- 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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A novel discriminant multiscale representation for ear recognition
摘要： This paper proposes a novel representation for ear recognition. It introduces a new alternative of binarised statistical image features based on multiscale framework. The proposed representation allows capturing the image content at multiple resolutions. The recognition accuracy can be enhanced by the following steps. First, for a given ear image, a set of multiscale response images are derived from the bank of binarised statistical image features (B-BSIF) filter. Second, the obtained response images are summarised by concatenating their histograms, which are obtained at each scale. Finally, a discriminative ear image representation is build by projecting the above mentioned histograms into a linear discriminant analysis subspace. The proposed representation is applied on three public databases: IIT Delhi-1, IIT Delhi-2 and USTB. The obtained recognition accuracy confirms its performance than the recent existing methods.
关键词： multi-resolution analysis,ear recognition,B-BSIF,WLDA,K-NN,whitened linear discriminant analysis
更新于2025-09-23 15:22:29
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A Fluorescence Sensing Determination of 2, 4, 6-Trinitrophenol Based on Cationic Water-Soluble Pillar[6]arene Graphene Nanocomposite
摘要： We describe a selective and sensitive fluorescence platform for the detection of trinitrophenol (TNP) based on competitive host–guest recognition between pyridine-functionalized pillar[6]arene (PCP6) and a probe (acridine orange, AO) that used PCP6-functionalized reduced graphene (PCP6-rGO) as the receptor. TNP is an electron-deficient and negative molecule, which is captured by PCP6 via electrostatic interactions and π–π interactions. Therefore, a selective and sensitive fluorescence probe for TNP detection is developed. It has a low detection limit of 0.0035 μM (S/N = 3) and a wider linear response of 0.01–5.0 and 5.0–125.0 for TNP. The sensing platform is also used to test TNP in two water and soil samples with satisfying results. This suggests that this approach has potential applications for the determination of TNP.
关键词： reduced graphene,trinitrophenol,host–guest recognition,cationic pillar[6]arene
更新于2025-09-23 15:22:29
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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Vehicle and Pedestrian Recognition Using Multilayer Lidar based on Support Vector Machine
摘要： Moving-object tracking (estimating position and velocity of moving objects) is a key technology for autonomous driving systems and driving assistance systems in mobile robotics and vehicle automation domains. To predict and avoid collisions, the tracking system has to recognize objects as accurately as possible. This paper presents a method for recognizing vehicles (cars and bicyclists) and pedestrians using multilayer lidar (3D lidar). Lidar data are clustered, and eight-dimensional features are extracted from each of clustered lidar data, such as distance from the lidar, velocity, object size, number of lidar-measurement points, and distribution of reflection intensities. A multiclass support vector machine is applied to classify cars, bicyclists, and pedestrians from these features. Experiments using “The Stanford Track Collection” data set allow us to compare the proposed method with a method based on the random forest algorithm and a conventional 26-dimensional feature-based method. The comparison shows that the proposed method improves recognition accuracy and processing time over the other methods. Therefore, the proposed method can work well under low computational environments.
关键词： multiclass classification,support vector machine,low-dimensional features,multilayer lidar,object recognition
更新于2025-09-23 15:22:29
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Visibility graphs for image processing
摘要： The family of image visibility graphs (IVG/IHVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such an operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.
关键词： image visibility graphs,image processing,pattern recognition,graph features,visibility patches,image classification
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Dynamic Hand Gesture Recognition Using FMCW Radar Sensor for Driving Assistance
摘要： Dynamic hand gesture recognition is very important for human-computer interaction. In vehicles, hand gesture recognition can be used as the driver's auxiliary system to achieve remote control of the instrument. To a certain extent, this system can avoid physical buttons and touch screens causing interference to the driver. In this paper, we describe a driver-assisted dynamic gesture recognition system to classify nine hand gestures based on micro-Doppler signatures obtained by 77GHz FMCW radar using a convolutional neural network (CNN). We further explore the changes in the accuracy of same gestures in a variety of experimental scenarios to help optimize the robustness of the system.
关键词： convolutional neural network,hand gesture recognition,driver assistance system,FMCW radar sensor
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 (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Fast Sparse Representation Method for SAR Target Configuration Recognition
摘要： Focusing on the problem of the real-time implementation in sparse representation (SR) based recognition algorithm, a fast sparse representation (FSR) algorithm is presented in this paper to improve the efficiency of synthetic aperture radar (SAR) target configuration recognition. Taking the inertia variance characteristic of SAR target images over a small range of azimuth angles into consideration, training samples of each configuration are averaged. Instead of using all the training samples to establish the dictionary in SR, the average samples are utilized to construct the dictionary in FSR. A small dictionary accelerates the speed of the proposed algorithm.
关键词： sparse representation (SR),Synthetic aperture radar (SAR) images,target configuration recognition
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 (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Adaptive Weighted Multi-Task Sparse Representation Classification in SAR Image Recognition
摘要： In this paper, a novel multi-task sparse representation (MSR) of the monogenic signal is proposed in order to overcome the misclassification caused by heterogeneity of three components of the monogenic signal. In recent years, the monogenic signal has been applied into the field of SAR image recognition due to its capability of capturing the broad spectral information with maximal spatial localization. The monogenic signal can be decomposed into three components (local amplitude, local phase and local orientation) at different scales. The components are concatenated to three component-specific features and then fed into a MSR classification framework. However, the heterogeneity of the three component-specific features makes it difficult to make decisions by simply counting the accumulated error in multi-task sparse representation classification. To solve this problem, a multi-task learning model based on Fisher discrimination criteria is designed and Fisher score is presented to measure the discriminative ability of three types of component-specific feature in different classes. The final decision is made by weighted accumulated reconstruction error. Experiment results prove the effectiveness of adaptive weighted MSR classification method of monogenic signal.
关键词： multi-task sparse representation,image recognition,SAR
更新于2025-09-23 15:22:29
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Nearest Centroid Neighbor Based Sparse Representation Classification for Finger vein Recognition
摘要： In this paper, an efficient finger vein recognition algorithm based on the combination of the nearest centroid neighbor and sparse representation classification techniques (kNCN-SRC) is presented. The previously proposed recognition algorithms are mainly based on distance computation. In the proposed method, the distance, as well as the spatial distribution, are considered to achieve a better recognition rate. The proposed method consists of two stages: first, the k nearest neighbors of the test sample are selected based on the nearest centroid neighbor and then in the second stage based on the selected number of closest nearest centroid neighbors (k) the test sample is classified by sparse representation. Findings from the proposed method kNCN-SRC demonstrated an increased recognition rate. This improvement can be attributed to the selection of the train samples, where the train samples are selected by considering the spatial and distance distribution. In addition, the complexity of SRC is reduced by reducing the number of train samples for classification of the test sample by sparse representation and the processing speed of the proposed algorithm is significantly improved in comparison to the conventional SRC which is due to the reduced number of training samples. It can be concluded that the kNCN-SRC classification method is efficient for finger vein recognition. An increase in the recognition rate of 3.35%, 9.07%, 20.23%, and 0.81% is obtained for the proposed kNCN-SRC method in comparison with the conventional SRC for the four tested public finger vein databases.
关键词： Finger vein recognition,Distance criterion,k-Nearest Centroid Neighbor,Spatial distribution,Sparse Representation Classification
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Mechatronics and Automation (ICMA) - Changchun (2018.8.5-2018.8.8)] 2018 IEEE International Conference on Mechatronics and Automation (ICMA) - Signal Recognition Method of X-ray Pulsar Based on Instantaneous Amplitude Feature
摘要： The X-ray pulsar signal recognition method based on instantaneous amplitude feature is proposed in this paper. The instantaneous amplitude of the X-ray pulsar signal after Hilbert transform is the main feature of the signal, and the signal recognition algorithm based on the minimum distance of the eigenvector is designed. In the simulation experiment, the feature samples of the pulsar navigation database are constructed. The results show that the proposed X-ray pulsar signal recognition algorithm is effective, which has the advantages of high reliability and fast recognition speed.
关键词： X-ray pulsar,signal recognition,instantaneous amplitude feature,Hilbert transform
更新于2025-09-23 15:22:29