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1. The Detection of Cheating in the Invigilating Video Based on SSD | |||
Bai Hao,Zhang Honggang | |||
Computer Science and Technology 28 November 2017 | |||
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Abstract:Over the years, with the continuous improvement and development of deep learning algorithm, especially in combination with object detection, a breakthrough has been made. Therefore, the third generation of intelligent monitoring technology combined with computer vision has also achieved great development, and has played a very important role in the real-time traffic,banking and other fields. The Single Shot MultiBox Detector (SSD) is a multi-target fast detection algorithm that directly predicts object classes and bounding boxes. Because of its excellent overall performance, it is one of the mainstream frameworks for object detection so far and is widely applied Image recognition.Through careful analysis of the object detection of deep learning, this paper uses SSD to detect the object that cheating in the field of the examination\'s surveillance video and improves the output of detection results by combining triplet loss.The experimental results show that detection results has been achieved quickly and accurately under different scenarios by this method. | |||
TO cite this article:Bai Hao,Zhang Honggang. The Detection of Cheating in the Invigilating Video Based on SSD[OL].[28 November 2017] http://en.paper.edu.cn/en_releasepaper/content/4742297 |
2. The Comprehension of Night Outdoor Scenes Based on GANs | |||
Wang Wenxue,Zhang Honggang | |||
Computer Science and Technology 28 November 2017 | |||
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Abstract:Recently, with the increasing demand of growing market of ADAS and autonomous car, the technology of CV is dramatically developing.However, the comprehension of complicated scene image is still a difficult problem. This paper focus on the comprehension of night traffic scene image. Different from the traditional way that processes the night image directly, we propose a new method to comprehend the image regarding the feature of night image. This paper crawled near 30,000 pairs ofday and nightimages from AMOS webcams,then filtered and processed them to meet the needs. This paper trained the GAN model with the input of night image and output of day image. After that, the paper segmented the image, a day image that is generated froma night image, as the segmentation result using SegNet. The features in the method can also be used in scene classification. This paperfinally got the night images\' sematic meanings. | |||
TO cite this article:Wang Wenxue,Zhang Honggang. The Comprehension of Night Outdoor Scenes Based on GANs[OL].[28 November 2017] http://en.paper.edu.cn/en_releasepaper/content/4742121 |
3. Cross-Weather Road Scene Re-Identification Based on CNN | |||
Zhu Jiahui,Di Shuai,Zhang Honggang | |||
Computer Science and Technology 17 November 2017 | |||
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Abstract:In recent years, researches in the field of autonomous driving are in full swing. Among them, the road detection technology is the key. This technology uses sensors to analyze road forward derection, traffic signs, road lines, pedestrains\' status and other information in real-time. Because the road environment is very complex, the road detection algorithm must be robust against illumination changes, different weather conditions etc. In real life, the road environment changes little, and the daily driving routes are mostly in the same section. Based on these assumptions, this paper propased a way to tranform the road image which are under poor environmental conditions into images that are taken in the same place but are under good environmental conditions, and then to carry out road detection in subsequent mudules. In order to verify the idea, a road image database was established firstly, and the system was completed base on the classical CNN network. The expected results were obtained. In order to improve system\'s accuracy, this paper fine tuned the parameters of the neural network in the classical CNN. Afterwards, the transformation was learned on the basis of the CNN features and then the CNN features were projected into the domain-invariant feature space which was immune to drastic weather or illumination changes. Finally, the experiments have proved the practicality and the validity of the system. | |||
TO cite this article:Zhu Jiahui,Di Shuai,Zhang Honggang. Cross-Weather Road Scene Re-Identification Based on CNN[OL].[17 November 2017] http://en.paper.edu.cn/en_releasepaper/content/4742122 |
4. Pointwise Manifold Regularization for semi-supervised classification | |||
WANG Yunyun,SHEN Yating | |||
Computer Science and Technology 26 April 2017 | |||
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Abstract:Manifold regularization (MR) provides a powerful framework for semi-supervised classification using both the labeled and unlabeled data. It constrains that similar instances over the manifold graph should share similar classification outputs according to the manifold assumption. It is easily noted that MR is built on the pairwise smoothness over the manifold graph, i.e., the smoothness constraint is implemented over all instance pairs and actually considers each instance pair as a single operator. However, the smoothness can be pointwise in nature, that is, the smoothness shall inherently occur "everywhere" to relate the behavior of each point or instance to that of its close neighbors. Thus in this paper, we attempt to develop a pointwise MR (PW_MR for short) for semi-supervised learning through constraining on individual local instances. In this way, the pointwise nature of smoothness is preserved, and moreover, by considering individual instances rather than the instance pairs, the importance or contribution of individual instances can be introduced. Such importance can be the confidence for correct prediction, or the local density, for example. Finally, our empirical results show the competitiveness of PR_MR compared to pairwise MR.????? | |||
TO cite this article:WANG Yunyun,SHEN Yating. Pointwise Manifold Regularization for semi-supervised classification[OL].[26 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4728268 |
5. Illumination and Rotation Invariant Featureof Texture Images Based on Hilbert-Huang Transform | |||
Yang Zhihua, Zhang Qian,Yang Lihua | |||
Computer Science and Technology 23 April 2017 | |||
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Abstract:This paper presents a novel method to extract theillumination and rotation invariant features for texture imagesbased on Hilbert-Huang transform. Texture images are usually ofquasi-periodic. It is shown in this paper that the main frequencyof the Hilbert marginal spectrum of a texture image can be used to measure the approximate period effectively and thus can be servedas a good feature for texture classification. This feature isproved to be invariant to uneven illumination. Being modified, itis shown that this feature is also invariant rotation. Experimentshave been conducted to compare the feature with the existing ones.It is shown that the proposed approach outperforms the existingmethods in both recognition rate and robustness to unevenillumination, rotation and noise pollution. | |||
TO cite this article:Yang Zhihua, Zhang Qian,Yang Lihua. Illumination and Rotation Invariant Featureof Texture Images Based on Hilbert-Huang Transform[OL].[23 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726832 |
6. Target-specific Convolutional \ Bi-directional LSTM Neural Network for \Political Ideology Analysis | |||
LI Xi-Lian, CHEN Wei, WANG Teng-Jiao | |||
Computer Science and Technology 20 April 2017 | |||
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Abstract:Ideology detection from text plays an important role in identifying the political ideology of politicians who have expressed their beliefs on many issues. Most existing approaches based on bag-of-words features fail to capture semantic information. And other sentence modeling methods are inefficient to extract ideological target context which is significant for identifying the political ideology. In this paper, we propose a target-specific Convolutional and Bi-directional Long Short Term Memory neural network (CB-LSTM) which is expert in intensifying ideological target-related context and learning semantic representations of the text at the same time. We conduct experiments on two commonly used datasets and a well-designed twitter dataset. The experimental results show that the CB-LSTM outperforms the state-of-the-art methods. | |||
TO cite this article:LI Xi-Lian, CHEN Wei, WANG Teng-Jiao. Target-specific Convolutional \ Bi-directional LSTM Neural Network for \Political Ideology Analysis[OL].[20 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726068 |
7. Touching Cells Segmentation Using Intensity and Shape Fused Method | |||
WANG Yuliang,WANG Chunqi | |||
Computer Science and Technology 07 March 2017 | |||
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Abstract:Cell segmentation is an essential step for cell observation and analysis at individual level and group level. However, in high throughput images, touching cells detection and separation remains a challenge. In this study, a touching cells segmentation method for negative phase contrast images is proposed, which takes advantage of both intensity information and shape information. An adaptive binarization is adopted in order to obtain the preliminary segmentation and the shape information of cell regions. Then the intensity information and the shape information are fused by means of gray weighted distance transform, and the transformation result is utilized to detect suspected touching cells. A region skeleton based touching cell separation method is implemented to split the actual touching cells and remain the actual individual one using the fused information. Experiments are conducted on comparison with other touching cell separation methods and on whole cell image segmentation, the result of which shows an improved performance of the proposed method. | |||
TO cite this article:WANG Yuliang,WANG Chunqi. Touching Cells Segmentation Using Intensity and Shape Fused Method[OL].[ 7 March 2017] http://en.paper.edu.cn/en_releasepaper/content/4720414 |
8. Sparse Discriminative Analysis and Its Application in Distraction Classification | |||
Computer Science and Technology 14 January 2017 | |||
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Abstract: Sparse discriminant analysis (SDA) imposes $l$-1 regularization to encourage sparse coefficients in linear discriminant transform. This approach has found a broad range of machine learning tasks, due to its capability of identifying the most promising features so that the feature dimensionality can be significantly reduced, leading to most robust and generalizable models. This paper reviews the development of SDA from linear discriminative analysis (LDA), and presents its application to the driving distraction detection task. | |||
TO cite this article:Sparse Discriminative Analysis and Its Application in Distraction Classification [OL].[14 January 2017] http://en.paper.edu.cn/en_releasepaper/content/4715885 |
9. Face Recognition based on Simplified CNN and Median Pooling | |||
XIONG Feng-ye, DONG Yuan, BAI Hong-liang | |||
Computer Science and Technology 13 October 2016 | |||
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Abstract:Convolution neural network(CNN) is increasingly used by the groups studying face recognition. CNN dramatically improves the performance on many datasets such as LFW and IJB-A. But most of the groups extract features from big networks with large amount of parameters and FLOPS. In this work, a simplified CNN architecture, which achieves comparable results to the state of the art, with only 0.8M training data, 4.4M parameters and 0.6B FLOPS, is proposed. In addition, an anti-noise median pooling method is introduced when dealing with template-based comparison. | |||
TO cite this article:XIONG Feng-ye, DONG Yuan, BAI Hong-liang. Face Recognition based on Simplified CNN and Median Pooling[OL].[13 October 2016] http://en.paper.edu.cn/en_releasepaper/content/4706549 |
10. A Shape Semantic Graph Representation for Object Understanding and Recognition in Point Clouds | |||
NING Xiaojuan, WANG Yinghui | |||
Computer Science and Technology 16 May 2016 | |||
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Abstract:To understand and to recognize 3D objects represented as point cloud data, we provide a shape semantic graph (SSG) representation method to describe 3D objects. Based on the decomposed components of the object, the boundary surface of different components and the topology of the skeleton, the SSG gives a semantic description that is consistent with human vision perception. The similarity measurement of SSG for different objects is also effective to distinguish the type of objects and find the most similar one. Experiments on the shape database show that SSG is valuable for capturing the components of the objects and the corresponding relations between them. Not only can it be suitable for the object without loop but also appropriate for the object with loop to represent the shape and the topology. Besides, the combination of two-step progressive similarity measurement strategy can effectively improve the recognition rate in the shape database containing point-sample data. | |||
TO cite this article:NING Xiaojuan, WANG Yinghui. A Shape Semantic Graph Representation for Object Understanding and Recognition in Point Clouds[OL].[16 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4688548 |
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