The coexistence of technologies, like big data application, cloud computing, and the numerous images in the Web has paved the need for new imageprocessing algorithms that exploit the processed image for diverse appli...
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Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted d...
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ISBN:
(纸本)9781450397612
Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. The effective analysis of a DCGAN in Graphic processing Unit and Tensor processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.
Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or col...
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ISBN:
(纸本)9781728163956
Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized subproblems. The subproblems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a ne...
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ISBN:
(纸本)9781665448994
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of imageprocessing technology, perceptual imageprocessing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual imageprocessing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.
The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemi...
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The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro imageprocessing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.
In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring metho...
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Detecting objects in the aerial-view scene is challenging for the objects usually have small scales relative to the image, making it hard to achieve high accuracy in full-image detection. Slice detection tries to over...
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ISBN:
(纸本)9781665475938
Detecting objects in the aerial-view scene is challenging for the objects usually have small scales relative to the image, making it hard to achieve high accuracy in full-image detection. Slice detection tries to overcome this by cutting the full image into slices before detecting them, but objects are sparsely distributed and usually clustered in local areas, a large number of background areas without objects can be ignored to improve detection efficiency. In this paper, we present PickDet, a framework for efficient and effective object detection in the aerial-view scene, which only chooses slices containing objects to conduct detection. The key components of PickDet include a lightweight convolutional network (PickNet), a screening strategy (SoftPick), and fine-tuned detectors. Given slices of aerial-view images, PickNet first outputs the probability of object existence. Then SoftPick conducts a double-threshold screening strategy to pick the slices which contain objects. Finally, all picked slices are fed into the detector in parallel and full-image detection is used as an auxiliary mean. Compared with previous methods, PickDet achieves higher accuracy and more efficiency in the aerial-view scene. We evaluate PickDet on Visdrone and Oiltank datasets, experiments show that PickDet can result in up to 28.0% AP improvement compared to full-image detection, and can result in up to 2.9% AP increase and up to 5 times inference speedup compared to slice detection.
A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were us...
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ISBN:
(纸本)9781728175652
A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image files were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed arc taken into account;and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.
With the increased usage of edge devices having local computation capabilities, deep neural network (DNN) training in a network of edge devices becomes promising. Several recent works have proposed fully edge-based di...
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ISBN:
(纸本)9781665480468
With the increased usage of edge devices having local computation capabilities, deep neural network (DNN) training in a network of edge devices becomes promising. Several recent works have proposed fully edge-based distributed training systems for situations when the communication to cloud is unstable or intermittent. However, such distributed systems become vulnerable when there are untrusted devices that launch data and model poisoning attacks during training, deteriorating the accuracy of the DNN model. To handle this challenge, we propose a Trustworthy distributed system for Machine learning training in an edge device network (TrustMe). TrustMe realizes both data and model parallelisms. It detects the untrusted devices producing illegitimate outputs. Next, it reassigns the training tasks of the untrusted devices to other trusted devices in such a way that the reassignment and the training that is restarted after the reassignment require minimal time. Our container-based emulation and real device experiments demonstrate that TrustMe achieves up to 12% higher accuracy and 45% less training time compared to existing methods in the presence of untrusted devices.
Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large sc...
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Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large scale computational resources. These new methods need to manage Tbytes or Pbytes of data with large-scale structural and functional relationships, TFlops or PFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analyzing data. Today, many areas in Life Sciences are facing these challenges. This special issue contains papers showing existing solutions and latest developments in Life Sciences and Computing Sciences to collaboratively explore new ideas and approaches to successfully apply distributed IT-systems in translational research, clinical intervention, and decision-making. (C) 2020 Published by Elsevier B.V.
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