- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Industrial Optical Character Recognition System in Printing Quality Control of Hot-Rolled Coils Identification
摘要： This work presents a system designed to detect printing errors and misidentifications on steel coils that could lead to tracking problems and even guide to the delivery of the wrong product to the final client. An optical character recognition system is proposed to extract the printed identification of steel coils from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters originally printed in an arch form, ending with a classification routine based on a convolutional neural network. The proposed system presents an approach to treat lighting variations in images, covering low contrast, darker and brighter images. Experiment carried out on a data set with approximately 20,000 images achieved an accuracy higher than 98%, supporting the validity of the proposed method.
关键词： Digital image processing,Convolutional neural networks,Optical character recognition,Intelligent manufacturing
更新于2025-09-12 10:27:22
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Face recognition using depth and infrared pictures
摘要： This paper proposes the face recognition method using both the depth and infrared pictures. The conventional face recognition methods based on color picture recognize the human faces accurately, but are easily a?ected by the illumination and are vulnerable to the attempts to steal user’s face information through fake face such as the photograph or the sculpture. On the other hand, the methods based on the depth or infrared picture are less a?ected by the illumination, and prevent an attempt to recognize a false face. This paper utilizes the depth picture to reduce the recognition time and the infrared picture to increase the recognition performance. In the face detection, this paper ?nds the nose of a person using the captured depth picture for reducing detection time, and it detects the region of the face. In the feature extraction, it extracts the feature pictures from the infrared picture by 3D Local Binary Pattern. In the face identi?cation, this paper compares between the features of the captured face and features of faces that are pre-stored in the DB, and obtains the face similarity. If the similarity of the face is larger than the certain threshold, the face recognition succeeds. Simulation results show that the face recognition performance is good not only in the normal environment but also in the little illumination.
关键词： local binary pattern,depth picture,face recognition,infrared picture
更新于2025-09-12 10:27:22
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Image quality enhanced recognition of laser cavity based on improved random hough transform
摘要： The tedious measurement of arc parts with low accuracy is serious problem in the traditional industrial measurement. In this paper, the method for the image quality enhanced recognition with digital image processing technology is developed by changing the direct detection of arc parts of the laser cavity into the detection of contour curve in the image. A circular arc fitting algorithm based on the improved random hough transform (RHT) is proposed to improve the disadvantages of RHT such as strong noise disturbance, high requirements for the extraction of contour continuity and slow calculation speed. The differences between the distance from the center of the fitting circle to all points in the testing area and the fitting radius were calculated. The minimum value was obtained to determine the optimal fitting circular arc. The algorithm is tested and applied to detect the actual workpiece. It is demonstrated that the accuracy and speed of cavity detection are much better by comparision with the traditional algorithm by the proposed improved algorithm.
关键词： Optimal circular arc fitting algorithm,Arc parts detection,Improved random hough transform,Image quality enhanced recognition
更新于2025-09-12 10:27:22
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Single plasmon-active optical fiber probe for instantaneous chiral detection
摘要： The chiral recognition of organic compounds is of vital importance in the field of pharmacology and medicine. Unfortunately, the common analytical routes used in this field are significantly restricted by time spent and equipment demands. In this work, we propose unprecedented alternative, aimed on enantiomers discrimination and estimation of their concentrations in uncomplicated and instantaneous manner. Proposed approach is based on the creation of optical fiber probe with two pronounced plasmonic bands attributed to gold and silver. The gold or silver surfaces were grafted with moieties, able to enantioselective entrap chiral amines from solution, resulting in a wavelength shift corresponding to each plasmonic metals. As a model compound of chiral amine, we chose the DOPA, also taking in mind its high medical relevancy. For the chiral detection, the optical fiber probe was simply immersed in the analytical solution of DOPA, and the selective shift of gold or silver plasmon band was observed in the reflected light depending on the DOPA chirality. The observed shifts depend on the concentration of DOPA enantiomers. In the case of racemic mixture, the shifts of both plasmonic bands emerge, making possible simultaneous determination of enantiomers concentrations and their ratio. The analytical cycle takes several minutes and requires very simple laboratory equipment.
关键词： fiber-optic probe,DOPA,racemic mixture,surface plasmon resonance,chiral detection,instantaneous recognition
更新于2025-09-12 10:27:22
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Two-Dimensional Fluorescent Strategy Based on Porous Silicon Quantum Dots for Metal-Ion Detection and Recognition
摘要： A two-dimensional photoluminescent (2D PL) detection strategy was established based on luminescent porous silicon (LuPSi) with wide-size-distributed silicon quantum dots and abundant surface chemistry. Owing to the intrinsic nature of LuPSi, interaction or reaction between analytes and LuPSi may cause static, dynamic, oxidation-induced, and deposition-induced quenching. By monitoring of both the PL intensity change and peak shift of LuPSi, the 2D PL detection strategy could discriminate di?erent analytes. Detection and recognition of di?erent metal ions in real water samples using a single peak were realized. Compared with the existing array-based methods, the 2D PL approach signi?cantly simpli?ed the sensing element and detection process.
关键词： metal ions,quantum dot array,two-dimensional photoluminescence,luminescent porous silicon,recognition
更新于2025-09-12 10:27:22
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[IEEE 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP) - Kuala Lumpur, Malaysia (2019.9.27-2019.9.29)] 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP) - 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion
摘要： This paper presents a novel multi-view convolutional neural network (CNN) model for 3D facial expression recognition (FER). In contrast to existing deep learning-based 3D FER approaches that mainly learn the expressions from frontal facial attribute images, the proposed model incorporates multi-view and facial prior information of the observed 3D face into the learning process. This information is jointly trained in an end-to-end manner to predict the emotion of the input 3D face model. The experiments on public 3D facial expression datasets show that training the CNN with additional information from different views and facial prior knowledge would result in learning more discriminative features as against from a single view. Our model outperforms the state-of-the-art 3D FER methods in term of recognition accuracy indicating its effectiveness. Moreover, the improvement of the proposed model is displayed more clearly in the discrimination of low- intensity facial expressions.
关键词： facial expression recognition (FER),convolutional neural network,3D face scan
更新于2025-09-12 10:27:22
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Determination of copper-based mineral species by laser induced breakdown spectroscopy and chemometric methods
摘要： The direct identification of mineral species in raw rocks was performed using laser induced breakdown spectroscopy (LIBS). A total of 162 sulfide rocks with mineralogical relevance in the copper industry were analyzed. These contained bornite (Cu5FeS4), chalcocite (Cu2S), chalcopyrite (CuFeS2), covellite (CuS), enargite (Cu3AsS4), molybdenite (MoS2), and pyrite (FeS2). The samples were collected from different mining locations to account for sample variability. Unsupervised multivariate methods like principal component analysis (PCA) and dendrogram analysis were explored, while supervised pattern recognition techniques, such as soft independent modelling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), K-nearest neighbor (KNN), decision tree analysis and artificial neural networks (ANNs) were compared. The sensitivity test performed on the LIBS data shows that the models KNN, SIMCA, PLS-DA and ANN achieve an average classification accuracy of 96.2, 98.1, 90.6 and 100%, respectively. In contrast, the robustness test of the models SIMCA and PLS-DA yields accuracies of 97.7 and 98.8%, respectively. The correct identification of very similar species in terms of their elemental composition such as bornite/chalcopyrite and chalcocite/covellite is also achieved.
关键词： mineral species,chemometrics,Laser induced breakdown spectroscopy,LIBS,multivariate methods,copper industry,pattern recognition
更新于2025-09-12 10:27:22
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[IEEE 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) - Newark, NJ, USA (2019.11.11-2019.11.14)] 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) - EyeLoc: Smartphone Vision Enabled Plug-n-play Indoor Localization in Large Shopping Malls
摘要： Indoor localization is an emerging demand in many large shopping malls. Existing indoor localization systems, however, require exhausted system bootstrap and calibration phases. The huge sunk cost usually hinders practical deployment of the indoor localization systems in large shopping malls. In contrast, we observe that ?oor-plan images of large shopping malls, which highlight the positions of many shops, are widely available in Google Maps, Gaode Maps, Baidu Maps etc. According to several observed shops, people can localize themselves (self-localization). However, due to the requirements of geometric sense and space transformation, not all people get used to this way. In this paper, we propose EyeLoc, which uses smartphone vision to enable accurate self-localization on ?oor-plan images. EyeLoc addresses several challenges which include developing ubiquitous smartphone vision system, ef?cient vision clue extraction and robust measurement error mitigation. We implement EyeLoc in Android and evaluate its performance in emulated environment and four large shopping malls. The 90-percentile errors of localization and heading direction are 4m and 20? in the two large shopping malls.
关键词： text detection/recognition,inertial measurement,smartphone vision system,Indoor localization
更新于2025-09-12 10:27:22
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European Microscopy Congress 2016: Proceedings || Electron holography by means of tilted reference waves
摘要： The paper discusses the direct recognition of the phase of light waves by means of a thin-film interference filter. It explores the experimental realization of a reference wave and a plane reference wave to form an interference pattern at the detector plane. The experimental setup includes a laser source, a beam splitter, and a detector array. The results demonstrate the feasibility of direct phase recognition with high spatial resolution.
关键词： light waves,spatial resolution,interference pattern,thin-film interference,phase recognition
更新于2025-09-11 14:15:04
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Classification of saline water for irrigated agriculture using near infrared spectroscopy coupled with pattern recognition techniques
摘要： This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500–4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.
关键词： pattern recognition,near infrared spectroscopy,agriculture,saline water
更新于2025-09-11 14:15:04