作者：
Karandashev, IakovKryzhanovsky, BorisRAS
Sci Res Inst Syst Anal Ctr Opt Neural Technol Nakhimovskiy Prosp 36B1 Moscow 117218 Russia RUDN Univ
Peoples Friendship Univ Russia 6 Miklukho Maklaya St Moscow 117198 Russia
In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the he...
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ISBN:
(纸本)9783030202576;9783030202569
In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the help of exact polynomial Schraudolph-Kamenetsky algorithm, we examined the global minimum depth in 2D EA-type models. We found a dependence of the global minimum depth on the dimension of the problem N and obtained its asymptotic value in the limit N -> infinity. We believe these evaluations can be further used for examining the behavior of 2D Bayesian models often used in machinelearning and imageprocessing.
Face recognition has long been a hot topic and challenging research point in areas such as imageprocessing, patternrecognition, and machine vision. The face is a biometric feature with the intrinsic nature of a huma...
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The prediction of failures in rotating machines is an important issue in industries to improve safety, to reduce the cost of maintenance and to prevent accidents. In this paper a predictive maintenance algorithm, base...
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The recent progress of computing, machinelearning, and especially deep learning, for imagerecognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here effic...
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Extreme learningmachine (ELM) is most popular emerging learning algorithm that modify classical ‘Generalized’ single hidden layer feed forward network. Though some traditional gradient based learning algorithm like...
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In this paper, we propose an efficient way to detect objects in 360 degrees videos in order to boost the performance of tracking on the same. Though extensive work has been done in the field of 2D video processing, th...
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ISBN:
(纸本)9783030348724;9783030348717
In this paper, we propose an efficient way to detect objects in 360 degrees videos in order to boost the performance of tracking on the same. Though extensive work has been done in the field of 2D video processing, the domain of 360. video processing has not been explored much yet, as it poses difficulties such as (1) unavailability of the annotated dataset (2) severe geometric distortions at panoramic poles of the image and (3) high resolution of the media which requires high computation capable machinery. The state-of-the-art detection algorithm involves the use of CNN (Convolution Neural Networks) trained on a large dataset. Faster RCNN, SSD, YOLO, YOLO9000, YOLOv3 etc. are some of the detection algorithms that use CNN. Among these, though YOLOv3 might not be the most accurate, it is the fastest, and this trade-off between speed and accuracy is acceptable. We improvise upon this algorithm, to make it suitable for the 360 degrees dataset. We propose YOLO360, a CNN network to detect objects in 360 degrees videos and thus increase the tracking precision and accuracy. This is achieved by performing transfer learning on YOLOv3 with the manually annotated dataset.
Face recognition has always been an active research area with several applications, such as security access control, human-machine interface and gender classification. More often, in real world, grayscale images have ...
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With the rapid growth in retail e-commerce industry, most of the traditional in-store retailers are focusing more on online and mobile channels. To stay competitive, retailers need quality metadata and powerful search...
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Customers should have several benchmarks to buy banana from the market. One of them is observing each size to its ripeness. This study present a framework for determining bananas based on types and levels of ripeness ...
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Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e.g., imagerecognition) and natural language processing (e.g., text classification). One of the benefits o...
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