- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - FACE - Face At Classroom Environment: Dataset and Exploration
摘要： The rapid development in face detection study has been greatly supported by the availability of large image datasets, which provide detailed annotations of faces on images. However, among a number of publicly accessible datasets, to our best knowledge, none of them are specifically created for academic applications. In this paper, we propose a systematic method in forming an image dataset tailored for classroom environment. We also made our dataset and its exploratory analyses publicly available. Studies in computer vision for academic application, such as an automated student attendance system, would benefit from our dataset.
关键词： image dataset,face recognition,face detection,computer vision,data collection,educational data mining,automated attendance system
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
摘要： In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The key contributions are as follows. We present a novel dataset based on Sentinel-2 satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Networks (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards various Earth observation applications. We demonstrate how the classification system can assist in improving geographical maps.
关键词： Deep Learning,Land Use Classification,Earth Observation,Convolutional Neural Network,Machine Learning,Dataset,Land Cover Classification
更新于2025-09-23 15:21:21
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Human Skin Segmentation Using Fully Convolutional Neural Networks
摘要： In recent years, skin segmentation has attracted much of attention from computer vision field. Normally, researchers use a simple pre-trained model or define a fixed threshold in color space to deal with skin segmentation. However, it is highly possible to failure in many conditions. In addition, convolutional neural network (CNN) has achieved great success in computer vision. This paper we present a fully convolutional neural network method in skin segmentation. A hand-crafted skin dataset has provided in this study. In the experiment, we attempt many CNN structures to determine the best one. According to the experimental result, we obtained a considerable result in three well-known skin datasets.
关键词： deep learning,skin segmentation,Convolutional neural network,human skin dataset
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE Intelligent Vehicles Symposium (IV) - Paris, France (2019.6.9-2019.6.12)] 2019 IEEE Intelligent Vehicles Symposium (IV) - DeLiO: Decoupled LiDAR Odometry
摘要： Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion. In this paper, we propose our Decoupled LiDAR Odometry (DeLiO), which – for the first time – decouples the rotation estimation completely from the translation estimation. In particular, the rotation is estimated by extracting the surface normals from the input point clouds and tracking their characteristic pattern on a unit sphere. Using this rotation the point clouds are unrotated so that the underlying transformation is pure translation, which can be easily estimated using a line cloud approach. An evaluation is performed on the KITTI dataset and the results are compared against state-of-the-art algorithms.
关键词： decoupled rotation and translation estimation,KITTI dataset,line cloud approach,LiDAR odometry,unit sphere,surface normals
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE Intelligent Vehicles Symposium (IV) - Changshu (2018.6.26-2018.6.30)] 2018 IEEE Intelligent Vehicles Symposium (IV) - The ParallelEye-CS Dataset: Constructing Artificial Scenes for Evaluating the Visual Intelligence of Intelligent Vehicles
摘要： Of?inetrainingandtestingareplayinganessen-tialroleindesignandevaluationofintelligentvehiclevisionalgorithms.Nevertheless,long-terminconvenienceconcerningtraditionalimagedatasetsisthatmanuallycollectingandan-notatingdatasetsfromrealsceneslacktestingtasksanddiverseenvironmentalconditions.Forthatvirtualdatasetscanmakeupfortheseregrets.Inthispaper,weproposetoconstructarti?cialscenesforevaluatingthevisualintelligenceofintelligentvehiclesandgenerateanewvirtualdatasetcalled“ParallelEye-CS”.Firstofall,theactualtrackmapdataisusedtobuild3DscenemodelofChineseFlagshipIntelligentVehicleProvingCenterArea,Changshu.Then,thecomputergraphicsandvirtualrealitytechnologiesareutilizedtosimulatethevirtualtestingtasksaccordingtotheChineseIntelligentVehiclesFutureChallenge(IVFC)tasks.Furthermore,theUnity3Dplatformisusedtogenerateaccurateground-truthlabelsandchangeenvironmentalconditions.Asaresult,wepresentaviableimplementationmethodforconstructingarti?cialscenesfortraf?cvisionresearch.Theexperimentalresultsshowthatourmethodisabletogeneratephotorealisticvirtualdatasetswithdiversetestingtasks.
关键词： artificial scenes,intelligent vehicles,ParallelEye-CS,visual intelligence,virtual dataset
更新于2025-09-11 14:15:04
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[ACM Press the 2nd International Conference - Chengdu, China (2018.06.16-2018.06.18)] Proceedings of the 2nd International Conference on Advances in Image Processing - ICAIP '18 - Aerial Object Tracking Dataset
摘要： Visual object tracking is one of the most significant research areas in computer vision. Many trackers have been proposed and achieved great success in recent years. Accompanying these excellent trackers, many datasets of image sequences and methods for evaluating them have also been put forward. However, we can hardly find a set of sequences which is specially regard to air targets even if it is very vital in civil aviation as well as in military domain, especially in air defence tasks. On the account of this, a series of aerial object image sequences with different attributes have been collected from the internet and made out to dataset in the form of OTB-100 Sequences, which is a very famous dataset for generic target tracking.
关键词： performance evaluation,Aerial object tracking,dataset
更新于2025-09-11 14:15:04
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A New Dataset for Source Identification of High Dynamic Range Images
摘要： Digital source identi?cation is one of the most important problems in the ?eld of multimedia forensics. While Standard Dynamic Range (SDR) images are commonly analyzed, High Dynamic Range (HDR) images are a less common research subject, which leaves space for further analysis. In this paper, we present a novel database of HDR and SDR images captured in different conditions, including various capturing motions, scenes and devices. As a possible application of this dataset, the performance of the well-known reference pattern noise-based source identi?cation algorithm was tested on both kinds of images. Results have shown dif?culties in source identi?cation conducted on HDR images, due to their complexity and wider dynamic range. It is concluded that capturing conditions and devices themselves can have an impact on source identi?cation, thus leaving space for more research in this ?eld.
关键词： source identi?cation,image forensics,multimedia forensics,HDR,dataset
更新于2025-09-10 09:29:36
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Getting to know low-light images with the Exclusively Dark dataset
摘要： Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light imagery have seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as a benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought. It consists exclusively of low-light images captured in visible light only, with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing the visualizations of both hand-crafted and learned features. We found that the effects of low-light reach far deeper into the features than can be solved by simple “illumination invariance”. It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The dataset can be downloaded at https://github.com/cs-chan/Exclusively-Dark-Image-Dataset.
关键词： feature analysis,Low-light images,dataset,object detection,illumination invariance
更新于2025-09-10 09:29:36
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Classification via weighted kernel CNN: application to SAR target recognition
摘要： The conventional convolutional neural network (CNN) has proven to be effective for synthetic aperture radar (SAR) target recognition. However, the relationship between different convolutional kernels is not taken into account. The lack of the relationship limits the feature extraction capability of the convolutional layer to a certain extent. To address this problem, this paper presents a novel method named weighted kernel CNN (WKCNN). WKCNN integrates a weighted kernel module (WKM) into the common CNN architecture. The WKM is proposed to model the interdependence between different kernels, and thus to improve the feature extraction capability of the convolutional layer. The WKM consists of variables and activations. The variable represents the weight of the convolutional kernel. The activation is a mapping function which is used to determine the range of the weight. To adjust the variable adaptively, back propagation (BP) algorithm for the WKM is derived. The training of the WKM is driven by optimizing the cost function according to the BP algorithm, and three training modes are presented and analysed. SAR target recognition experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset, and the results show the superiority of the proposed method.
关键词： SAR target recognition,weighted kernel module,convolutional neural network,back propagation algorithm,MSTAR dataset
更新于2025-09-10 09:29:36
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Roma, Italy (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Abnormal Behavior Detection in Crowded Scenes Using Density Heatmaps and Optical Flow
摘要： Crowd behavior analysis is an arduous task due to scale, light and crowd density variations. This paper aims to develop a new method that can precisely detect and classify abnormal behavior in dense crowds. A two-stream network is proposed that uses crowd density heat-maps and optical ?ow information to classify abnormal events. Work on this network has highlighted the lack of large scale relevant datasets due to the fact that dealing and annotating such kind of data is a highly time consuming and demanding task. Therefore, a new synthetic dataset has been created using the Grand Theft Auto V engine which offers highly detailed simulated crowd abnormal behaviors.
关键词： crowded scenes,density heatmaps,synthetic dataset,abnormal behavior detection,optical flow,two-stream network
更新于2025-09-04 15:30:14