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3rd Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Lin, Zhenkai Zheng, Fei Ding, Jianpei Li, Jinping Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Shandong Coll & Univ Key Lab Informat Proc & Cognit Comp 13 5 Year Jinan 250022 Shandong Peoples R China Jinan Shengquan Grp Share Holding Co Ltd Jinan 250000 Peoples R China Shandong ShenTu Intelligent Sci & Technol Co Ltd Jinan 250022 Peoples R China

ISBN: (纸本)9783030606329;9783030606336

Nailfold microcirculation can reflect the state of health. It is an important research topic to detect the change of microcirculation blood flow velocity. In order to save cost, people usually choose the low-cost micro camera to collect samples. However, the microvascular video collected by such camera is often disturbed by various noises, resulting in poor contrast and clarity of the image. Due to the large number of microvessels, it is time-consuming and laborious to detect the blood flow rate manually, which is inefficient and difficult to accurately detect the subtle changes of the blood flow velocity. At present, the previous research of blood flow velocity detection is mainly focused on the clearer microcirculation video. It is difficult to find any related research of blood flow velocity detection for noise image. For the low-quality nailfold microcirculation video collected by the low-cost microscope camera, we propose an automatic detection method of blood flow velocity based on projection analysis of spatiotemporal image. The method is as follows: firstly, video preprocessing, correlation matching are used to remove jitter and image blur is eliminated by deconvolution, the row mean is used to eliminate reflective area by analyzing the characteristics of noise distribution;secondly, we use cumulative background modeling to segment blood vessels and propose an automatic detection algorithm of blood vessel centerline;thirdly, the direction of binary spatiotemporal image is detected by using rotation projection, and then the blood flow velocity is calculated. The experimental results show that the proposed method can detect the blood flow velocity of microcirculation automatically and efficiently. Meanwhile, the average correlation coefficient between the proposed method and the manual measurement standard value is 0.935.

关键词： Nailfold microcirculation Blood flow velocity detection Rotational projection Spatiotemporal image

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Content-based Traditional Clothes pattern Retrieval Using Convolutional Neural Network

Content-based Traditional Clothes Pattern Retrieval Using Co...

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Material and Electrical Engineering conference (ISMEE), International Symposium on

作者： Abdul Haris Rangkuti Varyl Hasbi Athala Naufal Fauzi Luthfi Syaugi Vikri Aditama Ayu Hidayah Aslamiah School of Computer Science Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Bandung Indonesia

ISBN: (纸本)9781728177335

Indonesia is a country with an inclusive culture spread all over the country. Usually, cultural objects can have different meanings based on the details. Traditional cloth is an example of Indonesia's culture. Each province in Indonesia has various colors, patterns, textures, characteristics of traditional fabrics. However, each cloth's patterns and textures have different meanings or meanings. The various features and characteristics of traditional clothing can be extracted using the Convolutional Neural Network. This experiment uses 74 types of traditional patterns collected from the internet. Pre-processing creates 4144 new images with seven different rotation variations, four different overlap settings, and also two crop sides for the image database. CNN type for VGG19 is used as feature extraction of fabric pattern and Euclidean Distance for feature vector distance calculation. Based on the experimental results, the average output using this method is 86%. Besides that, about 69% of the results of the query process for pattern and texture images have an accuracy greater than 80%. Even some patterns can produce accuracy up to 100%. This study makes an excellent image retrieval accuracy but also needs a lot of improvement.

关键词： Electrical engineering image color analysis Query processing image retrieval Crops Euclidean distance Feature extraction

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An Identification Method of image-based for Output pattern of Metamorphic Relation in Burnup Calculation Program

An Identification Method of Image-based for Output Pattern o...

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Science and Technology Innovation (IAECST), International Academic Exchange conference on

作者： Shengfu Fu Xiaohua Yang Meng Li Fan Wang Engineering and Technology Research Center of Software Evaluation and Testing for Intellectual Equipment of Hunan Province Hengyang of Hunan Prov. China CNNC Key Laboratory on High Trusted Computing Hengyang of Hunan Prov. China School of Computer Science and Technology University of South China Hengyang China

ISBN: (纸本)9781665402682

It is challenging to verify the design and analysis software adequately for nuclear power areas because of the inflated cost, time-consuming benchmark problems development, and few verification examples. This situation is called the test oracle problem. Metamorphic testing cleverly combines the specific properties of physical equations and numerical algorithms with software verification. Without constructing numerical solutions or verification examples, it verifies the software by checking whether the input and output of multiple executions meet the metamorphic relations. Since metamorphic relation is the nature rules of physical models and numerical algorithms, it is not restricted to specific code implementation technology. Therefore, it is no doubt that metamorphic testing is a promising technique to alleviate the test oracle *** relation is the key to metamorphic testing, which consists of input pattern and output pattern. The former can be obtained through manual analysis by industry experts, while the latter does not have a system identification method. An output pattern identification method based on the solution figure has been introduced here. Specifically, the input pattern is received by artificially analyzing the physical property of the program under test. Next, a group of test inputs has been generated according to such patterns. The calculation results are received after those inputs drive the program. Moreover, the function form of the output pattern is guided by the image of the results. Finally, metamorphic relation is constructed by input pattern and output pattern. For a demonstration of this method’s details, a nuclide burnup calculation code is employed here, namely NUIT. By investigating the resulting image of 3820 isotopes, their output pattern classification models have been established according to the burnup depth and nuclide density. It includes four categories and seventeen sub-categories. The verification of other burnup

关键词： Technological innovation Codes Software algorithms pattern classification Manuals Software Mathematical models

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Spectral Band Subset Selection for Discrimination of Healthy Skin and Cutaneous Leishmanial Ulcers 9th

Spectral Band Subset Selection for Discrimination of Healthy...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Franco-Ceballos, Ricardo Torres-Madronero, Maria C. Galeano-Zea, July Murillo, Javier Zarzycki, Artur Garzon, Johnson Robledo, Sara M. Inst Tecnol Metropolitano Smart Machines & Pattern Recognit Lab Res Grp Automat Elect & Computat Sci Medellin Colombia Inst Tecnol Metropolitano Res Grp Adv Mat & Energy MatyEr Medellin Colombia Univ Antioquia Program Study & Control Trop Dis PECET Sch Med Medellin Colombia Inst Tecnol Metropolitano Robot & Control Syst Lab Res Grp Automat Elect & Computat Sci Medellin Colombia Univ Pontificia Bolivariana Grp Opt & Espectroscopia Medellin Colombia

ISBN: (纸本)9783030313326;9783030313319

Leishmaniasis is a parasitic disease, transmitted by the bite of an insect that has previously fed on an infected host. One of its clinical forms is Cutaneous Leishmaniasis - CL and due to its increasing incidence, it is necessary to create effective and easy-use diagnostic methods. In this paper, we assess two unsupervised band-selection algorithms that allow the dimensional reduction of hyperspectral data taken from CL ulcers, maintaining a high classification accuracy. This is an important task for the development of an non-invasive system based on multispectral imaging, that support the diagnosis and treatment follow-up of cutaneous ulcer caused by Leishmaniasis. Spectral data was obtained in golden hamsters subjected to varying conditions of infection. Two algorithms, one based on similarity and the other based on singular values decomposition, are implemented using MATLAB functions and are applied to the spectral data. The selected subsets of bands are used to classify the spectra into healthy skin, border and ulcer centers using support vector machines - SVM and neural networks - NN. The obtained results are represented in precision tables and allow to observe that both methods achieve an appropriate dimensional reduction of multispectral data without losing key information for their subsequent classification. At the end, we show that it is possible to obtain a subset of spectral bands to discriminate between healthy skin and cutaneous ulcers caused by Leishmaniasis.

关键词： Leishmaniasis Hyperspectral data Unsupervised band selection Spectral reduction Classification

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High-performance Two-stage Face recognition Algorithm for Reduced Illumination Effect

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SENSORS AND MATERIALS 2019年第9期31卷 2771-2776页

作者： Yeh, Cheng-Yu Chen, Chien-Fu Lin, Chun-Cheng Natl Chin Yi Univ Technol Dept Elect Engn 57Sect 2Zhongshan Rd Taichung 41170 Taiwan

Face recognition, which is a method of identifying a person in a digital image, is widely applied to biometric-based authentication systems. A significant decrease in recognition rate is caused by extracted features that are affected by illumination. In an attempt to resolve this problem, in this paper, we present a two-stage algorithm, namely, a local binary pattern (LBP) followed by the algorithm Fisherface. As the first step of this work, a face image is converted to an LBP, which is then projected onto a low-dimensional feature space using Fisherface for subsequent classification and recognition. As a result, the outperformance of this work is demonstrated by a recognition rate of up to 96.45%, a figure far beyond 67.97% using the LBP histogram (LBPH), 84.69% using Fisherface, and 93.09% using the support vector machine (SVM) algorithm.

关键词： face recognition local binary pattern (LBP) LBP histogram (LBPH) Fisherface principal component analysis (PCA) linear discriminant analysis (LDA)

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Bangladeshi License Plate recognition Using Adaboost Classifier 8

Bangladeshi License Plate Recognition Using Adaboost Classif...

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Joint 8th International conference on Informatics, Electronics and Vision (ICIEV) / 3rd International conference on Imaging, Vision and pattern recognition (icIVPR) / International conference on Activity and Behavior Computing (ABC)

作者： Dhar, Prashengit Abedin, Md Zainal Karim, Razuan Fatema-Tuj-Johora Hossain, Mohammad Shahadat Univ Sci & Technol Chittagong Dept Comp Sci & Engn Chattogram Bangladesh Univ Chittagong Dept Comp Sci & Engn Chittagong 4331 Bangladesh

ISBN: (纸本)9781728107882

License plate recognition (LPR) is a technology for the authentication of a vehicle by locating and recognizing the license plate number in an image through computer vision techniques and machine learning models. To develop intelligent traffic management such as vehicle monitoring, LPR is a key component. However, due to the diversity of layouts and characters of plates, universal solution is not possible. So, this research focuses on development of an algorithm for the recognition of license plate of Bangladesh by using image processing's and machine learning model. This algorithm executes in three steps: detection of the plate with shape verification, tilt correction and recognition of the number. For detection, RGB color space, median filtering, binarization, morphological analysis, region properties for filtering are applied. To discard noisy object, shape verification is done through robust distances to borders vectors. Before character segmentation, horizontal tilt correction is applied. Then, characters are extracted by using bounding box parameters from the extracted plate. Finally, the recognition is implemented by using the blending of Histogram Oriented Gradient (HOG) and Local Binary pattern (LBP) features and adaptive boosting (Adaboost) classifier is used to categorize the characters. The proposed algorithm is simulated on the images which are captured from different roads of Bangladesh. The experimental result shows that the detection and recognition accuracy is noteworthy.

关键词： License Plate recognition (LPR) RGB Color Model Distance to Borders (DtBs) Character Segmentation HOG (Histogram of Oriented Gradient) Local Binary pattern (LBP) Adaboost

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A Hybrid Deep Learning and Handcrafted Features based Approach for Thyroid Nodule Classification in Ultrasound images 3

A Hybrid Deep Learning and Handcrafted Features based Approa...

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2020 3rd International conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Xie, Jiahao Guo, Lehang Zhao, Chongke Li, Xiaolong Luo, Ye Jianwei, Lu Tongji University Shanghai China Shanghai Tenth People's Hospital Shanghai China

With the increasing incidence rate of thyroid cancer, the diagnosis of thyroid nodules has become an important task. In this paper, we designed a deep neural network (DNN) to classify whether a thyroid nodule is benign or malignant, and proposed a structure which combines local binary pattern (LBP) with deep learning. Our method mitigates the effects of overfitting in medical image diagnosis tasks. With well-designed transfer leaning, we achieve an accuracy of 85% on our own ultrasound thyroid dataset. To ensure the reliability of our experiments, all examples are estimated by experts in Shanghai Tenth People's Hospital using fine needle analysis (FNA), which is a gold standard for thyroid nodules diagnosis. The experimental results show that combinations of the traditional medial image features can help the deep learning network get more semantic information from low-level inputs. © Published under licence by IOP Publishing Ltd.

关键词： Local binary pattern

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An Adaptive Mobile Manipulator System for Indoor Surveillance and Security Monitoring 43rd

An Adaptive Mobile Manipulator System for Indoor Surveillanc...

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43rd National Systems conference on Innovative and Emerging Trends in Engineering Systems, NSC 2019

作者： Chakraborty, Arindam Naresh Naik, M. Dasgupta, Bhaskar Centre for Mechatronics IIT Kanpur Kanpur UP208016 India

ISBN: (纸本)9789811580246

Mobile vehicles offer the advantage of larger manipulation space while articulated chain of rigid bodies can provide dexterous and precise manipulation in its fixed workspace. In this work, the authors study the electronic and mechanical aspects of a multi-facility mobile manipulator system, KaMoMan (developed at IIT Kanpur), so as to make it efficient in monitoring acts (by capturing and analyzing object images, navigating, manipulating objects, etc.) in rather structured but occluded environs of industrial settings. It is shown that the redundant system performs better if it is reconfigurable according to the specifications of its operational space. Further, the camera position can be controlled by evaluating the appropriate rigid body transformations between various frames, and an RANSAC-type feature detection is incorporated for spatial recognition. Although real-time object and pattern detection is still an ongoing endeavour within the group, a complete integration framework (and electronic description of the system along with the design methodology) is outlined, and an outline of a multi-SONAR navigation is given for a practical hardware implementation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Autonomous navigation image analysis of workspace Mobile imaging Mobile manipulation Mobile surveillance

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Face Identification Using Local Ternary Tree pattern Based Spatial Structural Components 9th

Face Identification Using Local Ternary Tree Pattern Based S...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Rakshit, Rinku Datta Kisku, Dakshina Ranjan Tistarelli, Massimo Gupta, Phalguni Asansol Engn Coll Dept Informat Technol Asansol 713305 W Bengal India Natl Inst Technol Durgapur Dept Comp Sci & Engn Mahatma Gandhi Rd Durgapur 713209 W Bengal India Univ Sassari DAP Comp Vis Lab I-07041 Alghero SS Italy IIT Kanpur Dept Comp Sci & Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9783030313210;9783030313203

This paper reports a face identification system which makes use of a novel local descriptor called Local Ternary Tree pattern (LTTP). Exploiting and extracting distinctive local descriptor from a face image plays a crucial role in face identification task in the presence of a variety of face images including constrained, unconstrained and plastic surgery images. LTTP has been used to extract robust and useful spatial features which use to describe the various structural components on a face. To extract the features, a ternary tree is formed for each pixel with its eight neighbors in each block. LTTP pattern can be generated in four forms such as LTTP-Left Depth (LTTP-LD), LTTP-Left Breadth (LTTP-LB), LTTP-Right Depth (LTTP-rd) and LTTP-Right Breadth (LTTP-RB). The encoding schemes of these patterns are very simple and efficient in terms of computational as well as time complexity. The proposed face identification system is tested on six face databases, namely, the UMIST, the JAFFE, the extended Yale face B, the Plastic Surgery, the LFW and the UFI. The experimental evaluation demonstrates the most promising results considering a variety of faces captured under different environments. The proposed LTTP based system is also compared with some local descriptors under identical conditions.

关键词： Face identification Local descriptor Ternary tree Cosine similarity Sum of absolute differences Classifier

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Development of a Fire Detection Based on the analysis of Video Data by Means of Convolutional Neural Networks 9th

Development of a Fire Detection Based on the Analysis of Vid...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Lehr, Jan Gerson, Christian Ajami, Mohamad Krueger, Joerg Fraunhofer IPK Pascalstr 8-9 D-10587 Berlin Germany TU Berlin Str 17 Juni 135 D-10623 Berlin Germany

ISBN: (纸本)9783030313210;9783030313203

Convolutional Neural Networks (CNNs) have proven their worth in the field of image-based object recognition and localization. In the context of this work, a fire detector based on CNNs has been developed that detects fire by analyzing video sequences. The major additions of this work will primarily be realized through the use of temporal information contained in the video sequences depicting fire. In contrast to state of the art fire detectors, a large image database with 160,000 images with an even distribution of positive and negative samples has been created. To be able to compare image-based and video-based approaches as objectively as possible, different image-based CNNs will be trained under the same conditions as the video-based networks within the scope of this work. It will be shown that video-based networks offer an advantage over conventional image-based networks and therefore benefit from the temporal information of fire. We have achieved a prediction accuracy of 96.82%.

关键词： Deep learning Convolutional Neural Networks Fire detection Video-based networks

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