the fast Fourier transform (FFT), and has in recent years made major breakthroughs along with the rise of computational power and artificial intelligence. The technology is now used ubiquitously and in particular in the field of bioacoustics for identification of animal species, an important task for wildlife monitoring. It is still a growing field of science and especially the recognition of bird song which remains a hard-solved challenge. Even state-of-the-art algorithms are far from error-free. In this thesis, simple algorithms to match sounds to a sound database were implemented and assessed. A filtering method was developed to pick out characteristic frequencies at five time frames which were the basis for comparison and the matching procedure. The sounds used were pre-recorded bird songs (blackbird, nightingale, crow and seagull) as well as human voices (4 young Swedish males) that we recorded. Our findings show success rates typically at 50–70%, the lowest being the seagull of 30% for a small database and the highest being the blackbird at 90% for a large database. The voices were more difficult for the algorithms to distinguish, but they still had an overall success rate between 50% and 80%. Furthermore, increasing the database size did not improve success rates in general. In conclusion, this thesis shows the proof of concept and illustrates both the strengths as well as short-comings of the simple algorithms developed. The algorithms gave better success rates than pure chance of 25% but there is room for improvement since the algorithms were easily misled by sounds of the same frequencies. Further research will be needed to assess the devised algorithms' ability to identify even more birds and voices.

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Mann, Richard Philip. "Prediction of homing pigeon flight paths using Gaussian processes." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:bf6c3fb5-5208-4dfe-aa0a-6e6da45c0d87.

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Studies of avian navigation are making increasing use of miniature Global Positioning Satellite devices, to regularly record the position of birds in flight with high spatial and temporal resolution. I suggest a novel approach to analysing the data sets pro- duced in these experiments, focussing on studies of the domesticated homing pigeon (Columba Livia) in the local, familiar area. Using Gaussian processes and Bayesian inference as a mathematical foundation I develop and apply a statistical model to make quantitative predictions of homing pigeon flight paths. Using this model I show that pigeons, when released repeatedly from the same site, learn and follow a habitual route back to their home loft. The model reveals the rate of route learning and provides a quantitative estimate of the habitual route complete with associated spatio-temporal covariance. Furthermore I show that this habitual route is best described by a sequence of isolated waypoints rather than as a continuous path, and that these waypoints are preferentially found in certain terrain types, being especially rare within urban and forested environments. As a corollary I demonstrate an extension of the flight path model to simulate ex- periments where pigeons are released in pairs, and show that this can account for observed large scale patterns in such experiments based only on the individual birds’ previous behaviour in solo flights, making a successful quantitative prediction of the critical value associated with a non-linear behavioural transition.

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Fox, Elizabeth J. S. "Call-independent identification in birds." University of Western Australia. School of Animal Biology, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0218.

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[Truncated abstract] The identification of individual animals based on acoustic parameters is a non-invasive method of identifying individuals with considerable advantages over physical marking procedures. One requirement for an effective and practical method of acoustic individual identification is that it is call-independent, i.e. determining identity does not require a comparison of the same call or song type. This means that an individuals identity over time can be determined regardless of any changes to its vocal repertoire, and different individuals can be compared regardless of whether they share calls. Although several methods of acoustic identification currently exist, for example discriminant function analysis or spectrographic cross-correlation, none are call-independent. Call-independent identification has been developed for human speaker recognition, and this thesis aimed to: 1) determine if call-independent identification was possible in birds, using similar methods to those used for human speaker recognition, 2) examine the impact of noise in a recording on the identification accuracy and determine methods of removing the noise and increasing accuracy, 3) provide a comparison of features and classifiers to determine the best method of call-independent identification in birds, and 4) determine the practical limitations of call-independent identification in birds, with respect to increasing population size, changing vocal characteristics over time, using different call categories, and using the method in an open population. ... For classification, Gaussian mixture models and probabilistic neural networks resulted in higher accuracy, and were simpler to use, than multilayer perceptrons. Using the best methods of feature extraction and classification resulted in 86-95.5% identification accuracy for two passerine species, with all individuals correctly identified. A study of the limitations of the technique, in terms of population size, the category of call used, accuracy over time, and the effects of having an open population, found that acoustic identification using perceptual linear prediction and probabilistic neural networks can be used to successfully identify individuals in a population of at least 40 individuals, can be used successfully on call categories other than song, and can be used in open populations in which a new recording may belong to a previously unknown individual. However, identity was only able to be determined with accuracy for less than three months, limiting the current technique to short-term field studies. This thesis demonstrates the application of speaker recognition technology to enable call-independent identification in birds. Call-independence is a pre-requisite for the successful application of acoustic individual identification in many species, especially passerines, but has so far received little attention in the scientific literature. This thesis demonstrates that call-independent identification is possible in birds, as well as testing and finding methods to overcome the practical limitations of the methods, enabling their future use in biological studies, particularly for the conservation of threatened species.

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Weary, Daniel Martin. "Inter- and intra-specific recognition by song in the veery (Catharus fuscescens)." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=64479.

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Books on the topic "Bird recognition"

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How to know the birds: An introduction to bird recognition. New York: Gramercy Pub. Co., 1986.

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Fuchs, Roman, Petr Veselý, and Jana Nácarová. Predator Recognition in Birds. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12404-5.

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The easy way to bird recognition. Kingfisher Books, 1989.

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Easy Way to Bird Recognition (Larousse Easy Way Guides). Kingfisher Books Ltd, 1995.

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Martin, Graham R. The Sensory Ecology of Collisions and Entrapment. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199694532.003.0009.

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Collisions of birds with human artefacts (power lines, wind turbines, glass sheets, etc.) are major source of bird mortality. Many birds are also killed by entrapment in fishing nets. A sensory ecology perspective on this problem shows that collision and entrapment occur because these hazards present perceptual tasks that are beyond the capacities of the birds; birds are carrying out tasks where a hazard would not be predicted; or birds perceive the hazard but make an inappropriate categorical response. Birds that fly into power lines and turbines may be simply not looking ahead or are flying in conditions in which their resolution is very low. Reducing collisions requires far more than attempting to make hazards more conspicuous to humans. It requires recognition of the birds’ perceptual limitations and their distraction away from hazard sites. This requires taking account of the particular ecological requirements and sensory capacities of each target species.

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Olsen, Jerry. Australian High Country Owls. CSIRO Publishing, 2011. http://dx.doi.org/10.1071/9780643104105.

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Australian High Country Owls provides the latest scientific information on Australian owl species, especially Ninox owls. It details studies of Southern Boobooks and Powerful Owls, visits to North America and Europe to learn about owl research, and the resulting publications that overturned some existing beliefs about Australian owls. Ultimately, this led to the discovery of a new owl species in Indonesia, the Little Sumba Hawk-Owl. Appendices cover the biology, conservation and rehabilitation of Australian owls, including: field recognition, subspecies taxonomy, habitat, behaviour, food, range, migration, breeding, voice and calls, status and myths, questions about each species, and techniques for caring for injured and orphaned owls. The book includes numerous photographs of different owl species, and will be a handy reference for bird researchers and amateur bird watchers alike. 2012 Whitley Award Commendation for Vertebrate Natural History.

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Fuchs, Roman, Petr Veselý, and Jana Nácarová. Predator Recognition in Birds: The Use of Key Features. Springer, 2019.

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Grubb, T. G. Pattern recognition--a simple model for evaluating wildlife habitat. 1988.

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Martin, Graham R. Hearing and Olfaction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199694532.003.0003.

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Hearing and the sense of smell (olfaction) complement vision in gaining information about objects remote from the body. Hearing sensitivity in birds shows relatively little variation between species and sits well within the hearing capacities of young humans. Most birds have relatively poor ability to locate sounds in direction and distance. Only in owls does the accuracy of sound location match that of humans. A few highly specialized birds employ echolocation to orient themselves in the total darkness of caves. There is increasing evidence that olfaction is a key sense in birds guiding diverse behaviours across many species. Olfaction plays a key role in the location of profitable foraging locations at sea and on land, and in some species smell may be used to locate individual food items and nests. Olfaction may also play a role through semiochemicals in the recognition of species and individuals, and in mate choice.

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Criss, Amy H., and Marc W. Howard. Models of Episodic Memory. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.8.

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Episodic memory refers to memory for specific episodes from one’s life, such as working in the garden yesterday afternoon while enjoying the warm sun and chirping birds. In the laboratory, the study of episodic memory has been dominated by two tasks: single item recognition and recall. In single item recognition, participants are simply presented a cue and asked if they remember it appearing during the event in question (e.g., a specific flower from the garden) and in free recall they are asked to generate all aspects of the event. Models of episodic memory have focused on describing detailed patterns of performance in these and other laboratory tasks believed to be sensitive to episodic memory. This chapter reviews models with a focus on models of recognition with a specific emphasis on REM (Shiffrin & Steyvers, 1997) and models of recall with a focus on TCM (Howard & Kahana, 2002). We conclude that the current state of affairs, with no unified model of multiple memory tasks, is unsatisfactory and offer suggestions for addressing this gap.

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Book chapters on the topic "Bird recognition"

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Brandão, André, Pedro Pires, and Petia Georgieva. "Reinforcement Learning and Neuroevolution in Flappy Bird Game." In Pattern Recognition and Image Analysis, 225–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31332-6_20.

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Zhao, Yili, and Hua Zhou. "YNBIRDS: A System for Fine-Grained Bird Image Recognition." In Pattern Recognition and Computer Vision, 325–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31654-9_28.

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Reyes, Angie K., Juan C. Caicedo, and Jorge E. Camargo. "Identifying Colombian Bird Species from Audio Recordings." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 274–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52277-7_34.

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Mohanty, Ricky, Bandi Kumar Mallik, and Sandeep Singh Solanki. "Automatic Bird Species Recognition Based on Spiking Neural Network." In Nanoelectronics, Circuits and Communication Systems, 343–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2854-5_30.

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Bang, Arti V., and Priti P. Rege. "Automatic Recognition of Bird Species Using Human Factor Cepstral Coefficients." In Smart Computing and Informatics, 363–73. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5544-7_35.

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Zhi, Xuye, and Chengan Guo. "Bird Species Recognition Based on Deep Learning and Decision Fusion." In Advances in Neural Networks – ISNN 2018, 568–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92537-0_65.

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Ruiz-Muñoz, José Francisco, Mauricio Orozco-Alzate, and César Germán Castellanos-Domínguez. "Feature and Dissimilarity Representations for the Sound-Based Recognition of Bird Species." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 451–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_53.

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Kaplan, Gisela. "Visual Recognition in Birds." In Encyclopedia of Animal Cognition and Behavior, 1–5. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-47829-6_638-1.

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Vagg, R. S., K. A. Vickery, and P. A. Williams. "Development of Ruthenium Probes Designed to Bind Enantio- and Stereospecifically to DNA." In Molecular Recognition and Inclusion, 239–44. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5288-4_31.

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Machin, Karen L. "Recognition and Treatment of Pain in Birds." In Pain Management in Veterinary Practice, 407–15. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118999196.ch37.

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Conference papers on the topic "Bird recognition"

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Hodon, Michal, Peter Sarafin, and Peter Sevcik. "Monitoring and recognition of bird population in protected bird territory." In 2015 20th IEEE Symposium on Computers and Communication (ISCC). IEEE, 2015. http://dx.doi.org/10.1109/iscc.2015.7405516.

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Tahmoush, David. "Improved bird micro-doppler simulation for bird versus UAV recognition." In Radar Sensor Technology XXV, edited by Ann M. Raynal and Kenneth I. Ranney. SPIE, 2021. http://dx.doi.org/10.1117/12.2587214.

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Shim, Joo Yong, Joongheon Kim, and Jong-Kook Kim. "S2I-Bird: Sound-to-Image Generation of Bird Species using Generative Adversarial Networks." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412721.

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Wielgat, Robert, Tomasz P. Zielinski, Tomasz Potempa, Agnieszka Lisowska-Lis, and Daniel Krol. "HFCC based recognition of bird species." In 2007 Signal Processing Algorithms, Architectures, Arrangements, and Applications (SPA 2007). IEEE, 2007. http://dx.doi.org/10.1109/spa.2007.5903313.

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Incze, Agnes, Henrietta-Bernadett Jancso, Zoltan Szilagyi, Attila Farkas, and Csaba Sulyok. "Bird Sound Recognition Using a Convolutional Neural Network." In 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2018. http://dx.doi.org/10.1109/sisy.2018.8524677.

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Graciarena, Martin, Michelle Delplanche, Elizabeth Shriberg, and Andreas Stolcke. "Bird species recognition combining acoustic and sequence modeling." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946410.

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Graciarena, Martin, Michelle Delplanche, Elizabeth Shriberg, Andreas Stolcke, and Luciana Ferrer. "Acoustic front-end optimization for bird species recognition." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495923.

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Vilches, E., I. A. Escobar, E. E. Vallejo, and C. E. Taylor. "Data Mining Applied to Acoustic Bird Species Recognition." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.426.

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"BiRD'19: BiRD'19 - International Workshop on Behavior analysis and Recognition for knowledge Discovery - Program." In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2019. http://dx.doi.org/10.1109/percomw.2019.8730878.

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Pham, Tuan D. "Bird-Like Information Processing for AI-based Pattern Recognition." In 2013 12th Mexican International Conference on Artificial Intelligence (MICAI). IEEE, 2013. http://dx.doi.org/10.1109/micai.2013.27.

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