2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献

Automatic Building Footprint Extraction and Regularisation from LIDAR Point Cloud Data 基于激光雷达点云数据的楼宇足迹自动提取与正则化

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008096

M. Awrangjeb, Guojun Lu

{"title":"Automatic Building Footprint Extraction and Regularisation from LIDAR Point Cloud Data","authors":"M. Awrangjeb, Guojun Lu","doi":"10.1109/DICTA.2014.7008096","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008096","url":null,"abstract":"This paper presents a segmentation of LIDAR point cloud data for automatic extraction of building footprint. Using the ground height information from a DEM (Digital Elevation Model), the non-ground points (mainly buildings and trees) are separated from the ground points. Points on walls are removed from the set of non-ground points. The remaining non-ground points are then divided into clusters based on height and local neighbourhood. Planar roof segments are extracted from each cluster of points following a region-growing technique. Planes are initialised using coplanar points as seed points and then grown using plane compatibility tests. Once all the planar segments are extracted, a rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. The neighbouring planes are then merged to obtain individual building boundaries, which are regularised based on a new feature-based technique. Corners and line-segments are extracted from each boundary and adjusted using the assumption that each short building side is parallel or perpendicular to one or more neighbouring long building sides. Experimental results on five Australian data sets show that the proposed method offers higher correctness rate in building footprint extraction than a state-of-the-art method.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116881208","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

Near-Miss Event Detection at Railway Level Crossings 铁路平道口的近距离探测

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008119

Sina Aminmansour, F. Maire, C. Wullems

{"title":"Near-Miss Event Detection at Railway Level Crossings","authors":"Sina Aminmansour, F. Maire, C. Wullems","doi":"10.1109/DICTA.2014.7008119","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008119","url":null,"abstract":"Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near- miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near- miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120998805","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

Reflective Features Detection and Hierarchical Reflections Separation in Image Sequences 图像序列中的反射特征检测与分层反射分离

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008127

Di Yang, Srimal Jayawardena, Stephen Gould, Marcus Hutter

引用次数: 1

A Modular Learning Approach for Fish Counting and Measurement Using Stereo Baited Remote Underwater Video 基于立体诱饵远程水下视频的鱼类计数和测量模块化学习方法

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008086

F. Westling, Changming Sun, Dadong Wang

引用次数: 13

Performance Evaluation of 3D Local Surface Descriptors for Low and High Resolution Range Image Registration 三维局部表面描述符在低分辨率和高分辨率距离图像配准中的性能评价

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008123

S. A. A. Shah, Bennamoun, F. Boussaïd

引用次数: 12

Novel Evaluation Index for Image Quality 一种新的图像质量评价指标

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008120

Sheikh Md. Rabiul Islam, Xu Huang, K. Le

引用次数: 1

Pedestrian Lane Detection in Unstructured Environments for Assistive Navigation 面向辅助导航的非结构化环境行人车道检测

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008122

M. Le, S. L. Phung, A. Bouzerdoum

{"title":"Pedestrian Lane Detection in Unstructured Environments for Assistive Navigation","authors":"M. Le, S. L. Phung, A. Bouzerdoum","doi":"10.1109/DICTA.2014.7008122","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008122","url":null,"abstract":"Automatically finding paths is a crucial and challenging task in autonomous navigation systems. The task becomes more difficult in unstructured environments such as indoor or outdoor scenes with unmarked pedestrian lanes under severe illumination conditions, complex lane surface structures, and occlusion. This paper proposes a robust method for pedestrian lane detection in such unstructured environments. The proposed method detects the walking lane in a probabilistic framework integrating both appearance of the lane region and characteristics of the lane borders. The vanishing point is employed to identify the lane borders. We propose an improved vanishing point estimation method based on orientation of color edges, and use pedestrian detection for occlusion handling. The proposed pedestrian lane detection method is evaluated on a new data set of 2000 images collected from various indoor and outdoor scenes with different types of unmarked lanes. Experimental results and comparisons with other existing methods on the new data set have shown the efficiency and robustness of the proposed method.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123987905","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

Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images 基于概率潜在语义分析的图像标注无监督分类

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008133

Abass A. Olaode, G. Naghdy, Catherine A. Todd

{"title":"Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images","authors":"Abass A. Olaode, G. Naghdy, Catherine A. Todd","doi":"10.1109/DICTA.2014.7008133","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008133","url":null,"abstract":"Image annotation has been identified to be a suitable means by which the semantic gap which has made the accuracy of Content-based image retrieval unsatisfactory be eliminated. However existing methods of automatic annotation of images depends on supervised learning, which can be difficult to implement due to the need for manually annotated training samples which are not always readily available. This paper argues that the unsupervised learning via Probabilistic Latent Semantic Analysis provides a more suitable machine learning approach for image annotation especially due to its potential to based categorisation on the latent semantic content of the image samples, which can bridge the semantic gap present in Content Based Image Retrieval. This paper therefore proposes an unsupervised image categorisation model in which the semantic content of images are discovered using Probabilistic Latent Semantic Analysis, after which they are clustered into unique groups based on semantic content similarities using K-means algorithm, thereby providing suitable annotation exemplars. A common problem with categorisation algorithms based on Bag-of-Visual Words modelling is the loss of accuracy due to spatial incoherency of the Bag-of-Visual Word modelling, this paper also examines the effectiveness of Spatial pyramid as a means of eliminating spatial incoherency in Probabilistic Latent Semantic Analysis classification.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116015302","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: 16

Regularized Least-Squares Coding with Unlabeled Dictionary for Image-Set Based Face Recognition 基于图像集人脸识别的无标签字典正则化最小二乘编码

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008128

M. Uzair, A. Mian

{"title":"Regularized Least-Squares Coding with Unlabeled Dictionary for Image-Set Based Face Recognition","authors":"M. Uzair, A. Mian","doi":"10.1109/DICTA.2014.7008128","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008128","url":null,"abstract":"Image set based face recognition provides more opportunities compared to single mug-shot face recognition. However, modelling the variations in an image set is a challenging task. We propose a computationally efficient and accurate image set modelling technique. The idea is to reconstruct each image set sample with an unlabeled dictionary using the computationally efficient regularized least squares. The reconstruction coefficients form a latent representation of an image set and efficiently model its underlying structure. We propose max and sum pooling to aggregate the latent representations into a single compact feature vector representation per set. We then perform Linear Discriminant Analysis on the pooled reconstruction coefficients to increase the discrimination and reduce the dimensionality of the proposed features. The proposed algorithm is extensively evaluated for the task of image set based face recognition on the Honda/UCSD, CMU Mobo and YouTube celebrities datasets. Experimental results show that the proposed algorithm outperforms current state-of-the-art image set classification algorithms in terms of both accuracy and execution time.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116287696","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

Rank Minimization or Nuclear-Norm Minimization: Are We Solving the Right Problem? 等级最小化还是核规范最小化:我们解决的问题对吗?

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-11-01 DOI: 10.1109/DICTA.2014.7008126

Yuchao Dai, Hongdong Li

{"title":"Rank Minimization or Nuclear-Norm Minimization: Are We Solving the Right Problem?","authors":"Yuchao Dai, Hongdong Li","doi":"10.1109/DICTA.2014.7008126","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008126","url":null,"abstract":"Low rank method or rank-minimization has received considerable attention from recent computer vision community. Due to the inherent computational complexity of rank problems, the non-convex rank function is often relaxed to its convex relaxation, i.e. the nuclear norm. Thanks to recent progress made in the filed of compressive sensing (CS), vision researchers who are practicing CS are fully aware, and conscious, of the convex relaxation gap, as well as under which condition (e.g. Restricted Isometry Property) the relaxation is tight (i.e. with nil gap). In this paper, we however wish to alert the potential users of the low-rank method that: focusing too much on the issue of relaxation gap and optimization may possibly adversely obscure the \"big picture'' of the original vision problem. In particular, this paper shows that for many commonly cited low-rank problems, nuclear norm minimization formulation of the original rank-minimization problem do not necessarily lead to the desired solution. Degenerate solutions and multiplicity seem often or always exist. Even if a certain nuclear-norm minimization solution is a provably tight relaxation, this solution can possibly be meaningless in its particular context. We therefore advocate that, in solving vision problems via nuclear norm minimization, special care must be given, and domain-dependent prior knowledge must be taken into account. This paper summarizes recent relevant theoretical results, provides original analysis, uses real examples to demonstrate the practical implications.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128281582","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