Journal articles: 'Bird recognition'

1

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

Zaugg, Serge, Gilbert Saporta, Emiel van Loon, Heiko Schmaljohann, and Felix Liechti. "Automatic identification of bird targets with radar via patterns produced by wing flapping." Journal of The Royal Society Interface 5, no. 26 (March 10, 2008): 1041–53. http://dx.doi.org/10.1098/rsif.2007.1349.

Full text

Abstract:

Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.

APA, Harvard, Vancouver, ISO, and other styles

12

Guo, Bin, Wenjia Du, Lan Cheng, Jing Liang, and Lu Wang. "Application of artificial intelligence bird recognition technology in airport bird strike prevention safety management." IOP Conference Series: Earth and Environmental Science 565 (October 1, 2020): 012092. http://dx.doi.org/10.1088/1755-1315/565/1/012092.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Esmail, Hanif, and Emma Aarons. "Management of Suspected Avian (H5N1) Influenza in a Non-pandemic Setting." Acute Medicine Journal 6, no. 1 (January 1, 2007): 9–13. http://dx.doi.org/10.52964/amja.0146.

Full text

Abstract:

Avian (H5N1) influenza has been responsible for millions of wild bird and poultry deaths throughout the world. Sporadic human cases with a high mortality have occurred, almost exclusively in association with very close contact with sick, dying or dead birds. Appropriate management of suspected cases requires their prompt recognition via attention to travel and bird-exposure history. The early isolation, diagnosis and treatment of suspected cases as well as prompt involvement of the health protection unit should enable patients to be optimally managed with minimum risk to health care staff.

APA, Harvard, Vancouver, ISO, and other styles

14

Rajchard, J. "Ultraviolet (UV) light perception by birds: a review." Veterinární Medicína 54, No. 8 (September 22, 2009): 351–59. http://dx.doi.org/10.17221/110/2009-vetmed.

Full text

Abstract:

The ability to perceive the near ultraviolet part of the light spectrum (the wavelength 320–400 nm) has been detected in many bird species. This ability is an important bird sense. The ecological importance of UV perception has been studied mainly in the context of intra- and inter-sexual signalling, common species communication and also in foraging. Some birds of prey use UV reflectance in their feeding strategy: e.g., the kestrel (Falco tinnunculus), but also other birds of prey are able to recognize the presence of voles by perceiving the UV reflectance of their scent urine marks. The ability to detect the presence of prey is a common feature of birds with analogous feeding spectra in taxonomically distinct species. UV perception and its use in foraging have also been proved in predominantly herbivorous bird species. This ability is possessed both by bird species living in northern habitats and others living in tropical forests. The signalling and communication role of the UV perception is very important. The plumage of many bird species shows specific colour features – e.g., sexually different regions in plumage coloration unnoticed by the human eye. Also other body parts can have similar features – e.g., supra-orbital combs in the red grouse (Lagopus lagopus scoticus). All these characteristics are important primarily in the mate-choice decision. Birds apparently also use their ability of UV perception for recognition of their own eggs. Some bird species are able to modify plumage UV reflectance by uropygial secretions. The knowledge of all specific aspects of bird physiology can significantly help both breeders of various bird species and facilitate effective veterinary care.

APA, Harvard, Vancouver, ISO, and other styles

15

Wolfgang, Andrew, and Aaron Haines. "Testing Automated Call-Recognition Software for Winter Bird Vocalizations." Northeastern Naturalist 23, no. 2 (June 2016): 249–58. http://dx.doi.org/10.1656/045.023.0206.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Zhao, Yili, and Dan Xu. "Joint Semantic Parts for Fine-Grained Bird Images Recognition." Journal of Computer-Aided Design & Computer Graphics 30, no. 8 (2018): 1522. http://dx.doi.org/10.3724/sp.j.1089.2018.16781.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Nadimpalli, U. D., R. R. Price, S. G. Hall, and P. Bomma. "A Comparison of Image Processing Techniques for Bird Recognition." Biotechnology Progress 22, no. 1 (February 3, 2006): 9–13. http://dx.doi.org/10.1021/bp0500922.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Lattore, Martina, Shinichi Nakagawa, Terry Burke, Mireia Plaza, and Julia Schroeder. "No evidence for kin recognition in a passerine bird." PLOS ONE 14, no. 10 (October 23, 2019): e0213486. http://dx.doi.org/10.1371/journal.pone.0213486.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Qian, Kun, Jian Guo, Ken Ishida, and Satoshi Matsuoka. "Fast recognition of bird sounds using extreme learning machines." IEEJ Transactions on Electrical and Electronic Engineering 12, no. 2 (December 15, 2016): 294–96. http://dx.doi.org/10.1002/tee.22378.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Somervuo, P., A. Harma, and S. Fagerlund. "Parametric Representations of Bird Sounds for Automatic Species Recognition." IEEE Transactions on Audio, Speech and Language Processing 14, no. 6 (November 2006): 2252–63. http://dx.doi.org/10.1109/tasl.2006.872624.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

McGregor, Peter K. "Bird song and kin recognition: potential, constraints and evidence." Ethology Ecology & Evolution 1, no. 1 (May 1989): 123–33. http://dx.doi.org/10.1080/08927014.1989.9525536.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Dong, Xueyan, and Jingpeng Jia. "Advances in Automatic Bird Species Recognition from Environmental Audio." Journal of Physics: Conference Series 1544 (May 2020): 012110. http://dx.doi.org/10.1088/1742-6596/1544/1/012110.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Huang, Yo-Ping, and Haobijam Basanta. "Recognition of Endemic Bird Species Using Deep Learning Models." IEEE Access 9 (2021): 102975–84. http://dx.doi.org/10.1109/access.2021.3098532.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Chen, W. S., J. Liu, and J. Li. "Classification of UAV and bird target in low-altitude airspace with surveillance radar data." Aeronautical Journal 123, no. 1260 (February 2019): 191–211. http://dx.doi.org/10.1017/aer.2018.158.

Full text

Abstract:

ABSTRACTIn order to ensure low-altitude safety, a tracking and recognition method of unmanned aerial vehicle (UAV) and bird targets based on traditional surveillance radar data is proposed. First, several motion models for UAV and flying bird targets are established. Second, the target trajectories are filtered and smoothed with multiple motion models. Third, by calculating the time-domain variance of the model occurrence probability, the model conversion probability of the target is estimated, and then the target type is identified and classified. The effectiveness and robustness of the algorithm is demonstrated by several groups of Monte Carlo simulation experiments, including setting different recognition steps, different model transformation probability, filtering and smoothing algorithm comparison. The algorithm is also successfully applied on the ground-truth radar data collected by the low-altitude surveillance radar at airport and coastal environments, where the targets of UAVs and flying birds could be tracked and recognised.

APA, Harvard, Vancouver, ISO, and other styles

25

Mohanty, Ricky, Subhendu Kumar Pani, and Ahmad Taher Azar. "Recognition of Livestock Disease Using Adaptive Neuro-Fuzzy Inference System." International Journal of Sociotechnology and Knowledge Development 13, no. 4 (October 2021): 101–18. http://dx.doi.org/10.4018/ijskd.2021100107.

Full text

Abstract:

The livestock health management system is based on the principal concept to investigate bird health status by collecting biological traits like their sound utterance. This theme is implemented on four different species of livestock to cure them of bronchitis disease. This paper includes the audio features of both healthy and unhealthy livestock. Particularly, the secure audio-wellbeing features are incorporated into the platform to spontaneously examine and conclude using livestock voice information to recognize diseased birds. One month of long-term recognition experimental studies has been conducted where the recognition accuracy of the set of diseased birds was about 99% using adaptive neuro-fuzzy inference system (ANFIS). This recognition accuracy of ANFIS in this regard is better than the performance of an artificial neural network. This is a reliable way for researchers to investigate and constitute evidence of disease curability or eradication of incurable ones.

APA, Harvard, Vancouver, ISO, and other styles

26

Price, Trevor. "Sexual selection and natural selection in bird speciation." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1366 (February 28, 1998): 251–60. http://dx.doi.org/10.1098/rstb.1998.0207.

Full text

Abstract:

The role of sexual selection in speciation is investigated, addressing two main issues. First, how do sexually selected traits become species recognition traits? Theory and empirical evidence suggest that female preferences often do not evolve as a correlated response to evolution of male traits. This implies that, contrary to runaway (Fisherian) models of sexual selection, premating isolation will not arise as an automatic side effect of divergence between populations in sexually selected traits. I evaluate premating isolating mechanisms in one group, the birds. In this group premating isolation is often a consequence of sexual imprinting, whereby young birds learn features of their parents and use these features in mate choice. Song, morphology and plumage are known recognition cues. I conclude that perhaps the main role for sexual selection in speciation is in generating differences between populations in traits. Sexual imprinting then leads to these traits being used as species recognition mechanisms. The second issue addressed in this paper is the role of sexual selection in adaptive radiation, again concentrating on birds. Ecological differences between species include large differences in size, which may in themselves be sufficient for species recognition, and differences in habitat, which seem to evolve frequently and at all stages of an adaptive radiation. Differences in habitat often cause song and plumage patterns to evolve as a result of sexual selection for efficient communication. Therefore sexual selection is likely to have an important role in generating premating isolating mechanisms throughout an adaptive radiation. It is also possible that sexual selection, by creating more allopatric species, creates more opportunity for ecological divergence to occur. The limited available evidence does not support this idea. A role for sexual selection in accelerating ecological diversification has yet to be demonstrated.

APA, Harvard, Vancouver, ISO, and other styles

27

Woinarski, J. C. Z., A. Fisher, M. Armstrong, K. Brennan, A. D. Griffiths, B. Hill, J. Low Choy, et al. "Monitoring indicates greater resilience for birds than for mammals in Kakadu National Park, northern Australia." Wildlife Research 39, no. 5 (2012): 397. http://dx.doi.org/10.1071/wr11213.

Full text

Abstract:

Context A previous study reported major declines for native mammal species from Kakadu National Park, over the period 2001–09. The extent to which this result may be symptomatic of more pervasive biodiversity decline was unknown. Aims Our primary aim was to describe trends in the abundance of birds in Kakadu over the period 2001–09. We assessed whether any change in bird abundance was related to the arrival of invading cane toads (Rhinella marina), and to fire regimes. Methods Birds were monitored at 136 1-ha plots in Kakadu, during the period 2001–04 and again in 2007–09. This program complemented sampling of the same plots over the same period for native mammals. Key results In contrast to the decline reported for native mammals, the richness and total abundance of birds increased over this period, and far more individual bird species increased than decreased. Fire history in the between-sampling period had little influence on trends for individual species. Interpretation of the overall positive trends for bird species in Kakadu over this period should be tempered by recognition that most of the threatened bird species present in Kakadu were unrecorded in this monitoring program, and the two threatened species for which there were sufficient records to assess trends – partridge pigeon (Geophaps smithii) and white-throated grass-wren (Amytornis woodwardi) – both declined significantly. Conclusions The current decline of the mammal fauna in this region is not reflected in trends for the region’s bird fauna. Some of the observed changes (mostly increases) in the abundance of bird species may be due to the arrival of cane toads, and some may be due to local or regional-scale climatic variation or variation in the amount of flowering. The present study provides no assurance about threatened bird species, given that most were inadequately recorded in the study (perhaps because their decline pre-dated the present study). Implications These contrasting trends between mammals and birds demonstrate the need for biodiversity monitoring programs to be broadly based. The declines of two threatened bird species over this period indicate the need for more management focus for these species.

APA, Harvard, Vancouver, ISO, and other styles

28

ZHANG, Xiaoxia, and Ying LI. "Bird sounds recognition based on energy detection in complex environments." Journal of Computer Applications 33, no. 10 (November 11, 2013): 2945–49. http://dx.doi.org/10.3724/sp.j.1087.2013.02945.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

McLean, Ian G., Corinna Hölzer, and Belinda J. S. Studholme. "Teaching predator-recognition to a naive bird: implications for management." Biological Conservation 87, no. 1 (January 1999): 123–30. http://dx.doi.org/10.1016/s0006-3207(98)00024-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Zhu, Leqing, Yaoyao Lv, Daxing Zhang, Yadong Zhou, Guoli Yan, Huiyan Wang, and Xun Wang. "Fine-grained bird recognition by using contour-based pose transfer." Optical Engineering 54, no. 10 (October 13, 2015): 103105. http://dx.doi.org/10.1117/1.oe.54.10.103105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Jancovic, Peter, and Munevver Kokuer. "Bird Species Recognition Using Unsupervised Modeling of Individual Vocalization Elements." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 5 (May 2019): 932–47. http://dx.doi.org/10.1109/taslp.2019.2904790.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

北風, 裕教, 蓮人吉原, 蒼太岡部, and 遼. 松村. "Development of Harmful Bird Recognition System using Object Detection YOLO." 産業応用工学会論文誌 8, no. 1 (2020): 10–16. http://dx.doi.org/10.12792/jjiiae.8.1.10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Huang, Yo-Ping, and Haobijam Basanta. "Bird Image Retrieval and Recognition Using a Deep Learning Platform." IEEE Access 7 (2019): 66980–89. http://dx.doi.org/10.1109/access.2019.2918274.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Hauber, M. E., S. A. Russo, and P. W. Sherman. "A password for species recognition in a brood-parasitic bird." Proceedings of the Royal Society of London. Series B: Biological Sciences 268, no. 1471 (May 22, 2001): 1041–48. http://dx.doi.org/10.1098/rspb.2001.1617.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Pahuja, Roop, and Avijeet Kumar. "Sound-spectrogram based automatic bird species recognition using MLP classifier." Applied Acoustics 180 (September 2021): 108077. http://dx.doi.org/10.1016/j.apacoust.2021.108077.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Kondo, Noriko, Ei-Ichi Izawa, and Shigeru Watanabe. "Crows cross-modally recognize group members but not non-group members." Proceedings of the Royal Society B: Biological Sciences 279, no. 1735 (January 4, 2012): 1937–42. http://dx.doi.org/10.1098/rspb.2011.2419.

Full text

Abstract:

Recognizing other individuals by integrating different sensory modalities is a crucial ability of social animals, including humans. Although cross-modal individual recognition has been demonstrated in mammals, the extent of its use by birds remains unknown. Herein, we report the first evidence of cross-modal recognition of group members by a highly social bird, the large-billed crow ( Corvus macrorhynchos ). A cross-modal expectancy violation paradigm was used to test whether crows were sensitive to identity congruence between visual presentation of a group member and the subsequent playback of a contact call. Crows looked more rapidly and for a longer duration when the visual and auditory stimuli were incongruent than when congruent. Moreover, these responses were not observed with non-group member stimuli. These results indicate that crows spontaneously associate visual and auditory information of group members but not of non-group members, which is a demonstration of cross-modal audiovisual recognition of group members in birds.

APA, Harvard, Vancouver, ISO, and other styles

37

Stamp Dawkins, Marian. "Distance and Social Recognition in Hens: Implications for the Use of Photographs as Social Stimuli." Behaviour 133, no. 9-10 (1996): 663–80. http://dx.doi.org/10.1163/156853996x00413.

Full text

Abstract:

AbstractThe role of close (less than 30 cm) examination or scrutiny of other birds in social recognition was confirmed by showing that before approaching a hen whose identity was unknown, hens behaved similarly whether that bird was familiar or unfamiliar to them, but after examining the other hen they temporarily kept further away from unfamiliar hens (Expt 1). Hens chose flock-mates rather than unfamiliar birds as feeding companions even when all that was visible of the target birds were their heads and necks, confirming the role of these regions in recognition (Expt 2). They showed this preference for familiar birds whether they could see either the front or the side views of the heads of the target birds but attempts to confirm this result with photographs rather than live birds resulted in no significant preference being shown (Expts 3 and 4). The reasons for this lack of transfer from live birds to photographs and its implications for presenting artificial visual stimuli such as photographs and video to hens are discussed.

APA, Harvard, Vancouver, ISO, and other styles

38

Nijman, Vincent, Marco Campera, Ahmad Ardiansyah, Michela Balestri, Hani R. El Bizri, Budiadi Budiadi, Tungga Dewi, et al. "Large-Scale Trade in a Songbird That Is Extinct in the Wild." Diversity 13, no. 6 (May 30, 2021): 238. http://dx.doi.org/10.3390/d13060238.

Full text

Abstract:

Indonesia is at the epicenter of the Asian Songbird Crisis, i.e., the recognition that the cage bird trade has a devastating impact on numerous imperiled bird species in Asia. The Javan pied starling Gracupica jalla, only in the last five years recognized as distinct from the pied starlings of mainland Southeast Asia, has been declared extinct the wild in 2021. Up until the 1980s, it used to be one of the most common open countryside birds on the islands of Java and Bali, Indonesia. From the early 2000s onwards, the species is commercially bred to meet the demand from the domestic cagebird trade. We conducted 280 market surveys in 25 bird markets in Java and Bali between April 2014 and March 2020, with 15 markets being surveyed at least six times. We recorded 24,358 Javan pied starlings, making it one of the most commonly observed birds in the markets. We established that, conservatively, around 40% of the birds in the market were sold within one week and used this to estimate that at a minimum ~80,000 Javan pied starlings are sold in the bird markets on Java and Bali. The latter represents a monetary value of USD5.2 million. We showed that prices were low in the 1980s, when all birds were sourced from the wild. It became more varied and differentiated in the 2000s when a combination of now expensive wild-caught and cheaper captive-bred birds were offered for sale, and prices stabilized in the 2010s when most, if not all birds were commercially captive-bred. Javan pied starlings are not protected under Indonesian law, and there are no linked-up conservation efforts in place to re-establish a wild population on the islands, although small-scale releases do take place.

APA, Harvard, Vancouver, ISO, and other styles

39

北風, 裕教, 蒼太岡部, 蓮人吉原, and 遼. 松村. "Recognittion Rate Improvement of Injurious Bird Recognition System by Increasing CNN Learning Image using Data Augmentation." 産業応用工学会論文誌 7, no. 2 (2019): 69–76. http://dx.doi.org/10.12792/jjiiae.7.2.69.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Policht, Richard, Vlastimil Hart, Denis Goncharov, Peter Surový, Vladimír Hanzal, Jaroslav Červený, and Hynek Burda. "Vocal recognition of a nest-predator in black grouse." PeerJ 7 (March 15, 2019): e6533. http://dx.doi.org/10.7717/peerj.6533.

Full text

Abstract:

Corvids count among the important predators of bird nests. They are vocal animals and one can expect that birds threatened by their predation, such as black grouse, are sensitive to and recognize their calls. Within the framework of field studies, we noticed that adult black grouse were alerted by raven calls during periods outside the breeding season. Since black grouse are large, extremely precocial birds, this reaction can hardly be explained by sensitization specifically to the threat of nest predation by ravens. This surprising observation prompted us to study the phenomenon more systematically. According to our knowledge, the response of birds to corvid vocalization has been studied in altricial birds only. We tested whether the black grouse distinguishes and responds specifically to playback calls of the common raven. Black grouse recognized raven calls and were alerted, displaying typical neck stretching, followed by head scanning, and eventual escape. Surprisingly, males tended to react faster and exhibited a longer duration of vigilance behavior compared to females. Although raven calls are recognized by adult black grouse out of the nesting period, they are not directly endangered by the raven. We speculate that the responsiveness of adult grouse to raven calls might be explained as a learned response in juveniles from nesting hens that is then preserved in adults, or by a known association between the raven and the red fox. In that case, calls of the raven would be rather interpreted as a warning signal of probable proximity of the red fox.

APA, Harvard, Vancouver, ISO, and other styles

41

Batistela, Marciela, and Eliara Solange Müller. "Analysis of duet vocalizations in Myiothlypis leucoblephara (Aves, Parulidae)." Neotropical Biology and Conservation 14, no. 2 (August 13, 2019): 297–311. http://dx.doi.org/10.3897/neotropical.14.e37655.

Full text

Abstract:

Bird vocalizations might be used for specific recognition, territorial defense, and reproduction. Bioacoustic studies aim to understand the production, propagation and reception of acoustic signals, and they are an important component of research on animal behavior and evolution. In this study we analyzed the sound structure of duet vocalizations in pairs of Myiothlypis leucoblephara and evaluated whether the vocal variables differ among pairs and if there are differences in temporal characteristics and frequency of duets between pairs in forest edges vs. forest interior. Vocalizations were recorded from 17 bird pairs in three remnants of Atlantic Forest in southern Brazil. Six of the bird pairs were situated at the edge of the forest remnant, and 11 were in the interior of the remnant. The duets of different pairs between forest areas showed descriptive differences in the frequency, number of notes per call, and time between issuance of calls, with the main distinguishing feature being a change in frequency of a few notes in the second part of the musical phrase. The minimum frequency of vocalization was reduced at the private area than in the other two remnants (p <0.05). The duets of birds in the forest edge and forest interior did not significantly differ in minimum or maximum frequency of phrases (p> 0.05), phrase duration (p> 0.05) or number of notes per phrase (p> 0.05). Myiothlypis leucoblephara did not show a specific pattern with respect to issue of phrases in duets, but instead showed five different patterns, which were variable among pairs. There was a sharp decline or alternation in frequency between notes in the second part of the musical phrase for recognition among pairs. Variation in vocalization among M. leucoblephara duets may play a role in pair recognition.

APA, Harvard, Vancouver, ISO, and other styles

42

Lin, Hongji, Han Lin, and Weibin Chen. "Study on Recognition of Bird Species in Minjiang River Estuary Wetland." Procedia Environmental Sciences 10 (2011): 2478–83. http://dx.doi.org/10.1016/j.proenv.2011.09.386.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Stowell, Dan, Emmanouil Benetos, and Lisa F. Gill. "On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 6 (June 2017): 1193–206. http://dx.doi.org/10.1109/taslp.2017.2690565.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Briess, K., H. Jahn, E. Lorenz, D. Oertel, W. Skrbek, and B. Zhukov. "Fire recognition potential of the bi-spectral Infrared Detection (BIRD) satellite." International Journal of Remote Sensing 24, no. 4 (January 2003): 865–72. http://dx.doi.org/10.1080/01431160210154010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Mohanty, Ricky, Bandi Kumar Mallik, and Sandeep Singh Solanki. "Automatic bird species recognition system using neural network based on spike." Applied Acoustics 161 (April 2020): 107177. http://dx.doi.org/10.1016/j.apacoust.2019.107177.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Boulmaiz, Amira, Djemil Messadeg, Noureddine Doghmane, and Abdelmalik Taleb-Ahmed. "Robust acoustic bird recognition for habitat monitoring with wireless sensor networks." International Journal of Speech Technology 19, no. 3 (July 27, 2016): 631–45. http://dx.doi.org/10.1007/s10772-016-9354-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Osadchyi, V. V., V. S. Yeremeev, A. V. Matsyura, and K. Jankowski. "Cluster analysis, fuzzy sets, and fuzzy logic models in bird identification." Ukrainian Journal of Ecology 7, no. 2 (May 22, 2017): 96–103. http://dx.doi.org/10.15421/2017_25.

Full text

Abstract:

In our resent research (Osadchiy at al., 2016) we considered the mathematical model for the identifying of bird species according to the results of inaccurate field measurements. We used the total length of the bird, the wingspan, the wingbeat frequency, and the flight as the input factors of the model. Testing the model on a hypothetical case of identifying some target species, like Rook, Common raven, Mallard, White Stork, and Lapwing revealed that this model can be used for bird species identification with definite limitations. However, in previous model we applied the recognition algorithm that was based on the classical sections of mathematical statistics. The limitations of those model are obvious - it does not take into account many characteristics and behavioral features of birds that cannot be represented in numerical form, like diurnal activity pattern and flocking behavior. In this case the possibility of using the traditional sections of mathematical statistics is quite limited. The present study is devoted to the development of a mathematical method for the identifying of the bird species that based on cluster analysis with fuzzy logic and fuzzy sets which extends the possibilities of the algorithm that was previously proposed in our research.

APA, Harvard, Vancouver, ISO, and other styles

48

Soni, Neha, Enakshi Khular Sharma, and Amita Kapoor. "Novel BSSSO-Based Deep Convolutional Neural Network for Face Recognition with Multiple Disturbing Environments." Electronics 10, no. 5 (March 8, 2021): 626. http://dx.doi.org/10.3390/electronics10050626.

Full text

Abstract:

Face recognition technology is presenting exciting opportunities, but its performance gets degraded because of several factors, like pose variation, partial occlusion, expression, illumination, biased data, etc. This paper proposes a novel bird search-based shuffled shepherd optimization algorithm (BSSSO), a meta-heuristic technique motivated by the intuition of animals and the social behavior of birds, for improving the performance of face recognition. The main intention behind the research is to establish an optimization-driven deep learning approach for recognizing face images with multiple disturbing environments. The developed model undergoes three main steps, namely, (a) Noise Removal, (b) Feature Extraction, and (c) Recognition. For the removal of noise, a type II fuzzy system and cuckoo search optimization algorithm (T2FCS) is used. The feature extraction is carried out using the CNN, and landmark enabled 3D morphable model (L3DMM) is utilized to efficiently fit a 3D face from a single uncontrolled image. The obtained features are subjected to Deep CNN for face recognition, wherein the training is performed using novel BSSSO. The experimental findings on standard datasets (LFW, UMB-DB, Extended Yale B database) prove the ability of the proposed model over the existing face recognition approaches.

APA, Harvard, Vancouver, ISO, and other styles

49

Zhang, Yao-Hua, Yu-Feng Du, and Jian-Xu Zhang. "Uropygial gland volatiles facilitate species recognition between two sympatric sibling bird species." Behavioral Ecology 24, no. 6 (2013): 1271–78. http://dx.doi.org/10.1093/beheco/art068.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Beecher, Michael D., Patricia Loesche, Philip K. Stoddard, and S. Elizabeth Campbell. "Memory Does Not Constrain Individual Recognition in a Bird With Song Repertoires." Behaviour 122, no. 3-4 (1992): 274–87. http://dx.doi.org/10.1163/156853992x00543.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Bird recognition'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles