2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第2页

Memory and Time Efficient 3D Neuron Morphology Tracing in Large-Scale Images 大规模图像中记忆和时间效率高的三维神经元形态跟踪

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615765

Heng Wang, Donghao Zhang, Yang Song, Siqi Liu, Rong Gao, Hanchuan Peng, Weidong (Tom) Cai

引用次数: 8

Enhancing the Effectiveness of Local Descriptor Based Image Matching 增强基于局部描述子的图像匹配的有效性

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615800

Md Tahmid Hossain, S. Teng, Dengsheng Zhang, Suryani Lim, Guojun Lu

引用次数: 0

DeepParse: A Trainable Postal Address Parser DeepParse:一个可训练的邮政地址解析器

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615844

N. Abid, A. Ul-Hasan, F. Shafait

{"title":"DeepParse: A Trainable Postal Address Parser","authors":"N. Abid, A. Ul-Hasan, F. Shafait","doi":"10.1109/DICTA.2018.8615844","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615844","url":null,"abstract":"Postal applications are among the first beneficiaries of the advancements in document image processing techniques due to their economic significance. To automate the process of postal services, it is necessary to integrate contributions from a wide range of image processing domains, from image acquisition and preprocessing to interpretation through symbol, character and word recognition. Lately, machine learning approaches are deployed for postal address processing. Parsing problem has been explored using different techniques, like regular expressions, Conditional Random Fields (CRFs), Hidden Markov Models (HMMs), Decision Trees and Support Vector Machines (SVMs). These traditional techniques are designed on the assumption that the data is free from OCR errors which decreases the adaptability of the architecture in the real-world scenarios. Furthermore, their performance is affected in the presence of non-standardized addresses resulting in intermixing of similar classes. In this paper, we present the first trainable neural network based robust architecture DeepParse for postal address parsing that tackles these issues and can be applied to any Named Entity Recognition (NER) problem. The architecture takes the input at different granularity levels: characters, trigram characters and words to extract and learn the features and classify the addresses. The model was trained on a synthetically generated dataset and tested on the real-world addresses. DeepParse has also been tested on the NER dataset i.e. CoNLL2003 and gave the result of 90.44% which is on par with the state-of-art technique.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124345093","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: 13

Human Ear Surface Reconstruction Through Morphable Model Deformation 基于变形模型的人耳表面重建

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615786

S. Kabbour, Pierre-Yves Richard

引用次数: 1

Optimization of a Principal Component Analysis Implementation on Field-Programmable Gate Arrays (FPGA) for Analysis of Spectral Images 用于光谱图像分析的现场可编程门阵列(FPGA)主成分分析实现的优化

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615866

M. Schellhorn, G. Notni

引用次数: 5

3D Multiview Basketball Players Detection and Localization Based on Probabilistic Occupancy 基于概率占用的三维多视角篮球运动员检测与定位

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615798

Yukun Yang, Min Xu, Wanneng Wu, Ruiheng Zhang, Yu Peng

{"title":"3D Multiview Basketball Players Detection and Localization Based on Probabilistic Occupancy","authors":"Yukun Yang, Min Xu, Wanneng Wu, Ruiheng Zhang, Yu Peng","doi":"10.1109/DICTA.2018.8615798","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615798","url":null,"abstract":"This paper addresses the issue of 3D multiview basketball players detection and localization. Existing methods for this problem typically take background subtraction as input, which limits the accuracy of localization and the performance of further object tracking. Moreover, the performance of background subtraction based methods is heavily impacted by the occlusions in crowded scenes. In this paper, we propose an innovative method which jointly implements deep learning based player detection and occupancy probability based player localization. What's more, a new Bayesian model of the localization algorithms is developed, which uses foreground information from fisheye cameras to setup meaningful initialization values in the first step of iteration, in order to not only eliminate ambiguous detection, but also accelerate computational processes. Experimental results on real basketball game data demonstrate that our methods significantly improve the performance compared with current methods, by eliminating missed and false detection, as well as increasing probabilities of positive results.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"61 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130720214","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: 9

Data Augmentation using Evolutionary Image Processing 使用进化图像处理的数据增强

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615799

Kosaku Fujita, Masayuki Kobayashi, T. Nagao

{"title":"Data Augmentation using Evolutionary Image Processing","authors":"Kosaku Fujita, Masayuki Kobayashi, T. Nagao","doi":"10.1109/DICTA.2018.8615799","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615799","url":null,"abstract":"In the machine learning community, data augmentation techniques have been widely used to make deep neural networks invariant to object transition. However, less attention has been paid to data augmentation in traditional classification methods. In this paper, we take a closer look at traditional classification methods and introduce a new data augmentation technique based on the concept of image transformation. Starting with a few existing examples, we add noise and generate new data points to reduce sparseness in a given feature space. Then, we generate images corresponding to the new data points, although this is usually an ill-posed problem. Herein, the novelty is in constructing an image transformation tree and generating new data from a small number of instances. This allows us to reduce sparseness in the feature space and build more robust classifiers. We evaluate our method on the Caltech-101 dataset to verify its potential. In the context of the situation where the amount of training data is limited, we demonstrate that the support vector machine-based classifiers trained with an augmented dataset using our method outperform classifiers trained with the original dataset in most cases.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132999215","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: 11

Image Registration via Geometrically Constrained Total Variation Optical Flow 几何约束全变分光流图像配准

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615805

M. Shoeiby, M. Armin, A. Robles-Kelly

引用次数: 0

Demodulation of Multi-Level Data using Convolutional Neural Network in Holographic Data Storage 全息数据存储中多层数据的卷积神经网络解调

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615863

Yutaro Katano, Tetsuhiko Muroi, N. Kinoshita, Norihiko Ishii

引用次数: 3

Generative Adversarial Network (GAN) Based Data Augmentation for Palmprint Recognition 基于生成对抗网络(GAN)的掌纹识别数据增强

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615782

Gengxing Wang, Wenxiong Kang, Qiuxia Wu, Zhiyong Wang, Junbin Gao

{"title":"Generative Adversarial Network (GAN) Based Data Augmentation for Palmprint Recognition","authors":"Gengxing Wang, Wenxiong Kang, Qiuxia Wu, Zhiyong Wang, Junbin Gao","doi":"10.1109/DICTA.2018.8615782","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615782","url":null,"abstract":"Palmprint recognition is a very important field of biometrics, and has been intensively researched on both feature extraction and classification methods. Recently, deep learning techniques such as convolutional neural networks have demonstrated clear advantages over traditional learning algorithms for various image classification tasks such as object recognition and detection. However, a large amount of data is needed to train deep networks, which limits its application to some tasks such as palmprint recognition where it lacks of sufficient training samples for each class (i.e., each individual). In this paper, we propose a Generative Adversarial Net (GAN) based solution to augment training data for improved performance of palmprint recognition. An improved Deep Convolutional Generative Adversarial Net (DCGAN) is first devised to generate high quality plamprint images by replacing convolutional transpose layer with linear upsampling and introducing Structure Similarity (SSIM) index into loss function. As a result, the generated images have discriminative features, increased smoothness and consistency, and less variance compared to those generated by the baseline DCGAN. Then, a mixing training strategy via a combination of GAN-based and classical data augmentation techniques is adopted to further improve recognition performance. The experimental results on two publicly available datasets demonstrate the effectiveness of our proposed GAN based data augmentation method in palmprint recognition. Our method is able to achieve 1.52% and 0.37% Equal Error Rates (EER) on IIT Delhi and CASIA palmprint datasets, respectively, which outperforms other existing methods.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122549782","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: 30