- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles
摘要： Environment maps must ?rst be generated to drive mobile robots automatically. Path planning is performed based on the information given in an environment map. Various types of sensors, such as ultrasonic and laser sensors, are used by mobile robots to acquire data on its surrounding environment. Among these, the laser sensor, which has the property of being able to go straight and high accuracy, is used most often. However, the beams from laser sensors are refracted and re?ected when it meets a transparent obstacle, thus generating noise. Therefore, in this paper, a state-of-the-art algorithm was proposed to detect transparent obstacles by analyzing the pattern of the re?ected noise generated when a laser meets a transparent obstacle. The experiment was carried out using the environment map generated by the aforementioned method and gave results demonstrating that the robot could avoid transparent obstacles while it was moving towards the destination.
关键词： re?ection noise,path planning,mobile robot,transparent obstacle recognition,laser range ?nder
更新于2025-09-23 15:21:01
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The intelligent vehicle target recognition algorithm based on target infrared features combined with lidar
摘要： The intelligent vehicle target detection system can sense and recognize the surrounding pedestrians, vehicles and other objects through sensors, which is the basis for achieving intelligent vehicle unmanned driving. The laser imaging radar actively emits laser light and receives its reflected echo, which can form an angle-angle-distance-intensity image, making it easier to realize target recognition. The combination of the lidar and the infrared characteristics of the target can obtain more information and improve target recognition and anti-interference ability. In order to achieve fast and accurate moving target detection in a complex battlefield environment, this paper studies lidar imaging and target infrared features, as well as intelligent vehicle target detection, and proposes a target recognition method that combines target infrared features and lidar. Compensation makes it difficult to describe the disadvantages of moving targets in a single source data. The experimental results show that the laser and infrared fusion detection algorithm does not increase the complexity of the algorithm, which greatly improves the adaptability and robustness of the vehicle target detection algorithm, and improves the accuracy of the measurement detection algorithm.
关键词： Target recognition,Image fusion,Infrared feature,Infrared radar
更新于2025-09-23 15:21:01
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[IEEE SoutheastCon 2018 - St. Petersburg, FL (2018.4.19-2018.4.22)] SoutheastCon 2018 - RGBD-Sphere SLAM
摘要： This article proposes a SLAM algorithm referred to as RGBD-Sphere SLAM. The key innovation of this work is the prototypical system that demonstrates how formal models of 3D geometric shape and appearance can be transformed into generative classification models that detect and recognize these shapes. Object models are specified as shape programs in PSML; a custom-built procedural language for 3D object modeling. Classifiers for each PSML shape are created by simulating how instances of each shape manifest in real-world sensor data, e.g., color images and range images. The proposed RGBD-Sphere SLAM algorithm demonstrates a prototypical example of the PSML program specifies spherical 3D objects having diffuse surface albedos and distinct color appearances. A recognizer uses PSML models of each object’s geometry and appearance to detect instances of these objects within streaming RGBD sensor data. The detected model parameters are then integrated into an RGBD SLAM algorithm; hence the name RGBD-Sphere SLAM. This article describes the PSML programs, the spherical detection and recognition algorithms used, and describes the impact this approach has for improving the performance of RGBD SLAM approaches by incorporating detected objects as landmarks. This is the first example of a prototypical system that externalizes the geometric and appearance modeling to a programming language from which a recognizer is created, and marks an important step towards enabling users to “program” their problem space and allow computers to transform the formal object models, as expressed in PSML, into customized classifiers suited for specific sensor suites, e.g., color imagery and depth imagery.
关键词： object recognition,RGBD,SLAM,3D object modeling,PSML
更新于2025-09-23 15:21:01
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Human sensitivity to perturbations constrained by a model of the natural image manifold
摘要： Humans are remarkably well tuned to the statistical properties of natural images. However, quantitative characterization of processing within the domain of natural images has been difficult because most parametric manipulations of a natural image make that image appear less natural. We used generative adversarial networks (GANs) to constrain parametric manipulations to remain within an approximation of the manifold of natural images. In the first experiment, seven observers decided which one of two synthetic perturbed images matched a synthetic unperturbed comparison image. Observers were significantly more sensitive to perturbations that were constrained to an approximate manifold of natural images than they were to perturbations applied directly in pixel space. Trial-by-trial errors were consistent with the idea that these perturbations disrupt configural aspects of visual structure used in image segmentation. In a second experiment, five observers discriminated paths along the image manifold as recovered by the GAN. Observers were remarkably good at this task, confirming that observers are tuned to fairly detailed properties of an approximate manifold of natural images. We conclude that human tuning to natural images is more general than detecting deviations from natural appearance, and that humans have, to some extent, access to detailed interrelations between natural images.
关键词： natural images,image recognition,noise perturbations,artificial neural networks,generative adversarial nets
更新于2025-09-23 15:21:01
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Canonical Correlation Analysis Regularization: An Effective Deep Multi-View Learning Baseline for RGB-D Object Recognition
摘要： Object recognition methods based on multi-modal data, color plus depth (RGB-D), usually treat each modality separately in feature extraction, which neglects implicit relations between two views and preserves noise from any view to the ?nal representation. To address these limitations, we propose a novel Canonical Correlation Analysis (CCA)-based multi-view Convolutional Neural Network (CNNs) framework for RGB-D object representation. The RGB and depth streams process corresponding images respectively, then are connected by CCA module leading to a common-correlated feature space. In addition, to embed CCA into deep CNNs in a supervised manner, two different schemes are explored. One considers CCA as a regularization term adding to the loss function (CCAR). However, solving CCA optimization directly is neither computationally ef?cient nor compatible with the mini-batch based stochastic optimization. Thus, we further propose an approximation method of CCA regularization (ACCAR), using the obtained CCA projection matrices to replace the weights of feature concatenation layer at regular intervals. Such a scheme enjoys bene?ts of full CCA regularization and is ef?cient by amortizing its cost over many training iterations. Experiments on benchmark RGB-D object recognition datasets have shown that the proposed methods outperform most existing methods using the very same of their network architectures.
关键词： Deep learning,Canonical Correlation Analysis,Multi-view feature learning,RGB-D object recognition
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Scene Recognition with Convolutional Residual Features via Deep Forest
摘要： Convolutional Neural Networks (CNNs) have made remarkable progress on image classification and other relative computer vision filed, which need large-scale data for training. In this paper, a method named DFCRF (Deep Forest with Convolutional Residual Features) is proposed. It is based on the gcForest proposed by Zhou and Feng. And we use a recent released AI Challenger dataset, containing around only 50,000 images mainly captured in China. Different from utilizing only CNNs, we use convolutional residual features for further recognition, followed by gradient-based XGBoost and cascade deep forest. Then, we conduct extensive experiments on the AI Challenger dataset and reconstructed Places2 dataset to show the effectiveness of our method.
关键词： deep forest,scene recognition,AI challenger,convolutional neural network
更新于2025-09-23 15:21:01
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Gestalt Interest Points with a Neural Network for Makeup-Robust Face Recognition
摘要： In this paper, we propose a novel approach for the domain of makeup-robust face recognition. Most face recognition schemes usually fail to generalize well on these data where there is a large difference between the training and testing sets, e.g., makeup changes. Our method focuses on the problem of determining whether face images before and after makeup refer to the same identity. The work on this fundamental research topic benefits various real-world applications, for example automated passport control, security in general, and surveillance. Experiments show that our method is highly effective in comparison to state-of-the-art methods.
关键词： CNN,Face recognition,makeup-robust,GIP,person identification
更新于2025-09-23 15:21:01
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[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Optimal Approach for Image Recognition Using Deep Convolutional Architecture
摘要： In the recent time, deep learning has achieved huge popularity due to its performance in various machine learning algorithms. Deep learning as hierarchical or structured learning attempts to model high-level abstractions in data by using a group of processing layers. The foundation of deep learning architectures is inspired by the understanding of information processing and neural responses in human brain. The architectures are created by stacking multiple linear or nonlinear operations. The article mainly focuses on the state-of-the-art deep learning models and various real-world application-speci?c training methods. Selecting optimal architecture for speci?c problem is a challenging task; at a closing stage of the article, we proposed optimal approach to deep convolutional architecture for the application of image recognition.
关键词： Deep neural networks,Image recognition,Image processing,Transfer learning,Convolutional neural networks,Deep learning
更新于2025-09-23 15:21:01
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Atomic Force Microscopy in Molecular and Cell Biology || AFM Imaging-Force Spectroscopy Combination for Molecular Recognition at the Single-Cell Level
摘要： Molecular recognition at the single-cell level is an increasingly important issue in Biomedical Sciences. With atomic force microscopy, cell surface receptors may be recognized through the interaction with their ligands, inclusively for the identification of cell-cell adhesion proteins. The spatial location of a specific interaction can be determined by adhesion force mapping, which combines topographic images with local force spectroscopy measurements. Another valuable possibility is to simultaneously record topographic and recognition images (TREC imaging) of cells, enabling the mapping of specific binding events on cells in real time. This review is focused on recent developments on these molecular recognition approaches, presenting examples of different biological and biomedical applications.
关键词： Molecular recognition,TREC imaging,Atomic force microscopy,Adhesion force mapping,Biomedical applications
更新于2025-09-23 15:21:01
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A new method of mark detection for software-based optical mark recognition
摘要： Software optical mark recognition (SOMR) is the process whereby information entered on a survey form or questionnaire is converted using specialized software into a machine-readable format. SOMR normally requires input fields to be completely darkened, have no internal labels, or be filled with a soft pencil, otherwise mark detection will be inaccurate. Forms can also have print and scan artefacts that further increase the error rate. This article presents a new method of mark detection that improves over existing techniques based on pixel counting and simple thresholding. Its main advantage is that it can be used under a variety of conditions and yet maintain a high level of accuracy that is sufficient for scientific applications. Field testing shows no software misclassification in 5695 samples filled by trained personnel, and only two misclassifications in 6000 samples filled by untrained respondents. Sensitivity, specificity, and accuracy were 99.73%, 99.98%, and 99.94% respectively, even in the presence of print and scan artefacts, which was superior to other methods tested. A separate direct comparison for mark detection showed a sensitivity, specificity, and accuracy respectively of 99.7%, 100.0%, 100.0% (new method), 96.3%, 96.0%, 96.1% (pixel counting), and 99.9%, 99.8%, 99.8% (simple thresholding) on clean forms, and 100.0%, 99.1%, 99.3% (new method), 98.4%, 95.6%, 96.2% (pixel counting), 100.0%, 38.3%, 51.4% (simple thresholding) on forms with print artefacts. This method is designed for bubble and box fields, while other types such as handwriting fields require separate error control measures.
关键词： Software optical mark recognition,pixel counting,mark detection,print and scan artefacts,simple thresholding
更新于2025-09-23 15:21:01