2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献_第4页

Partitioning the Input Domain for Classification 为分类划分输入域

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371293

Adrian Rechy Romero, Srimal Jayawardena, Mark Cox, P. Borges

引用次数: 2

Image Labeling by Integrating Local, Middle and Global Information 集成局部、中间和全局信息的图像标注

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371268

T. Ishida, K. Hotta

引用次数: 1

A Survey on Human Action Recognition Using Depth Sensors 基于深度传感器的人体动作识别研究进展

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371223

Bin Liang, Lihong Zheng

{"title":"A Survey on Human Action Recognition Using Depth Sensors","authors":"Bin Liang, Lihong Zheng","doi":"10.1109/DICTA.2015.7371223","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371223","url":null,"abstract":"The recent advent of depth sensors opens up new opportunities to advance human action recognition by providing depth information. Many different approaches have been proposed for human action recognition using depth sensors. The main purpose of this paper is to provide a comprehensive study and an updated review on human action recognition using depth sensors. We give an overview of recent works in this field from the viewpoints of data modalities, feature extraction and classification. In terms of data modalities from depth sensors, recent approaches can be roughly categorized into depth map based and skeleton based approaches. Since depth maps encode 3D shape and appearance information, approaches based on depth maps are suitable for short simple actions and can achieve high performance. In contrast, due to the discriminative power and more concise form of skeletal joints, skeleton based approaches can model more complex actions, even in real time. This paper further provides a summary of the results obtained in the last couple of years on the public datasets. Moreover, we discuss limitations of the state of the art and outline promising directions of research in this area. The review assists in guiding both researchers and practitioners in the selection and development of approaches for human action recognition using depth sensors.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115858954","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: 45

Robust Angle Invariant GAS Meter Reading 鲁棒角度不变燃气仪表读数

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371300

I. Gallo, Alessandro Zamberletti, L. Noce

{"title":"Robust Angle Invariant GAS Meter Reading","authors":"I. Gallo, Alessandro Zamberletti, L. Noce","doi":"10.1109/DICTA.2015.7371300","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371300","url":null,"abstract":"In this work we propose a novel method for automatic gas meter reading from real world images. In a wide range of countries all over the world, the existing automatic technology is not adopted, usually the reading is manually done on site, and a picture is taken through a mobile device as a proof of reading. In order to confirm the reading, a tedious work of checking the proof images is commonly done offline by an operator. With this contribution we aim to supply an effective system, able to provide a real support to the validation process reducing the human effort and the time consumed. We exploit both region-based and Maximally Stable Extremal Regions techniques, during the phase involving the localization of the meter area and to detect the meter counter digits in the detection step respectively. The evaluation has been carried out on every step of our approach, as well as on the overall assessment; although the problem is complex, the proposed method leads to good results even when applied to degraded images, it represents an effective solution to the gas meter reading problem and it can be utilized in real applications.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115777063","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: 28

Selective Multi-Source Total Variation Image Restoration 选择性多源全变差图像恢复

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371306

Stephen Tierney, Yi Guo, Junbin Gao

引用次数: 6

Monocular Position-Pose Measurement Based on Circular and Linear Features 基于圆形和线性特征的单目位姿测量

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371284

Cai Meng, Jiao Xue, Zhan Hu

引用次数: 6

Quantitative and Qualitative Evaluation of Performance and Robustness of Image Stitching Algorithms 图像拼接算法的性能和鲁棒性的定量和定性评价

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371297

Vipula Dissanayake, Sachini Herath, Sanka Rasnayaka, Sachith Seneviratne, Rajith Vidanaarachchi, C. Gamage

{"title":"Quantitative and Qualitative Evaluation of Performance and Robustness of Image Stitching Algorithms","authors":"Vipula Dissanayake, Sachini Herath, Sanka Rasnayaka, Sachith Seneviratne, Rajith Vidanaarachchi, C. Gamage","doi":"10.1109/DICTA.2015.7371297","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371297","url":null,"abstract":"Many different image stitching algorithms, and mechanisms to assess their quality have been proposed by different research groups in the past decade. However, a comparison across different stitching algorithms and evaluation mechanisms has not been performed before. Our objective is to recognize the best algorithm for panoramic image stitching. We measure the robustness of different algorithms by means of assessing image quality of a set of panoramas. For the evaluation itself a varied set of assessment criteria are used, and the evaluation is performed over a large range of images captured using differing cameras. In an ideal stitching algorithm, the resulting stitched image should be without visible seams and other noticeable anomalies. An objective evaluation for image quality should give results corresponding to a similar evaluation by the Human Visual System. Our conclusion is that the choice of stitching algorithm is scenario dependent, with run-time and accuracy being the primary considerations.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121998975","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: 14

Kernel Subspace Integral Image Based Probabilistic Visual Object Tracking 基于核子空间积分图像的概率视觉目标跟踪

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371275

Iftikhar Majeed, Omar Arif

引用次数: 0

Morphological Filtering and Hierarchical Deformation for Partially Overlapping Cell Segmentation 部分重叠细胞分割的形态学滤波和层次变形

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371285

Afaf Tareef, Yang Song, Min-Zhao Lee, D. Feng, Mei Chen, Weidong (Tom) Cai

引用次数: 13

Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests 使用级联深度学习和随机森林的乳房x线照片自动质量检测

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371234

Neeraj Dhungel, G. Carneiro, A. Bradley

{"title":"Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests","authors":"Neeraj Dhungel, G. Carneiro, A. Bradley","doi":"10.1109/DICTA.2015.7371234","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371234","url":null,"abstract":"Mass detection from mammograms plays a crucial role as a pre- processing stage for mass segmentation and classification. The detection of masses from mammograms is considered to be a challenging problem due to their large variation in shape, size, boundary and texture and also because of their low signal to noise ratio compared to the surrounding breast tissue. In this paper, we present a novel approach for detecting masses in mammograms using a cascade of deep learning and random forest classifiers. The first stage classifier consists of a multi-scale deep belief network that selects suspicious regions to be further processed by a two-level cascade of deep convolutional neural networks. The regions that survive this deep learning analysis are then processed by a two-level cascade of random forest classifiers that use morphological and texture features extracted from regions selected along the cascade. Finally, regions that survive the cascade of random forest classifiers are combined using connected component analysis to produce state-of-the-art results. We also show that the proposed cascade of deep learning and random forest classifiers are effective in the reduction of false positive regions, while maintaining a high true positive detection rate. We tested our mass detection system on two publicly available datasets: DDSM-BCRP and INbreast. The final mass detection produced by our approach achieves the best results on these publicly available datasets with a true positive rate of 0.96 ± 0.03 at 1.2 false positive per image on INbreast and true positive rate of 0.75 at 4.8 false positive per image on DDSM-BCRP.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132925999","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: 206