Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorit...
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Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next-generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence-to-sequence autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of convolutional Long Short-Term Memory AE (Conv-LSTMAE) is evaluated and compared with sequential convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv-LSTMAE-based AAD demonstrates better detection performance than CAE model-based AAD under different signal-to-noise ratio conditions of sound events such as explosion, fire and glass breaking.
Image restoration is an essential part in the field of computer vision, which aims at predicting and filling the pixels of the missing images to achieve satisfactory visual effects, it has extensive application value ...
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Image restoration is an essential part in the field of computer vision, which aims at predicting and filling the pixels of the missing images to achieve satisfactory visual effects, it has extensive application value in the fields of film and television special effects production,image editing, digital cultural heritage protection and virtual reality. With the introduction and application of the concept of deep learning in recent years, it has been widely studied in the academic and industrial fields, the performance of image restoration has been significantly improved, so that this long-standing research topic has once again aroused widespread concern and heated discussion on the social level. In order to enable more researchers to explore the theory of image restoration and its development, this paper reviews the related technologies in this field: firstly, the traditional image restoration methods are described, secondly, the background of deep learning is introduced, then the image restoration methods based on deep learning are described, subsequently, the several deep-learning based methods are compared and analyzed, finally, the future research direction and emphasis of image restoration are analyzed and prospected.
As teams of professional leagues are becoming more and more analytically driven, the interest in effective data management and access of sports plays has dramatically increased. In this article, we present a retrieval...
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As teams of professional leagues are becoming more and more analytically driven, the interest in effective data management and access of sports plays has dramatically increased. In this article, we present a retrieval system that can quickly find the most relevant plays from historical games given an input query. To search through a large number of games at an interactive speed, our system is built upon a distributed framework so that each query-result pair is evaluated in parallel. We also propose a pairwise learning to rank approach to improve search ranking based on users' clickthrough behavior. The similarity metric in training the rank function is based on automatically learnt features from a convolutional autoencoder. Finally, we showcase the efficacy of our learning to rank approach by demonstrating rank quality in a user study.
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by using interpolation techni...
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ISBN:
(纸本)9781450366151
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by using interpolation techniques before feeding them to deep learning models that are ImageNet compliant and accept only low-resolution images of size 224 x 224 px. This popular technique may lead to the loss of key information thus hampering the classification. Significant pathological features in medical images typically being small sized and highly affected. To combat this problem, we introduce a convolutional neural network (CNN) based classification approach which learns to reduce the resolution of the image using an autoencoder and at the same time classify it using another network, while both the tasks are trained jointly. This algorithm guides the model to learn essential representations from high-resolution images for classification along with reconstruction. We have used the publicly available dataset of chest x-rays to evaluate this approach and have outperformed state-of-the-art on test data. Besides, we have experimented with the effects of different augmentation approaches in this dataset and report baselines using some well known ImageNet class of CNNs.
Removal of cloud cover from remote sensing satellite images is crucial to many optical remote sensing image data users because cloud cover can conceal important spatial information on an image data. This underscores t...
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Removal of cloud cover from remote sensing satellite images is crucial to many optical remote sensing image data users because cloud cover can conceal important spatial information on an image data. This underscores the importance of making an informed choice in the selection of appropriate cloud cover detection and removal algorithms. For the purpose of large-scale training data, neural networks have been successful in many image processing tasks, but the use of neural networks to remove cloud occlusion in remote sensing imagery is still relatively evolving. The aim of this study is to evaluate the performance of two image restoration algorithms (The spatial attentive generative adversarial network and convolutional autoencoder with symmetrical skip connection) used for the removal of cloud cover on remote sensing images. An open-source RICE dataset was used for the training and prediction of the models as each of them were implemented for the cloud cover removal. The evaluation metrics used to compare the models' performance are the Structural similarity index ratio (SSIM), peak signal to noise ratio (PSNR), and the time taken for each model to complete its network training. After a successful completion of the network training using 80% of the data and the remaining 20% to test the networks, the spatial attentive generative adversarial network achieved the best performance on both the peak signal to noise ratio with a value of 26.3447 and the SSIM with a value of 0.8949 while the convolutional autoencoder generates a peak signal to noise ratio of 25.8257 and a SSIM of 0.6307. The result proves that SpaGAN is more effective for automatic cloud cover removal on remote sensing images and the improvement of the quality of the restored image when compared to CNN autoencoder.
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