2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献_第3页

Semantic Segmentation of RGB-D Images Using 3D and Local Neighbouring Features 基于3D和局部邻近特征的RGB-D图像语义分割

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371307

F. Fooladgar, S. Kasaei

{"title":"Semantic Segmentation of RGB-D Images Using 3D and Local Neighbouring Features","authors":"F. Fooladgar, S. Kasaei","doi":"10.1109/DICTA.2015.7371307","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371307","url":null,"abstract":"3D scene understanding is one of the most important problems in the field of computer vision. Although, in the past decades, considerable attention has been devoted on the 2D scene understanding problem, now with the development of the depth sensors (like Microsoft Kinect), the 3D scene understanding has become a very challenging task. Traditionally, the scene understanding problem was considered as the semantic labeling of each image pixel. Semantic labeling of RGB-D images has not attained a comparable success, as the RGB semantic labeling, due to the lack of a challenging dataset. With the introduction of an RGB-D dataset, called NYU-V2, it became possible to propose a novel method to improve the labeling accuracy. In this paper, a semantic segmentation algorithm for RGB-D images is presented. The concentration of the proposed algorithm is on the feature description and classification steps. In the feature description step, the more discriminative features from RGB images and the 3D point cloud data are grouped with local neighboring features to incorporate their context into the classification step. In the classification step, a pairwise multi-class conditional random field framework is utilized in which the unary potential function is considered as the probabilistic output of a random forest classifier. The proposed algorithm is evaluated on the NYU-V2 dataset and the performance is compared to that of other methods presented in the literature. The proposed algorithm achieves the state-of-the-art results on the NYU-V2 dataset.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"49 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":"122514214","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: 5

An Analysis of Human Engagement Behaviour Using Descriptors from Human Feedback, Eye Tracking, and Saliency Modelling 使用来自人类反馈、眼动追踪和显著性建模的描述符分析人类参与行为

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371227

Pallab Kanti Podder, M. Paul, Tanmoy Debnath, M. Murshed

引用次数: 11

Robust Human Tracking to Occlusion in Crowded Scenes 拥挤场景中对遮挡的鲁棒人体跟踪

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371302

Hiromasa Takada, K. Hotta

引用次数: 1

Aerial Car Detection and Urban Understanding 空中汽车检测和城市理解

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371225

D. Kamenetsky, J. Sherrah

引用次数: 9

In Situ Leaf Classification Using Histograms of Oriented Gradients 基于定向梯度直方图的原位叶片分类

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371274

A. Olsen, Sung-Ji Han, Brendan Calvert, P. Ridd, Owen Kenny

引用次数: 30

Direct 6-DoF Pose Estimation from Point-Plane Correspondences 直接从点平面对应的六自由度姿态估计

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371253

K. Khoshelham

引用次数: 12

Track before Detect for Space Situation Awareness 跟踪前探测空间态势感知

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371316

S. Davey, T. Bessell, B. Cheung, M. Rutten

引用次数: 3

Camera Network Topology Estimation by Lighting Variation 基于光照变化的摄像机网络拓扑估计

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371245

M. Zhu, A. Dick, A. Hengel

引用次数: 3

Multi-Camera Tracking of Intelligent Targets with Hidden Reciprocal Chains 隐藏互反链智能目标的多相机跟踪

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371287

G. Stamatescu, A. Dick, L. White

引用次数: 7

Class-Semantic Textons with Superpixel Neighborhoods for Natural Roadside Vegetation Classification 基于超像素邻域的自然路边植被分类类语义文本

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2015-11-01 DOI: 10.1109/DICTA.2015.7371246

Ligang Zhang, B. Verma

{"title":"Class-Semantic Textons with Superpixel Neighborhoods for Natural Roadside Vegetation Classification","authors":"Ligang Zhang, B. Verma","doi":"10.1109/DICTA.2015.7371246","DOIUrl":"https://doi.org/10.1109/DICTA.2015.7371246","url":null,"abstract":"Accurate classification of roadside vegetation plays a significant role in many practical applications, such as vegetation growth management and fire hazard identification. However, relatively little attention has been paid to this field in previous studies, particularly for natural data. In this paper, a novel approach is proposed for natural roadside vegetation classification, which generates class- sematic color-texture textons at a pixel level and then makes a collective classification decision in a neighborhood of superpixels. It first learns two individual sets of bag-of-word visual dictionaries (i.e. class-semantic textons) from color and filter-bank texture features respectively for each object. The color and texture features of all pixels in each superpixel in a test image are mapped into one of the learnt textons using the nearest Euclidean distance, which are further aggregated into class probabilities for each superpixel. The class probabilities in each superpixel and its neighboring superpixels are combined using a linear weighting mixing, and the classification of this superpixel is finally achieved by assigning it the class with the highest class probability. Our approach shows higher accuracy than four benchmarking approaches on both a cropped region and an image datasets collected by the Department of Transport and Main Roads, Queensland, Australia.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"147 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":"128137437","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