2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献_第2页

A Comparison between Anatomy-Based and Data-Driven Tree Models for Human Pose Estimation 基于解剖和数据驱动的树模型在人体姿态估计中的比较

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227386

H. Vu, Richardt H. Wilkinson, M. Lech, E. Cheng

引用次数: 1

Gate and Common Pathway Detection in Crowd Scenes Using Motion Units and Meta-Tracking 使用运动单元和元跟踪的人群场景门和公共路径检测

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227438

Abdullah N. Moustafa, Mohamed E. Hussein, W. Gomaa

引用次数: 2

A Novel Orientation-Context Descriptor and Locality-Preserving Fisher Discrimination Dictionary Learning for Action Recognition 一种新的方向-上下文描述符和保域Fisher判别字典学习用于动作识别

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227395

Renlong Pan, Lihong Ma, Yupeng Zhan, S. Cai

{"title":"A Novel Orientation-Context Descriptor and Locality-Preserving Fisher Discrimination Dictionary Learning for Action Recognition","authors":"Renlong Pan, Lihong Ma, Yupeng Zhan, S. Cai","doi":"10.1109/DICTA.2017.8227395","DOIUrl":"https://doi.org/10.1109/DICTA.2017.8227395","url":null,"abstract":"This paper presents a novel local posture orientation-context descriptor, and proposes a FDDL(Fisher discriminant dictionary learning) method based on local orientation-preserving(LOP-FDDL) for sparse coding in action recognition task. To take full use of the information about the position of the local body-part related to the center of the torso, ant the spatial-temporal shape changes of the human body-parts, we extract orientation-context descriptors of local body-parts to express the local posture of human body. Our descriptors not only include orientation information, but and also include the information of geometric structure and motion of body-parts. In order to accurately express action sequences, we need to learn a discriminative dictionary with strong expressive power which consists of the information about categories and orientations of body-parts from the extracted posture descriptors. Hence, a discriminative dictionary learning model based on the manifold constraint of local orientation-preserving is proposed, and Fisher Criteria is considered in the sparse coding stage of this model, which makes the coding coefficients discriminative. Meanwhile, to improve the performance of dictionary and learning efficiency, we initialize the dictionary as a class-structured dictionary which is a block-structured dictionary with orientation information. Experimental results demonstrate that our proposed method is better than other related action recognition methods on Weizmann and KTH public datasets.","PeriodicalId":194175,"journal":{"name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132557851","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: 1

Combining Unmixing and Deep Feature Learning for Hyperspectral Image Classification 结合解混和深度特征学习的高光谱图像分类

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227419

F. Alam, J. Zhou, Lei Tong, Alan Wee-Chung Liew, Yongsheng Gao

{"title":"Combining Unmixing and Deep Feature Learning for Hyperspectral Image Classification","authors":"F. Alam, J. Zhou, Lei Tong, Alan Wee-Chung Liew, Yongsheng Gao","doi":"10.1109/DICTA.2017.8227419","DOIUrl":"https://doi.org/10.1109/DICTA.2017.8227419","url":null,"abstract":"Image classification is one of the critical tasks in hyperspectral remote sensing. In recent years, significant improvement have been achieved by various classification methods. However, mixed spectral responses from different ground materials still create confusions in complex scenes. In this regard, unmixing approaches are being successfully carried out to decompose mixed pixels into a collection of spectral signatures. Considering the usefulness of these techniques, we propose to utilize the unmixing results as an input to classifiers for better classification accuracy. We propose a novel band group based structure preserving nonnegative matrix factorization (NMF) method to estimate the individual spectral responses from different materials within different ranges of wavelengths. Then we train a convolutional neural network (CNN) with the unmixing results to generate powerful features and eventually classify the data. This method is evaluated on a new dataset and compared with several state-of-the-art models, which shows the promising potential of our method.","PeriodicalId":194175,"journal":{"name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134421716","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: 8

A Fully-Convolutional Framework for Semantic Segmentation 语义分割的全卷积框架

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227388

Yalong Jiang, Z. Chi

引用次数: 5

4K Ultra High Definition Video Coding Using Homogeneous Motion Discovery Oriented Prediction 基于均匀运动发现预测的4K超高清视频编码

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227385

Ashek Ahmmed, Afrin Rahman, M. Pickering, A. Naman

引用次数: 1

Local Features Augmenting for Better Image Retrieval 基于局部特征增强的图像检索

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227461

Long Zhao, Yu Wang, Jien Kato

引用次数: 0

Robust Tracking via Spatio-Temporally Weighted Multiple Instance Learning 基于时空加权多实例学习的鲁棒跟踪

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227488

Li Wang, Xiao'an Tang, Dongdong Li

{"title":"Robust Tracking via Spatio-Temporally Weighted Multiple Instance Learning","authors":"Li Wang, Xiao'an Tang, Dongdong Li","doi":"10.1109/DICTA.2017.8227488","DOIUrl":"https://doi.org/10.1109/DICTA.2017.8227488","url":null,"abstract":"Due to the superiority in handling label ambiguity, multiple instance learning (MIL) has been introduced into adaptive tracking-by-detection methods to alleviate drift and yields promising tracking performance. However, the MIL tracker assumes that all samples in a positive bag contribute equally to the bag probability, which ignores sample importance. To address this issue, in this paper we propose a spatio- temporally weighted MIL (STWMIL) tracker which integrates temporal weight into the update scheme for Haar-like features and spatial weight into the bag probability function. Spatial weight for the positive sample near the target location is larger than that far from the target location, which means the former contributes more to the positive bag probability. Based on spatial weight, a novel bag probability function is proposed using the weighted Noisy-OR model. Temporal weight for the recently-acquired images is larger than that for the earlier observations, which means less modeling power is expended on old observations. Based on temporal weight, a novel update scheme with changing but convergent learning rate is derived with strict mathematic proof. Extensive experiments performed on the OTB-2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of- the-art trackers.","PeriodicalId":194175,"journal":{"name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449088","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

A Novel Virtual View Quality Enhancement Technique through a Learning of Synthesised Video 一种基于合成视频学习的虚拟视场质量增强技术

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227397

D. M. Rahaman, M. Paul

{"title":"A Novel Virtual View Quality Enhancement Technique through a Learning of Synthesised Video","authors":"D. M. Rahaman, M. Paul","doi":"10.1109/DICTA.2017.8227397","DOIUrl":"https://doi.org/10.1109/DICTA.2017.8227397","url":null,"abstract":"With the development of displaying techniques, free viewpoint video (FVV) system shows its potential to provide immersive perceptual feeling by changing viewpoints. To provide this luxury, a large number of high quality views have to be synthesised from limited number of viewpoints. However, in this process, a portion of the background is occluded by the foreground object in the generated synthesised videos. Recent techniques, i.e. view synthesized prediction using Gaussian model (VSPGM) and adaptive weighting between warped and learned foregrounds indicate that learning techniques may fill occluded areas almost correctly. However, these techniques use temporal correlation by assuming that original texture of the target viewpoint are already available to fill up occluded areas which is not a practical solution. Moreover, if a pixel position experiences foreground once during learning, the existing techniques considered it as foreground throughout the process. However, the actual fact is that after experiencing a foreground a pixel position can be background again. To address the aforementioned issues, in the proposed view synthesise technique, we apply Gaussian mixture modelling (GMM) on the output images of inverse mapping (IM) technique for further improving the quality of the synthesised videos. In this technique, the foreground and background pixel intensities are refined from adaptive weights of the output of inverse mapping and the pixel intensities from the corresponding model(s) of the GMM. This technique provides a better pixel correspondence, which improves 0.10~0.46dB PSNR compared to the IM technique.","PeriodicalId":194175,"journal":{"name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115832281","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: 3

Fine-Grained Butterfly Recognition with Deep Residual Networks: A New Baseline and Benchmark 基于深度残差网络的细粒度蝴蝶识别:一种新的基线和基准

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227435

Lin Nie, Keze Wang, Xiaoling Fan, Yuefang Gao

引用次数: 6