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

Enhanced-IPMH as a Robust Visual Descriptor from H.264/AVC and Evaluation of Parameters Effects 基于H.264/AVC的增强ipmh鲁棒视觉描述符及其参数效果评价

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371254

A. Rouhi

{"title":"Enhanced-IPMH as a Robust Visual Descriptor from H.264/AVC and Evaluation of Parameters Effects","authors":"A. Rouhi","doi":"10.1109/DICTA.2015.7371254","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371254","url":null,"abstract":"Intra-prediction Modes-based (IPM-based) descriptors are among robust and competitive visual descriptors for near-duplicate video similarity detection, in general and content-based copy detection (CCD), in particular. IPM-based descriptors are extracted from the compressed H.264/AVC (MPEG-4/AVC) video domain. Intra-prediction Modes (IPM) are the building blocks of the key frames (I and IDR slices) in the H.264/AVC video standard. IPM-based descriptors are generally constructed based on the probability distribution of the unified intra-prediction modes of the key frames. The current research introduce an enhanced version of IPM-Histogram (IPMH) with 10 bins, which is called enhanced-IPMH (e-IPMH). This research conducted using a subset of TRECVID/CCD (2011), dataset and TREC-EVAL-Video software to compute the performance measures. Based on the experimental evidences, the e-IPMH is an effective and inexpensive visual feature, compared to the pixel domain global descriptors. Analysing the experimental results of the e-IPMH, compared to its predecessor, IPMH shows improvement in the performance measures: Mean Reciprocal Rank (MRR) and Precision@1. However, its mean processing time, reveals it is slower compared to IPMH, due to its larger descriptor size. The current research also conducted a series of experiments to evaluate the effect of spatio-temporal parameters on IPM-based descriptors. The scope of the experiments are limited to the content-preserving visual distortions: T3, T4, T5 and T6 which are the functional scope of global visual descriptors.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"17 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":"122791637","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

An Automatic Algorithm for Tracking Small Intestine in CT Enterography CT小肠造影中小肠的自动跟踪算法

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371228

J. Horáček, M. Horák, J. Kolomazník, J. Pelikán

引用次数: 1

Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance 交通监控中车辆外观自动识别的无监督处理

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371318

Jakub Sochor, A. Herout

{"title":"Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance","authors":"Jakub Sochor, A. Herout","doi":"10.1109/DICTA.2015.7371318","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371318","url":null,"abstract":"This paper deals with unsupervised collection of information from traffic surveillance video streams. Deployment of usable traffic surveillance systems requires minimizing of efforts per installed camera - our goal is to enroll a new view on the street without any human operator input. We propose a method of automatically collecting vehicle samples from surveillance cameras, analyze their appearance and fully automatically collect a fine-grained dataset. This dataset can be used in multiple ways, we are explicitly showcasing the following ones: fine-grained recognition of vehicles and camera calibration including the scale. The experiments show that based on the automatically collected data, make&model vehicle recognition in the wild can be done accurately: average precision 0.890. The camera scale calibration (directly enabling automatic speed and size measurement) is twice as precise as the previous existing method. Our work leads to automatic collection of traffic statistics without the costly need for manual calibration or make&model annotation of vehicle samples. Unlike most previous approaches, our method is not limited to a small range of viewpoints (such as eye-level cameras shots), which is crucial for surveillance applications.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"27 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":"114313742","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

Image Analysis-Based Automatic Utility Pole Detection for Remote Surveillance 基于图像分析的电线杆远程监控自动检测

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371267

Hrishikesh Sharma, Adithya Vellaiappan, Tanima Dutta, P. Balamuralidhar

{"title":"Image Analysis-Based Automatic Utility Pole Detection for Remote Surveillance","authors":"Hrishikesh Sharma, Adithya Vellaiappan, Tanima Dutta, P. Balamuralidhar","doi":"10.1109/DICTA.2015.7371267","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371267","url":null,"abstract":"In case of disasters such as cyclones, earthquakes, severe floods etc., widespread damages to infrastructures such as power grid, communication infrastructure etc. is commonplace. Especially to power grid, the damages to various structures are typically spread out in wide areas. Usage of drones to do fast remote survey of damage area is gaining popularity. From the remote surveillance video of any wide disaster area that is fairly long, it is important to extract keyframes that contain specific component structures of the power grid. The keyframes can then be analyzed for possible damage to the specific structure. In this context, we present an algorithm for automated detection of utility poles. Specifically, we show robust detection of poles in frames of videos available from various sources. The detection is performed by first extracting 2D shapes of poles as analytically defined geometric shape, quadrilateral, whose edges exhibit noise corruption. A pole is then detected as a shape-based template, where one long rectangular trapezium, is perpendicularly intersected by at least one trapezium representing a crossarm that suspends the conductors. Via testing and comparison, our algorithm is shown to be more robust as compared to other approaches, especially against highly variable background. We believe such detection, with limited false negatives, will form stepping stone towards future detection of damages in utility poles.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"106 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":"121741174","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: 7

Skin Hair Removal for 2D Psoriasis Images 皮肤脱毛的二维牛皮癣图像

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371308

Y. George, M. Aldeen, R. Garnavi

{"title":"Skin Hair Removal for 2D Psoriasis Images","authors":"Y. George, M. Aldeen, R. Garnavi","doi":"10.1109/DICTA.2015.7371308","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371308","url":null,"abstract":"Presence of hair in psoriasis skin images may adversely affect the extraction of the features required for computer aided analysis, thus compromise the detection and diagnostic results. Therefore, for the diagnosis of psoriasis to be accurate, it is vitally important to remove hair, if it exists, from images in the preprocessing stage. This paper presents, for the first time, a hair detection and removal algorithm for 2D psoriasis images. The hair removal process starts with a markers removal algorithm, where the shape features are extracted from the binary input image. The outcome of this step is removal of all objects that obscure the image lesions such that the output image contains psoriasis lesions and normal skin only. Next, the dark hair in the skin is identified using contrast enhancement method and morphological operations. Finally, image interpolation is performed to replace the hair pixels with hair free neighbouring pixels values through image inpainting. The proposed algorithm is tested on 64 psoriasis images acquired from the Royal Melbourne Hospital, Victoria, Australia. Experimental results demonstrate that the algorithm is highly accurate and effective. In addition, the widely used hair removal software DullRazor® is used on the same 64 images for comparison. The results show that our proposed algorithm performs quite well and is more adapt to psoriasis images. The method is more effective because it overcomes the problem of removing skin hair without affecting the intensity or texture features of the lesions.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"21 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":"127414558","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: 18

Monocular Human Motion Tracking with Non-Connected Body Part Dependency 非连接身体部位依赖的单眼人体运动跟踪

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371283

Jinglan Tian, Ling Li, Wanquan Liu

{"title":"Monocular Human Motion Tracking with Non-Connected Body Part Dependency","authors":"Jinglan Tian, Ling Li, Wanquan Liu","doi":"10.1109/DICTA.2015.7371283","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371283","url":null,"abstract":"2D articulated human pose tracking in monocular image sequences remains an extremely challenging task due to background cluttering, variation in body appearance, occlusion and imaging conditions. Most of the current approaches only deal with simple appearance and adjacent or connected body part dependencies, especially the tree-structured priors assumed over body part connections. Such prior excludes the dependencies between non-connected body parts which could actually contribute to tracking accuracies. Building on the successful pictorial structures model, we propose a novel framework for human pose tracking including more dependencies of non-connected body parts. In order to implement inference efficiently for the proposed model, we introduce a factor graph to factorize all the unary term and all dependencies that are modelled in the pairwise term of the proposed model. In this paper, we also observe that the posterior marginals of each part from the tree structure model satisfy a Gaussian distribution. Based on this property, the sampling procedure becomes straight-forward and the search space can be shrunk effectively. We incorporate a simple motion constraint to capture the temporal continuity of body parts between frames, since the positions/orientations of body parts usually change smoothly between consecutive frames. In addition, we introduce a full body detector as the first step of our framework to reduce the search space for pose tracking. We also exploit the temporal continuity of body parts between frames by incorporating constraints on the location distance and the orientation difference for each body part between two successive frames. We evaluate our framework on two challenging image sequences and conduct a series of experiments to compare the performance with the approaches based on the tree-based model. The results illustrate that the proposed framework improves the performance significantly.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"17 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":"117031697","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

Non-Local Noise Estimation for Adaptive Image Denoising 自适应图像去噪中的非局部噪声估计

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371290

M. Hanif, A. Seghouane

引用次数: 0

A Locally Constrained Random Walk Approach for Airway Segmentation of Low-Contrast Computed Tomography (CT) Image 低对比度CT图像气道分割的局部约束随机游走方法

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371216

Robin Huang, Lei Bi, Changyang Li, Younhyun Jung, Jinman Kim, M. Fulham, D. Feng

{"title":"A Locally Constrained Random Walk Approach for Airway Segmentation of Low-Contrast Computed Tomography (CT) Image","authors":"Robin Huang, Lei Bi, Changyang Li, Younhyun Jung, Jinman Kim, M. Fulham, D. Feng","doi":"10.1109/DICTA.2015.7371216","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371216","url":null,"abstract":"Positron emission tomography (PET) combined with computed tomography (CT) is a routine imaging modality for the diagnosis and interpretation of malignant diseases of the thorax. Accurate airway segmentation is critical for the localization of sites of abnormal metabolism detected with PET-CT. The vast majority of published segmentation algorithms, however, are designed for high- resolution CT and these algorithms do not perform well with the low-contrast CT acquired in PET-CT images. In this study, we present a new fully automated airway segmentation algorithm that is optimised to tolerate the image characteristics inherent in low-contrast CT images. Our algorithm accurately and robustly segments the airway by introducing: (i) a robust multi-atlas initialisation which incorporates shape priori knowledge for seeds derivation; and (ii) a modified knowledge-based random walk segmentation that uses the derived seeds and manipulates the weights of the edge paths in a locally constrained search space. Our proposed algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance in segmentation results against comparative state-of- the-art algorithms.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"104 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":"124071798","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

Implementation of Gaussian and Box Kernel Based Approximation of Bilateral Filter Using OpenCL 利用OpenCL实现基于高斯和盒核的双边滤波器逼近

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371269

Honey Gupta, Daniel Sanju Antony, N. RathnaG.

引用次数: 4

A Model Integrating Fire Prediction and Detection for Rural-Urban Interface 城乡结合部火灾预测与探测集成模型

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371217

N. Alamgir, W. Boles, V. Chandran

引用次数: 5