- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Automatic robot path integration using three-dimensional vision and offline programming
摘要： In manufacturing industries, offline programming (OLP) platforms provide an independent methodology for robot integration using 3D model simulation away from the actual robot cell and production process, reducing integration time and costs. However, traditional OLP platforms still require prior knowledge of the workpiece position in a predefined environment, which requires complex human operations and specific-purpose designs, highly reducing the autonomy of the systems. The presented approach proposes to overcome these problems by defining a novel automated offline programming system (AOLP), which integrates a flexible and intuitive OLP platform with a state-of-the-art autonomous object pose estimation method, to achieve an environment and model independent platform for automatic robotic manufacturing. The autonomous recognition capabilities of the three-dimensional vision system provide the relative position of the workpiece model in the OLP platform, with robustness against clutter, illumination, and object material. After that, the user-friendly OLP platform allows an efficient and automatic path generation, simulation, robot code generation, and robot execution. The proposed system precision and robustness are analyzed and validated in a real-world environment on four different sets of experiment. Finally, the proposed system's features are discussed and compared with other available solutions for practical industrial manufacturing, showing the advantages of the proposed approach. Overall, despite sensor resolution limitations, the proposed system shows a remarkable precision and promising direction towards highly efficient and productive manufacturing solutions.
关键词： Machine vision,Path generation,Industrial manipulator,Automated offline programming,3D object recognition,6D pose estimation
更新于2025-09-23 15:22:29
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A Novel Regularized Nonnegative Matrix Factorization for Spectral-Spatial Dimension Reduction of Hyperspectral Imagery
摘要： Dimension reduction (DR) is an essential preprocessing for hyperspectral image (HSI) classification. Recently, nonnegative matrix factorization (NMF) has been shown as an effective tool for the DR of hyperspectral data given the fact that it provides interpretable results. However, the basic NMF ignores the geometric structure information of the HSI data, thus limiting its performance. To this end, a novel regularized NMF method, termed NMF with adaptive graph regularizer (NMFAGR), is proposed for the spectral-spatial dimension reduction of hyperspectral data in this paper. Specifically, to enhance the preservation ability of the geometric structure information, the NMFAGR performs the dimension reduction and graph learning simultaneously. Regarding the mutual correlation between these two tasks, a graph regularizer is added as an interaction. Moreover, to effectively utilize complementary information among spectral-spatial features, the NMFAGR allocates feature weight factors automatically without requiring any additional parameters. An efficient algorithm is utilized to solve the optimization problem. The effectiveness of the proposed method is demonstrated on three benchmark hyperspectral data sets through experimentation.
关键词： Hyperspectral images,feature extraction,pattern recognition
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM) - Shanghai, China (2018.11.24-2018.11.26)] 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM) - Image Preprocessing of Iris Recognition
摘要： The aim of this paper is to propose the methods for image preprocessing of image enhancement and boundary detection. Iris recognition has been widely considered as one of the most dependable identification method. However, the iris systems are still not widespread due to many factors, for example, the production cost, the processing time and the recognition rate. The problems of production cost and the processing time will be resolved with the development of integrate circuit technology. The problem of recognition rate mentioned here is not about the iris itself, but the acquisition of the effective image of the iris. The quality of the iris image has become the key point of the current iris system. The preprocessing of iris recognition involves hardware and software design of the system and in this paper both of the designs are discussed.
关键词： Hough transform,iris recognition,image preprocessing,histogram equalization
更新于2025-09-23 15:22:29
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[IEEE 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM) - Erlagol (2018.6.29-2018.7.3)] 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM) - The Problem of Biometric Identification of a Subject and Subject's Changed State: Perspectives of New Features Application in Analysis of Face and Neck Thermograms
摘要： The analysis of the current state in the field of subjects biometric identification is provided. The main methods of biometric identification, the process of features space formation used subsequently for decision-making on the subject's access to system resources, as well as new approaches to the use of biometric images for protection of data on electronic documents are considered. The new problem of identification of subject’s changed state, and perspectives of using subjects’ thermal images for the purpose of determining changed state, is discussed.
关键词： Pattern recognition,biometric identification,face thermal images,encryption key,user’s variant state detection
更新于2025-09-23 15:22:29
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[IEEE 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) - Niagara Falls, NY, USA (2018.8.5-2018.8.8)] 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) - Separating Optical and Language Models Through Encoder-Decoder Strategy for Transferable Handwriting Recognition
摘要： Lack of data can be an issue when beginning a new study on historical handwritten documents. To deal with this, we propose a deep-learning based recognizer which separates the optical and the language models in order to train them separately using different resources. In this work, we present the optical encoder part of a multilingual transductive transfer learning applied to historical handwriting recognition. The optical encoder transforms the input word image into a non-latent space that depends only on the letter-n-grams: it enables it to be independent of the language. This transformation avoids embedding a language model and operating the transfer learning across languages using the same alphabet. The language decoder creates from a vector of letter-n-grams a word as a sequence of characters. Experiments show that separating optical and language model can be a solution for multilingual transfer learning.
关键词： Optical model,Language model,knowledge transfer,Handwriting recognition
更新于2025-09-23 15:22:29
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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) - Sketchpointnet: A Compact Network for Robust Sketch Recognition
摘要： Sketch recognition is a challenging image processing task. In this paper, we propose a novel point-based network with a compact architecture, named SketchPointNet, for robust sketch recognition. Sketch features are hierarchically learned from three miniPointNets, by successively sampling and grouping 2D points in a bottom-up fashion. SketchPointNet exploits both temporal and spatial context in strokes during point sampling and grouping. By directly consuming the sparse points, SketchPointNet is very compact and efficient. Compared with state-of-the-art techniques, SketchPointNet achieves comparable performance on the challenging TU-Berlin dataset while it significantly reduces the network size.
关键词： point set,stroke pattern,Sketch recognition,deep neural network
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 8th International Conference on Consumer Electronics - Berlin - Berlin (2018.9.2-2018.9.5)] 2018 IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin) - Activity monitoring from RGB input for indoor action recognition systems
摘要： In this work we present how some state-of-the-art action recognition techniques can be tailored to monitoring indoor human activities. At first, we analyze the most relevant algorithms and eventually we present an effective implementation based on the solely RGB input. Our preliminary results show that the proposed solution achieves competitive results with respect to other methods in the state of the art that take also advantage of multiple inputs.
关键词： Action recognition,tracking
更新于2025-09-23 15:22:29
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Evaluating Feature Extractors and Dimension Reduction Methods for Near Infrared Face Recognition Systems
摘要： This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPDA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments.
关键词： comparative study,undecimated discrete wavelet transform,Face recognition,near infrared,Zernike moments
更新于2025-09-23 15:22:29
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Application research of image recognition technology based on CNN in image location of environmental monitoring UAV
摘要： UAV remote sensing has been widely used in emergency rescue, disaster relief, environmental monitoring, urban planning, and so on. Image recognition and image location in environmental monitoring has become an academic hotspot in the field of computer vision. Convolution neural network model is the most commonly used image processing model. Compared with the traditional artificial neural network model, convolution neural network has more hidden layers. Its unique convolution and pooling operations have higher efficiency in image processing. It has incomparable advantages in image recognition and location and other forms of two-dimensional graphics tasks. As a new deformation of convolution neural network, residual neural network aims to make convolution layer learn a kind of residual instead of a direct learning goal. After analyzing the characteristics of CNN model for image feature representation and residual network, a residual network model is built. The UAV remote sensing system is selected as the platform to acquire image data, and the problem of image recognition based on residual neural network is studied, which is verified by experiment simulation and precision analysis. Finally, the problems and experiences in the process of learning and designing are discussed, and the future improvements in the field of image target location and recognition are prospected.
关键词： Residual network,CNN,Image recognition,UAV
更新于2025-09-23 15:22:29
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - LIDAR-based Vehicle Recognition with Global Cylindrical-coordinate Histogram Descriptor
摘要： Vehicle Recognition is crucial for an Autonomous Land Vehicle (ALV) navigation in urban environments. In this paper, we proposed a novel Global Cylindrical Coordination Histogram Descriptor(GCCHD) to recognize vehicles in urban environments. This description is created for the centre point of each object. In order to achieve independence with respect to the rotation around z axis, a global reference frame is introduced in GCCHD, and all the three-dimensional points in the cylindrical support region are projected into the three-dimensional histogram according to their three cylindrical coordinates. Experiments on the public Sydney Urban Object dataset and the dataset we prepare and label manually verify the performance of the GCCHD in vehicle recognition.
关键词： Vehicle Recognition,three-dimensional points,GCCHD,Autonomous Land Vehicle
更新于2025-09-23 15:22:29