Deep Image Similarity Measurement Based on the Improved Triplet Network with Spatial Pyramid Pooling

Yuan, Xinpan; Liu, Qunfeng; Long, Jun; Hu, Lei; Wang, Yulou

doi:10.3390/info10040129

Cited by 16 publications

(7 citation statements)

References 28 publications

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“…For the verification scenario a Siamese network [ 44 ] is adopted. Due to the nature of these networks, they are ideal for verification tasks of reduced dimensionality [ 45 ]. Siamese networks have mainly been used for face verification, with DeepFace [ 46 ] and FaceNet [ 47 ] being the most popular implementations.…”

Section: Methodsmentioning

confidence: 99%

Image-Based Somatotype as a Biometric Trait for Non-Collaborative Person Recognition at a Distance and On-The-Move

Danelakis

Theoharis

2020

Sensors

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It has recently been shown in Re-Identification (Re-ID) work that full-body images of people reveal their somatotype, even after change in apparel. A significant advantage of this biometric trait is that it can easily be captured, even at a distance, as a full-body image of a person, taken by a standard 2D camera. In this work, full-body image-based somatotype is investigated as a novel soft biometric feature for person recognition at a distance and on-the-move. The two common scenarios of (i) identification and (ii) verification are both studied and evaluated. To this end, two different deep networks have been recruited, one for the identification and one for the verification scenario. Experiments have been conducted on popular, publicly available datasets and the results indicate that somatotype can indeed be a valuable biometric trait for identity recognition at a distance and on-the-move (and hence also suitable for non-collaborative individuals) due to the ease of obtaining the required images. This soft biometric trait can be especially useful under a wider biometric fusion scheme.

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Section: Methodsmentioning

confidence: 99%

Image-Based Somatotype as a Biometric Trait for Non-Collaborative Person Recognition at a Distance and On-The-Move

Danelakis

Theoharis

2020

Sensors

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“…After the VAG model reconstructs the images x corresponding to the normal state of the input images, use the pixel-wise anomaly detection algorithm to calculate the pixel-level anomaly residual score Ars(i, j) between x and x. In this paper, we leverage the cosine distance [40] as the anomaly residual score which is defined in Equation (22), to calculate the difference for each pixel of both images.…”

Section: Online Anomaly Detectingmentioning

confidence: 99%

Application of Thermography and Adversarial Reconstruction Anomaly Detection in Power Cast-Resin Transformer

Fanchiang

Kuo

2022

Sensors

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Dry-type power transformers play a critical role in the power system. Detecting various overheating faults in the running state of the power transformer is necessary to avoid the collapse of the power system. In this paper, we propose a novel deep variational autoencoder-based anomaly detection method to recognize the overheating position in the operation of the dry-type transformer. Firstly, the thermal images of the transformer are acquired by the thermal camera and collected for training and testing datasets. Next, the variational autoencoder-based generative adversarial networks are trained to generate the normal images with different running conditions from heavy to light loading. Through the pixel-wise cosine difference between original and reconstructed images, the residual images with faulty features are obtained. Finally, we evaluate the trained model and anomaly detection method on normal and abnormal testing images to demonstrate the effeteness and performance of the proposed work. The results show that our method effectively improves the anomaly accuracy, AUROC, F1-scores and average precision, which is more effective than other anomaly detection methods. The proposed method is simple, lightweight and has less storage size. It reveals great advantages for practical applications.

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“…Multilayer Perceptron (MLP) used to be the best classifier to solve the classification problem. However, the performance of the MLP network is limited by the property of gradient explosion and diminishing, and the MLP network has been gradually replaced by deep learning models [16]. Convolutional Neural Networks (CNN) are one of the most popular architectures in deep learning, and achieve the best published results on benchmarks for object classification (such as NORB and CIFAR10) and handwritten digit recognition (MNIST).…”

Section: Reviewmentioning

confidence: 99%

Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

2020

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Silicon wafer is the most crucial material in the semiconductor manufacturing industry. Owing to limited resources, the reclamation of monitor and dummy wafers for reuse can dramatically lower the cost, and become a competitive edge in this industry. However, defects such as void, scratches, particles, and contamination are found on the surfaces of the reclaimed wafers. Most of the reclaimed wafers with the asymmetric distribution of the defects, known as the “good (G)” reclaimed wafers, can be re-polished if their defects are not irreversible and if their thicknesses are sufficient for re-polishing. Currently, the “no good (NG)” reclaimed wafers must be first screened by experienced human inspectors to determine their re-usability through defect mapping. This screening task is tedious, time-consuming, and unreliable. This study presents a deep-learning-based reclaimed wafers defect classification approach. Three neural networks, multilayer perceptron (MLP), convolutional neural network (CNN) and Residual Network (ResNet), are adopted and compared for classification. These networks analyze the pattern of defect mapping and determine not only the reclaimed wafers are suitable for re-polishing but also where the defect categories belong. The open source TensorFlow library was used to train the MLP, CNN, and ResNet networks using collected wafer images as input data. Based on the experimental results, we found that the system applying CNN networks with a proper design of kernels and structures gave fast and superior performance in identifying defective wafers owing to its deep learning capability, and the ResNet averagely exhibited excellent accuracy, while the large-scale MLP networks also acquired good results with proper network structures.

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Deep Image Similarity Measurement Based on the Improved Triplet Network with Spatial Pyramid Pooling

Cited by 16 publications

References 28 publications

Image-Based Somatotype as a Biometric Trait for Non-Collaborative Person Recognition at a Distance and On-The-Move

Image-Based Somatotype as a Biometric Trait for Non-Collaborative Person Recognition at a Distance and On-The-Move

Application of Thermography and Adversarial Reconstruction Anomaly Detection in Power Cast-Resin Transformer

Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

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