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pro vyhledávání: '"Tay, Yong Haur"'
Publikováno v:
In Engineering Applications of Artificial Intelligence November 2023 126 Part C
Wood Identification has never been more important to serve the purpose of global forest species protection and timber regulation. Macroscopic level wood identification practiced by wood anatomists can identify wood up to genus level. This is sufficie
Externí odkaz:
http://arxiv.org/abs/1709.08154
In this paper we propose a supervised learning system for counting and localizing palm trees in high-resolution, panchromatic satellite imagery (40cm/pixel to 1.5m/pixel). A convolutional neural network classifier trained on a set of palm and no-palm
Externí odkaz:
http://arxiv.org/abs/1701.06462
While vehicle license plate recognition (VLPR) is usually done with a sliding window approach, it can have limited performance on datasets with characters that are of variable width. This can be solved by hand-crafting algorithms to prescale the char
Externí odkaz:
http://arxiv.org/abs/1701.06439
Autor:
Chong, Yong Shean, Tay, Yong Haur
We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neur
Externí odkaz:
http://arxiv.org/abs/1701.01546
Autor:
Chong, Yong Shean, Tay, Yong Haur
This review article surveys the current progresses made toward video-based anomaly detection. We address the most fundamental aspect for video anomaly detection, that is, video feature representation. Much research works have been done in finding the
Externí odkaz:
http://arxiv.org/abs/1505.00523
Automated car license plate recognition systems are developed and applied for purpose of facilitating the surveillance, law enforcement, access control and intelligent transportation monitoring with least human intervention. In this paper, an algorit
Externí odkaz:
http://arxiv.org/abs/1504.06921
Today's high performance deep artificial neural networks (ANNs) rely heavily on parameter optimization, which is sequential in nature and even with a powerful GPU, would have taken weeks to train them up for solving challenging tasks [22]. HMAX [17]
Externí odkaz:
http://arxiv.org/abs/1502.02772
Autor:
Chua, Kah Keong, Tay, Yong Haur
In this paper, we described and developed a framework for Multilayer Perceptron (MLP) to work on low level image processing, where MLP will be used to perform image super-resolution. Meanwhile, MLP are trained with different types of images from vari
Externí odkaz:
http://arxiv.org/abs/1212.5352
Autor:
Lim, Hao Wooi, Tay, Yong Haur
We present a method for visual object classification using only a single feature, transformed color SIFT with a variant of Spatial Pyramid Matching (SPM) that we called Sliding Spatial Pyramid Matching (SSPM), trained with an ensemble of linear regre
Externí odkaz:
http://arxiv.org/abs/1212.3767