2013 2nd IAPR Asian Conference on Pattern Recognition最新文献_第4页

Planar Segmentation from Point Clouds via Graph Laplacian Regularized K-Planes 基于图拉普拉斯正则k平面的点云平面分割

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.15

Wei Sui, Lingfeng Wang, Huai-Yu Wu, Chunhong Pan

引用次数: 0

Consistent Segmentation Based Color Correction for Coarsely Registered Images 基于一致分割的粗配准图像颜色校正

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.72

Haoxing Wang, Longquan Dai, Xiaopeng Zhang

引用次数: 2

Vehicle Detection in Satellite Images by Parallel Deep Convolutional Neural Networks 基于并行深度卷积神经网络的卫星图像车辆检测

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.33

Xueyun Chen, Shiming Xiang, Cheng-Lin Liu, Chunhong Pan

{"title":"Vehicle Detection in Satellite Images by Parallel Deep Convolutional Neural Networks","authors":"Xueyun Chen, Shiming Xiang, Cheng-Lin Liu, Chunhong Pan","doi":"10.1109/ACPR.2013.33","DOIUrl":"https://doi.org/10.1109/ACPR.2013.33","url":null,"abstract":"Deep convolutional Neural Networks (DNN) is the state-of-the-art machine learning method. It has been used in many recognition tasks including handwritten digits, Chinese words and traffic signs, etc. However, training and test DNN are time-consuming tasks. In practical vehicle detection application, both speed and accuracy are required. So increasing the speeds of DNN while keeping its high accuracy has significant meaning for many recognition and detection applications. We introduce parallel branches into the DNN. The maps of the layers of DNN are divided into several parallel branches, each branch has the same number of maps. There are not direct connections between different branches. Our parallel DNN (PNN) keeps the same structure and dimensions of the DNN, reducing the total number of connections between maps. The more number of branches we divide, the more swift the speed of the PNN is, the conventional DNN becomes a special form of PNN which has only one branch. Experiments on large vehicle database showed that the detection accuracy of PNN dropped slightly with the speed increasing. Even the fastest PNN (10 times faster than DNN), whose branch has only two maps, fully outperformed the traditional methods based on features (such as HOG, LBP). In fact, PNN provides a good solution way for compromising the speed and accuracy requirements in many applications.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122751171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 70

Real-Time Binary Descriptor Based Background Modeling 基于实时二进制描述符的背景建模

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.125

Wan-Chen Liu, Shu-Zhe Lin, Min-Hsiang Yang, Chun-Rong Huang

引用次数: 13

Learning Fingerprint Orientation Fields Using Continuous Restricted Boltzmann Machines 使用连续受限玻尔兹曼机学习指纹方向场

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.37

M. Sahasrabudhe, A. Namboodiri

引用次数: 14

Automatic Elements Extraction of Chinese Web News Using Prior Information of Content and Structure 基于内容和结构先验信息的中文网络新闻元素自动提取

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.52

Chengru Song, Shifeng Weng, Changshui Zhang

引用次数: 0

Saliency Detection Using Color Spatial Variance Weighted Graph Model 基于颜色空间方差加权图模型的显著性检测

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.93

Xiaoyun Yan, Yuehuang Wang, Mengmeng Song, Man Jiang

引用次数: 0

New Banknote Number Recognition Algorithm Based on Support Vector Machine 基于支持向量机的纸币号码识别新算法

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.115

S. Gai, Guowei Yang, S. Zhang, M. Wan

引用次数: 6

A Multi-resolution Action Recognition Algorithm Using Wavelet Domain Features 使用小波域特征的多分辨率动作识别算法

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.143

H. Imtiaz, U. Mahbub, G. Schaefer, Md Atiqur Rahman Ahad

{"title":"A Multi-resolution Action Recognition Algorithm Using Wavelet Domain Features","authors":"H. Imtiaz, U. Mahbub, G. Schaefer, Md Atiqur Rahman Ahad","doi":"10.1109/ACPR.2013.143","DOIUrl":"https://doi.org/10.1109/ACPR.2013.143","url":null,"abstract":"This paper proposes a novel approach for human action recognition using multi-resolution feature extraction based on the two-dimensional discrete wavelet transform (2D-DWT). Action representations can be considered as image templates, which can be useful for understanding various actions or gestures as well as for recognition and analysis. An action recognition scheme is developed based on extracting features from the frames of a video sequence. The proposed feature selection algorithm offers the advantage of very low feature dimensionality and therefore lower computational burden. It is shown that the use of wavelet-domain features enhances the distinguish ability of different actions, resulting in a very high within-class compactness and between-class separability of the extracted features, while certain undesirable phenomena, such as camera movement and change in camera distance from the subject, are less severe in the frequency domain. Principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations on a standard benchmark database confirm that the proposed approach offers not only computational savings but also a very recognition accuracy.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126963557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Correlation-Based Facade Parsing Using Shape Grammar 使用形状语法的基于关联的Facade解析

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.81

Runze Zhang, Ruiling Deng, Xin He, Gang Zeng, Rui Gan, H. Zha

引用次数: 1