2013 2nd IAPR Asian Conference on Pattern Recognition最新文献_第6页

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

Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning 使用皮肤镜成像和集成学习进行黑色素瘤分类

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.102

G. Schaefer, B. Krawczyk, M. E. Celebi, H. Iyatomi

{"title":"Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning","authors":"G. Schaefer, B. Krawczyk, M. E. Celebi, H. Iyatomi","doi":"10.1109/ACPR.2013.102","DOIUrl":"https://doi.org/10.1109/ACPR.2013.102","url":null,"abstract":"Malignant melanoma, the deadliest form of skin cancer, is one of the most rapidly increasing cancers in the world. Early diagnosis is crucial, since if detected early, it can be cured through a simple excision. In this paper, we present an effective approach to melanoma classification from dermoscopic images of skin lesions. First, we perform automatic border detection to delineate the lesion from the background skin. Shape features are then extracted from this border, while colour and texture features are obtained based on a division of the image into clinically significant regions. The derived features are then used in a pattern classification stage for which we employ a dedicated ensemble learning approach to address the class imbalance in the training data. Our classifier committee trains individual classifiers on balanced subspaces, removes redundant predictors based on a diversity measure and combines the remaining classifiers using a neural network fuser. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, to provide both high sensitivity and specificity, and the use of our classifier ensemble to lead to statistically better recognition performance.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"42 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":"122930802","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: 10

Deformed and Touched Characters Recognition 变形和触摸字符识别

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.193

Tadashi Hyuga, H. Wada, Tomoyoshi Aizawa, Yoshihisa Ijiri, M. Kawade

引用次数: 1

Multi-layered Background Modeling for Complex Environment Surveillance 复杂环境监测的多层背景建模

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.83

S. Yoshinaga, Atsushi Shimada, H. Nagahara, R. Taniguchi, Kouichiro Kajitani, Takeshi Naito

引用次数: 1

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

Consensus Region Merging for Image Segmentation 图像分割的一致区域合并

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.142

F. Nielsen, R. Nock

引用次数: 8

A Fast Alternative for Template Matching: An ObjectCode Method 模板匹配的快速替代方法:ObjectCode方法

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.80

Yiping Shen, Shuxiao Li, Chenxu Wang, Hongxing Chang

引用次数: 1

HEp-2 Cell Classification Using Multi-dimensional Local Binary Patterns and Ensemble Classification 基于多维局部二值模式和集成分类的HEp-2细胞分类

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.175

G. Schaefer, N. Doshi, B. Krawczyk

{"title":"HEp-2 Cell Classification Using Multi-dimensional Local Binary Patterns and Ensemble Classification","authors":"G. Schaefer, N. Doshi, B. Krawczyk","doi":"10.1109/ACPR.2013.175","DOIUrl":"https://doi.org/10.1109/ACPR.2013.175","url":null,"abstract":"Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. This categorisation is typically performed by manual evaluation which is time consuming and subjective. In this paper, we present a method for automatic classification of HEp-2 cells using local binary pattern (LBP) based texture descriptors and ensemble classification. In our approach, we utilise multi-dimensional LBP (MD-LBP) histograms, which record multi-scale texture information while maintaining the relationships between the scales. Our dedicated ensemble classification approach is based on a set of heterogeneous base classifiers obtained through application of different feature selection algorithms, a diversity based pruning stage and a neural network classifier fuser. We test our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to outperform all algorithms that were entered in the competition as well as to exceed the performance of a human expert.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"40 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":"125335923","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: 4

Group Leadership Estimation Based on Influence of Pointing Actions 基于指向行为影响的群体领导力评估

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.181

H. Habe, K. Kajiwara, Ikuhisa Mitsugami, Y. Yagi

引用次数: 0

Improving Sampling Criterion for Alpha Matting 改进的Alpha抠图采样准则

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.145

Jun Cheng, Z. Miao

引用次数: 8