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Convolutional Neural Network Based on HOG Feature for Bird Species Detection and Classification
2021
Advances in Science, Technology and Engineering Systems
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Acknowledgment This work is funded by the division of Information and Communication Technology (ICT), Ministry of Posts, Telecommunications and Information Technology, the People's Republic of Bangladesh ...
The author's work is based on Haar-like and HOG features using LeNet Architecture for the detection of birds and classification of bird species [10] . ...
made based on Convolutional neural networks (CNN). ...
doi:10.25046/aj060285
fatcat:j6zlntj7afffrbjsssyri6gg44
Bird Detection using Siamese Neural Network
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The system can identify bird species in a large view of the image. The model will be trained using a convolutional neural network-based architecture called Siamese Network. ...
The proposed system uses deep neural networks for identifying bird species. The model will be trained on bird images that are coming in the endangered species category. ...
We might want to recognize SD Choudhury for animating our innovativeness and editing this paper to give his important sources of info. ...
doi:10.35940/ijitee.e2468.059720
fatcat:acrjynzvmrbatbwelawwvtjize
Deep learning–based automatic bird identification system for offshore wind farms
2020
Wind Energy
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Classification is based on these images, and it is implemented by convolutional neural network trained with a deep learning algorithm. ...
A prototype system has been built on Finnish west coast. In the proposed system, a separate radar system detects birds and provides WGS84 coordinates to a steering system of a camera. ...
;Writing-Review and Editing, J.T.T. ...
doi:10.1002/we.2492
fatcat:2jzh465hgbfmvk3ue6lvwz4fd4
Learning Features and Parts for Fine-Grained Recognition
2014
2014 22nd International Conference on Pattern Recognition
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The appearance descriptors are learned using a convolutional neural network. ...
This paper addresses the problem of fine-grained recognition: recognizing subordinate categories such as bird species, car models, or dog breeds. ...
ACKNOWLEDGMENTS This work was partially supported by an ONR MURI grant and the Yahoo! FREP program. ...
doi:10.1109/icpr.2014.15
dblp:conf/icpr/KrauseGDLF14
fatcat:mh7b3p4glzgjnkjdlyavbfd4xy
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
[article]
2014
arXiv
pre-print
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Our experiments advance state-of-the-art performance on bird species recognition, with a large improvement of correct classification rates over previous methods (75% vs. 55-65%). ...
We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification. ...
[17] extracted CNN features from part regions detected using a DPM, obtaining state-of-the-art results in bird species classification. ...
arXiv:1406.2952v1
fatcat:bsm6e2kay5eyrk5cpe226dmtpu
Part-based R-CNNs for Fine-grained Category Detection
[article]
2014
arXiv
pre-print
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We propose a model for fine-grained categorization that overcomes these limitations by leveraging deep convolutional features computed on bottom-up region proposals. ...
Experiments on the Caltech-UCSD bird dataset confirm that our method outperforms state-of-the-art fine-grained categorization methods in an end-to-end evaluation without requiring a bounding box at test ...
Acknowledgments This work was supported in part by DARPA Mind's Eye and MSEE programs, by NSF awards IIS-0905647, IIS-1134072, and IIS-1212798, and by support from Toyota. ...
arXiv:1407.3867v1
fatcat:ccnxfmoiufhs3nzzbvyujyofte
Part-Based R-CNNs for Fine-Grained Category Detection
[chapter]
2014
Lecture Notes in Computer Science
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We propose a model for fine-grained categorization that overcomes these limitations by leveraging deep convolutional features computed on bottom-up region proposals. ...
Experiments on the Caltech-UCSD bird dataset confirm that our method outperforms state-of-the-art fine-grained categorization methods in an end-to-end evaluation without requiring a bounding box at test ...
Acknowledgments This work was supported in part by DARPA Mind's Eye and MSEE programs, by NSF awards IIS-0905647, IIS-1134072, and IIS-1212798, and by support from Toyota. ...
doi:10.1007/978-3-319-10590-1_54
fatcat:mzjexmuuevg35bzfkqvji4sx3y
Convolutional Neural Network Model in Machine Learning Methods and Computer Vision for Image Recognition: A Review
2018
Journal of Applied Sciences Research
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Based on twenty five journal that have been review, this paper focusing on the development trend of convolution neural network (CNNs) model due to various learning method in image recognition since 2000s ...
Recently, Convolutional Neural Networks (CNNs) are used in variety of areas including image and pattern recognition, speech recognition, biometric embedded vision, food recognition and video analysis for ...
Only a year later, the deep learning technique based on convolutional neural network has achieved great performance improvement in large-scale image classification tasks and set off the upsurge of deep ...
doi:10.22587/jasr.2018.14.6.5
fatcat:stw6qs54szbkbozd2yy3edch6y
Fine-Grained Classification of Product Images Based on Convolutional Neural Networks
2018
Advances in Molecular Imaging
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The deep and shallow convolutional neural networks have data pre-processing, feature extraction and softmax classification. ...
This paper designs and trains a deep convolutional neural network that the convolution kernel size and the order of network connection are based on the high efficiency of the filter capacity and coverage ...
The training result based on the small samples and shallow convolutional neural network is often unsatisfactory. ...
doi:10.4236/ami.2018.84007
fatcat:oxq3572r2zg6boffledbxbhine
Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications
2020
Sensors
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In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. ...
The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. ...
is cost-effective, as a relatively small dataset is used. In the future, we have a plan to include the RCNN technique and wireless communication in the proposed model. ...
doi:10.3390/s20143923
pmid:32679644
fatcat:7xnpnlfg5fcpni7oy3wm6c67ba
An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning
2020
Iraqi Journal of Science
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In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). ...
Detection and classification of animals is a major challenge that is facing the researchers. ...
Estimating test animal images (bear, hog, fox, deer, and wolf) using the Convolutional Neural Network (CNN) [25] . ...
doi:10.24996/ijs.2020.61.9.23
fatcat:fjptsm3xhjh5hmvejp477gyqti
Actions and Attributes from Wholes and Parts
[article]
2015
arXiv
pre-print
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We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision. ...
For the tasks of action and attribute classification, we train holistic convolutional neural networks and show that adding parts leads to top-performing results for both tasks. ...
[5] tackle the problem of bird species categorization by first detecting bird parts with a HOG-DPM and then extracting CNN features from the aligned parts. ...
arXiv:1412.2604v2
fatcat:jmfzpjhrnfdhxgzdqeeyxpqtrm
Actions and Attributes from Wholes and Parts
2015
2015 IEEE International Conference on Computer Vision (ICCV)
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We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision. ...
For the tasks of action and attribute classification, we train holistic convolutional neural networks and show that adding parts leads to top-performing results for both tasks. ...
Acknowledgements This work was supported by the Intel Visual Computing Center and the ONR SMARTS MURI N000140911051. The GPUs used in this research were generously donated by the NVIDIA Corporation ...
doi:10.1109/iccv.2015.284
dblp:conf/iccv/GkioxariGM15a
fatcat:7vranzelszb53hrhm5qjnutmuu
Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier
2022
Journal of Food Quality
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In the first part, data augmentation is performed on the PlantVillage data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural ...
network (CNN). ...
It also has intermediate layers of dropout and max pooling [6] . (3) MobileNet: for use on mobile and embedded devices, this convolutional neural network is based on deep separable convolution operations ...
doi:10.1155/2022/2845320
fatcat:gbctc4qqajeehpeaviu753dd3m
Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach
[article]
2021
arXiv
pre-print
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Following the idea, in this paper, we present a neural network model called the Recurrent Correlational Network, where detection and tracking are jointly performed over a multi-frame representation learned ...
Furthermore, our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on a bird image dataset. ...
Our detection pipeline is based on region proposal and classification of the proposal, as in region-based CNNs [23] . ...
arXiv:2105.08253v1
fatcat:iyfjllni2rd3ljtz2yqrzj6hre
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