Journal ArticleDOI
Deep Learning Framework for Recognition of Cattle Using Muzzle Point Image Pattern
Santosh Kumar,Amit Kumar Pandey,K. Sai Ram Satwik,Sunil Kumar,Sanjay Singh,Amit Singh,Anand Mohan +6 more
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TLDR
The deep learning-based recognition system is proposed for identification of different cattle based on their primary muzzle point (nose pattern) image pattern characteristics to solve major problem of missed or swapped animal and false insurance claims.About:
This article is published in Measurement.The article was published on 2018-02-01. It has received 117 citations till now. The article focuses on the topics: Muzzle & Feature (machine learning).read more
Citations
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Journal ArticleDOI
A deep learning method with wrapper based feature extraction for wireless intrusion detection system
Sydney Mambwe Kasongo,Yanxia Sun +1 more
TL;DR: The results suggested that the proposed Feed-Forward Deep Neural Network (FFDNN) wireless IDS system using a Wrapper Based Feature Extraction Unit (WFEU) has greater detection accuracy than other approaches.
Journal ArticleDOI
A novel gas turbine fault diagnosis method based on transfer learning with CNN
Shi-sheng Zhong,Song Fu,Lin Lin +2 more
TL;DR: It is shown how feature representations learned with CNN on large-scale annotated gas turbine normal dataset can be efficiently transferred to fault diagnosis task with limited fault data.
Journal ArticleDOI
Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming
TL;DR: According to the experimental results, the proposed instance segmentation approach based on a Mask R-CNN deep learning framework can render fairly desirable cattle segmentation performance with 0.92 Mean Pixel Accuracy (MPA) and achieve contour extraction with an Average Distance Error (ADE) of 33.56 pixel, which is better than that of the state-of-the-art SharpMask and DeepMask instances segmentation methods.
Journal ArticleDOI
Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection
Asma Belhadi,Youcef Djenouri,Gautam Srivastava,Gautam Srivastava,Djamel Djenouri,Jerry Chun-Wei Lin,Giancarlo Fortino +6 more
TL;DR: A new model to identify collective abnormal human behaviors from large pedestrian data in smart cities is introduced and the results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance.
Journal ArticleDOI
Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning
TL;DR: The research trend of livestock behaviour recognition from four aspects is elaborate, i.e., development of robust livestock identification algorithms, recognition of livestock behaviours for different growth stages, further quantification of the results of behaviour recognition, and building evaluation system of growth status, health and welfare.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
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Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
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Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.