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نتیجه جستجو - Pattern recognition

تعداد مقالات یافته شده: 307
ردیف عنوان نوع
1 Finger vein pattern recognition using image processing technique
تشخیص الگوی رگ انگشت با استفاده از تکنیک پردازش تصویر-2021
Human identification based on finger vein pattern is an interesting branch of biometric recognition that is getting attention among the researchers in the recent decade as vein patterns are unique such as iris recognition, face recognition, finger print for authentication and security purposes. The vein pattern uti- lized in this authentication technology refers to the image of vessels within the body which will be seen as a random mesh at the surface of the body. Vein pattern authentication can be applied to almost all people. In the proposed system we have used simple and easy algorithms for processing. The processing steps include image enhancement, thresholding and thinning. The processed images are matched with the images in the database. Thus the proposed biometric system will be effectively used in authentication and security purposes.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 12th National Conference on Recent Advancements in Biomedical Engineering.
Keywords: Fingerprint | Vein recognition | Pattern matching
مقاله انگلیسی
2 Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021
Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception
مقاله انگلیسی
3 Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding
رمزگذار خودکار گرافیکی برای استخراج نمودار مبتنی بر EEG-2021
Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in question. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed a Abstract Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in ques- tion. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed atraditional variational auto-encoder to demonstrate that more accurate features can be obtained when observing EEG-based brain connectivity from a graph perspective. Email addresses: tina.behrouzi@mail.utoronto.ca (Tina Behrouzi),dimitris@comm.utoronto.ca (Dimitrios Hatzinakos)Preprint submitted to Pattern Recognition July 20, 2021
Keywords: Biometrics | Functional connectivity | Electroencephalogram (EEG) | Graph Variational Auto Encoder (GVAE) | Graph deep learning
مقاله انگلیسی
4 Application of spectral features for separating homochromatic foreign matter from mixed congee
کاربرد ویژگیهای طیفی برای جداسازی مواد خارجی هم رنگ از مخروط مخروطی-2021
Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee.
Keyword: Mixed congee | Homochromatic foreign matter | Hyperspectral imaging technology | Pattern recognition | Chemometrics
مقاله انگلیسی
5 Gait recognition based on vision systems: A systematic survey
تشخیص راه رفتن بر اساس سیستم های بینایی: یک مرور سیستماتیک-2021
With the growing popularity of biometrics technology in the pattern recognition field, especially identification of human has gained the attention of researchers from both academia and industry. One such type of biometric technique is Gait recognition, which is used to identify a human being based on their walking style. Generally, two types of approaches are adopted by any algorithm designed for gait recognition, namely model based and model free approaches. The key reason behind the popularity of gait recognition is that it can identify a person from a considerable distance while other biometrics has failed to do so. In this paper, the authors have conducted a survey of extant studies on gait recognition in consideration of gait recognition approaches and phases of a gait cycle. Moreover, some aspects like floor sensors, accelerometer based recognition, the influences of environ- mental factors, which are ignored by exiting surveys, are also covered in our survey study. The information of gait is usually obtained from different parts of silhouettes. This paper also describes different benchmark datasets for gait recognition. This study will provide firsthand knowledge to the researchers working on the gait recognition domain in any real-world field. It has been observed that work done on the gait recognition with sufficiently high accuracy is limited in comparison to research on various other biometric recognition systems and has enough potential for future research.
Keywords: Gait recognition | Surveillance | Biometric | Person identification
مقاله انگلیسی
6 Implementation of homeostasis functionality in neuron circuit using doublegate device for spiking neural network
اجرای عملکرد هموستاز در مدار نورون با استفاده از دستگاه دروازه دو تایی برای شبکه عصبی spiking -2020
The homeostatic neuron circuit using a double-gate MOSFET is proposed to imitate a homeostasis functionality of a biological neuron in spiking neural networks (SNN) based on a spike-timing dependent plasticity (STDP). The threshold voltage (Vth) of the double-gate MOSFET is controlled by independent two-gate biases (VG1 and VG2). By using Vth change of the double-gate MOSFET in the neuron circuits, the fire rate of the output neuron is controlled. The homeostasis functionality is implemented by the operation of multi-neuron system based on the proposed neuron circuit. Through the SNN based on STDP using MNIST datasets, it is demonstrated that the recognition rate (~91%) of the SNN with the proposed homeostasis functionality is higher than that (~79%) of the SNN without the proposed homeostasis functionality. Also, the results of the recognition rate with the variations (σ/μ < 0.5) of the synaptic devices and the initial Vth of neuron circuits show a low degradation (1 ~ 3%) in the recognition rate. Thus, it is demonstrated that the homeostasis functionality of the proposed neuron circuit has the immunity to variations (σ/μ < 0.5) of the synaptic devices and the neuron circuits in the SNN based on STDP.
Keywords: Double-gate MOSFET | Neuron circuit | Homeostasis functionality | Pattern recognition | Spiking neural networks (SNNs)
مقاله انگلیسی
7 Density and speed of sound prediction for binary mixtures of water and ammonium-based ionic liquids using feedforward and cascade forward neural networks
تراکم و سرعت پیش بینی صدا برای مخلوط های باینری مایعات یونی آب و آمونیوم با استفاده از شبکه های عصبی cascade forward-2020
Ionic liquids have attracted a lot of attention in the past years because of some of their properties that distinguish them fromthe classic solvents. Thus, the need for models that can represent their properties without new experimental efforts arises, as experiments are frequently expensive and time-consuming. Neural networks are processing systems capable of simulating biological learning and generalizing the learned functional relations to new cases never seen before. They have been used with success in several areas, like optimization, pattern recognition and function approximation. Therefore, they can be an important asset for properties prediction. This work is focused on designing, training and studying feedforward and cascade forward neural networks for density and speed of sound prediction for binary mixture of water and ammonium-based ionic liquids, using the temperature, mass fraction of ionic liquid and the structural groups of the reagents used to synthesize the ionic liquid as input variables. Besides the synaptic paradigm, some network parameters were also evaluated, namely the hidden neuron number and the number of layers. Also, 13 training algorithms were tested and had their performance evaluated. It was verified a superiority of the Levenberg-Marquardt method and the Bayesian regularization in the training. The proposed neural networks, two 12-10-10-1 cascade forward networks trained with Bayesian regularization, achieved an average absolute relative deviation of 0.0107% for density prediction and 0.1% for speed of sound prediction. The error dispersions showed the networks did not develop trends in prediction.
Keywords: Neural networks | Ionic liquids | Density | Speed of sound | Thermophysical properties
مقاله انگلیسی
8 A review of learning in biologically plausible spiking neural networks
مروری بر یادگیری در شبکه های عصبی اسپایک بیولوژیکی قابل قبول-2020
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of SNN learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the SNN field are discussed.
Keywords: Spiking neural network (SNN) | Learning | Synaptic plasticity
مقاله انگلیسی
9 A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy
یک الگوریتم جستجوی سریع جدید برای همسایگان دقیق k-مبتنی بر استراتژی بررسی مبتنی بر مثلث-نابرابری بهینه-2020
The k-nearest neighbor (KNN) algorithm has been widely used in pattern recognition, regression, outlier detection and other data mining areas. However, it suffers from the large distance computation cost, especially when dealing with big data applications. In this paper, we propose a new fast search (FS) algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based (OTI) check strategy. During the procedure of searching exact k-nearest neighbors for any query, the OTI check strategy can eliminate more redundant distance computations for the instances located in the marginal area of neighboring clusters compared with the original TI check strategy. Considering the large space complexity and extra time complexity of OTI, we also propose an efficient optimal triangle-inequalitybased (EOTI) check strategy. The experimental results demonstrate that our proposed two algorithms (OTI and EOTI) achieve the best performance compared with other related KNN fast search algorithms, especially in the case of dealing with high-dimensional datasets
Keywords: Exact k-nearest neighbors | Fast search algorithm | Clustering | Triangle inequality | Optimal check strategy
مقاله انگلیسی
10 Is hybrid AI suited for hybrid threats? Insights from social media analysis
آیا هوش مصنوعی ترکیبی برای تهدیدهای ترکیبی مناسب است؟ بینش از تحلیل رسانه های اجتماعی-2020
Social media create the opportunity for a truly connected world and change the way people communicate, exchange ideas and organize themselves into virtual communities. Both understanding online behavior and processing online content are of strategic importance for security applications. However, high volumes, noisy data and rapid changes of topics impose challenges that hinder the efficacy of classification models and the relevance of semantic models. This paper performs a comparative analysis on supervised, unsupervised and semantic-driven approaches used to analyze social data streams. The goal of the paper is to determine whether empirical findings support the enhancement of decision support and pattern recognition applications. The paper reports on research that has used various approaches to identify hidden patterns in social data collections where text is highly unstructured, comes with a mix of modalities and has potentially incorrect spatial-temporal stamps. The conclusion reports that the disconnected use of machine learning models and semantic-driven approaches in mining social media data has several weaknesses.
Index Terms: social networks | hybrid AI | defense and security
مقاله انگلیسی
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