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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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the influence of the Ag nanostructures dimensions on the pattern of electric field distribution in cases of blood cancer 10

The influence of the Ag nanostructures dimensions on the pat...

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10th international conference on theoretical and Applied Physics, ICTAP 2020

作者： Farahdina, U. Zulfa, V.Z. Firdhaus, M. Endarko, E. Rubiyanto, A. Nasori, N. Laboratory of Medical Physics and Biophysics Department of Physics Institut Teknologi Sepuluh Nopember Kampus ITS Surabaya East Java60111 Indonesia

Above normal concentrations of leukocyte can indicate blood cancer in humans. this study uses a finite difference time domain (FDTD) based on the Ag nanostructure array experiment from previous study. the influence of leukocyte and blood medium and also Ag nanostructures dimensions the on the pattern of electric field distributions in Ag nanostructure arrays was described on this study. the electric field distribution and transmittance of Ag nanostructure arrays using different medium was analysed. the electric field in Ag nanosquare with side size 50 nm irradiated with electromagnetic wave at a wavelength of 350 nm has value 1.78 × 10-5 V/m and has the highest electric field value compared to the other dimensions and wavelengths of electromagnetic wave. the maximum difference value of transmittance Ag nanosquare array between blood and leukocyte is 8.06 a.u. this value is resulted from Ag nanosquare with side size 50 nm irradiated with electromagnetic wave at a wavelength of 250 nm. these result provide an initial interpretation of optimum wavelength of electromagnetic wave and size of Ag nanosquare arrays for therapy and detection of blood cancer. © Published under licence by IOP Publishing Ltd.

关键词： Electromagnetic waves

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Virtual Reality for Enhancement of Emotional Mindset in the First Lockdown of United Kingdom for the Covid-19 Pandemics 14th

Virtual Reality for Enhancement of Emotional Mindset in the ...

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14th international conference on Brain Informatics (BI)

作者： Franzoni, Valentina Di Marco, Niccolo Biondi, Giulio Milani, Alfredo Univ Perugia Dept Math & Comp Sci Perugia Italy Univ Florence Dept Math & Comp Sci Florence Italy

ISBN: (纸本)9783030869939;9783030869922

From 20 March to 10 May 2020, the "stay at home" countermeasures for the Covid-19 emergency lockdown were defined in the United Kingdom (UK) as leaving home for only the following reasons: "Key worker travelling to work", "Shopping for basic necessities", "Any medical need" or "Exercise once a day". Data collected from the UK Office for National Statistics through online and telephone questionnaires are an exceptional baseline data set on people behaviour during the Covid-19 pandemics. In this paper, data from demographic surveys from the UK are compared to statistical and feedback data from the Virtual Reality app called TRIPP for meditation in the experiences called Focus and Calm. Our data analysis shows that during lockdown the psychological and emotional mindset, severely challenged, has been successfully enhanced with the use of Virtual Reality.

关键词： Artificial intelligence Emotion recognition Coronavirus Affective computing Lockdown

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Discrimination of Decision Confidence Levels from EEG Signals

Discrimination of Decision Confidence Levels from EEG Signal...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Rui Li Le-Dian Liu Bao-Liang Lu Center for Brain-Machine Interface and Neuromodulation RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanchai China

To explore the capability of utilizing electroencephalograms (EEGs) for the measurement of human decision confidence levels, this paper develops a new visual perceptual decision confidence experiment. In this experiment, a visual perceptual decision-making task is performed by 14 participants, and their EEG data are recorded. the problem of measuring decision confidence levels is considered to be a pattern classification task, and two pattern classifiers are trained with differential entropy (DE), power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM), and asymmetry (ASM) features extracted from multichannel EEG data. We compare the performance of these features and find that the DE feature performs better than the others for measuring levels of decision confidence. the experimental results indicate that EEG signals offer good capability for measuring human decision confidence levels. the best performance of our proposed method in measuring five levels of decision confidence reaches an accuracy of 49.14 % and F1-score of 45.07 %, and for the extreme levels of decision confidence, the recognition accuracy reaches 91.28 %, with an average F1-score of 88.92 %. Topographic maps are also used to depict the neural patterns of EEG signals, suggesting that the posterior parietal cortex and occipital cortex might be sensitive brain areas for indicating decision confidence.

关键词： Support vector machines Visualization Power measurement Atmospheric measurements pattern classification Feature extraction Particle measurements

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DAL: A Deep Depth-Aware Long-term Tracker

DAL: A Deep Depth-Aware Long-term Tracker

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international conference on pattern recognition

作者： Yanlin Qian Song Yan Alan Lukežič Matej Kristan Joni-Kristian Kämäräinen Jiří Matas Computing Sciences Tampere University Finland Faculty of Computer and Information Science University of Ljubljana Slovenia Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

the best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run. We reformulate deep discriminative correlation filter (DCF) to embed the depth information into deep features. Moreover, the same depth-aware correlation filter is used for target redetection. Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.

关键词： Target tracking Correlation Benchmark testing Information filters pattern recognition Optimization

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A Local Descriptor with Physiological Characteristic for Finger Vein recognition

A Local Descriptor with Physiological Characteristic for Fin...

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international conference on pattern recognition

作者： Liping Zhang Weijun Li Xin Ning Linjun Sun Xiaoli Dong Institute of Semiconductors Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Microelectronics University of Chinese Academy of Sciences Beijing China Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

ISBN: (纸本)9781728188089;9781728188096

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, a prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make the feature robust against local changes in images, a histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.

关键词： Histograms Image recognition Databases Veins Fingers Physiology Stability analysis

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Automatic Identification of Leaf Disease using Image Segmentation Method

Automatic Identification of Leaf Disease using Image Segment...

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Annual Information Technology, Electromechanical Engineering and Microelectronics conference (IEMECON)

作者： R. Seetharaman D. Rahul H. Mani Lakshmanan S. Gayathri S.S. Sreeja Mole K. Anandan CEG Campus Anna University Chennai India Christu Jyothi Institute of Technology and Science Jangaon India PS Division of E&E TUV SUD South Asia Pvt Ltd Chennai India

ISBN: (纸本)9781665426879

An Internet of things (IOT) based plant diseased leaf segmentation and recognition method is proposed based on Fusion of Super-pixel clustering, K-mean clustering and Grey Level Cooccurrence Matrix (GLCM) algorithms. Firstly, the color diseased leaf image is divided into a few compact super-pixels by super-pixel clustering algorithm. then K means clustering algorithm is employed to segment the lesion image from each super-pixel. Finally, the GLCM features are extracted from three color components of each segmented lesion image and its gray scale image and the feature points are stored in a database. the experiment results on four plant diseased leaf image databases indicate that the proposed method is effective. this paper provides a feasible solution to anybody for automatic identification the leaf diseases at initial stages by using leaf image segmentation and plant disease recognition.

关键词： Image segmentation Cloud computing Image color analysis Clustering algorithms Support vector machine classification Feature extraction Internet of things

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Context for Object Detection via Lightweight Global and Mid-level Representations

Context for Object Detection via Lightweight Global and Mid-...

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international conference on pattern recognition

作者： Mesut Erhan Unal Adriana Kovashka School of Computing and Information University of Pittsburgh Pittsburgh Pennsylvania

ISBN: (纸本)9781728188089;9781728188096

We propose an approach for explicitly capturing context in object detection. We model visual and geometric relationships between object regions, but also model the global scene as a first-class participant. In contrast to prior approaches, both the context we rely on, as well as our proposed mechanism for belief propagation over regions, is lightweight. We also experiment with capturing similarities between regions at a semantic level, by modeling class co-occurrence and linguistic similarity between class names. We show that our approach significantly outperforms Faster R-CNN, and performs competitively with a much more costly approach that also models context.

关键词： Visualization Annotations Image edge detection Semantics Object detection Switches Linguistics

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Illustration and Identification of Skin Lesions in Dermoscopic Dataset Using pattern recognition Techniques 5th

Illustration and Identification of Skin Lesions in Dermoscop...

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5th international conference on Intelligent, Interactive Systems and Applications, IISA 2020

作者： Haider, Syeda Tooba Hussain, Syed Basharat Batool, Amreen Rokia, Laraba Selsabil Mohsan, Syed Agha Hassnain Sadiq, Muhammad Abubakar Saleem, Mazahir North China Electric Power University Beijing China Tiangong University Tianjin China East China University of Science and Technology Shanghai China Ocean College Zhejiang University Hangzhou China Beijing China Islamabad Pakistan

ISBN: (纸本)9783030637835

this paper proposes a generic method of description of skin lesion types and recognition of Melanocytic lesions in Dermoscopic images. Scientific judgments show that cancerous lesions demonstrate various characteristics such as streaks, texture or pigmented networks as compared to normal or benign lesions. Our aim is to confirm those judgments by carrying out proper segmentation, features extraction and classification. Segmentation phase of PH2 Public dataset was done by means of active contours. Features extraction stage included implementation of Local Binary patterns and its extension to study textural features more deeply. Finally, K nearest neighbor and Support vector machine were used for classification. this choice of methods is motivated by scientific importance of differential configurationally structures in the melanomas. the experiment of the offered algorithm is done on both sets of images, manually segmented (segmentation done by physicians followed by Features extraction and classification done by us) and automatic segmented images (all the steps performed by us) using performance metrics of sensitivity, specificity and Accuracy. the highest result of the system was attained by manually segmented images, i.e. SE = 89.2, SP = 84, Accuracy = 89.2. Whereas, images with automated segmentation showed SE = 89.3, SP = 75.4, Accuracy = 88.2. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Flamingo Search Algorithm and Its Application to Path Planning Problem 21

Flamingo Search Algorithm and Its Application to Path Planni...

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Proceedings of the 2021 4th international conference on Artificial Intelligence and pattern recognition

作者： Zhiheng Wang Jianhua Liu School of Computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://***/18280426650/FSA.

关键词： Constrained optimization Flamingo search algorithm Path planning problem Swarm intelligence optimization algorithm

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Penalizing Small Errors Using an Adaptive Logarithmic Loss 25th

Penalizing Small Errors Using an Adaptive Logarithmic Loss

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Kaul, Chaitanya Pears, Nick Dai, Hang Murray-Smith, Roderick Manandhar, Suresh School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom Department of Computer Science University of York YorkYO10 5DD United Kingdom MBZUAI Masdar City Abu Dhabi United Arab Emirates NAAMII Katunje Bhaktapur Kathmandu Nepal

ISBN: (纸本)9783030687625

Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth. Fundamentally, they define a functional landscape for traversal by gradient descent. Although numerous loss functions have been proposed to date in order to handle various machine learning problems, little attention has been given to enhancing these functions to better traverse the loss landscape. In this paper, we simultaneously and significantly mitigate two prominent problems in medical image segmentation namely: i) class imbalance between foreground and background pixels and ii) poor loss function convergence. To this end, we propose an Adaptive Logarithmic Loss (ALL) function. We compare this loss function with the existing state-of-the-art on the ISIC 2018 dataset, the nuclei segmentation dataset as well as the DRIVE retinal vessel segmentation dataset. We measure the performance of our methodology on benchmark metrics and demonstrate state-of-the-art performance. More generally, we show that our system can be used as a framework for better training of deep neural networks. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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