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Face Recognition Systems in Math Classroom Through Computer Vision Traditional Techniques 1st

Face Recognition Systems in Math Classroom Through Computer ...

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1st International conference on Applied Technologies (ICAT)

作者： Granda, Luis Barba-Guaman, Luis Torres-Carrion, Pablo Cordero, Jorge Univ Tecn Particular Loja Grp Invest Inteligencia Artificial Aplicada Loja Ecuador

ISBN: (纸本)9783030425203;9783030425197

The methods and techniques of detection of the human face and facial recognition have presented a great impulse in recent years, thanks to the advance in areas such as artificial vision and machine learning. Although deep neural network techniques are in vogue, traditional techniques allow you to create applications that do not consume many resources from computing devices. In this research, we present a facial recognition system that implements the Eigenfaces method, developed in C # of Microsoft visual Studio and open-source video processing libraries such as OpenCV as EmguCV. The application is divided into two sections: the first called register where, through an integrated camera, images of the user's face or other means such as video and stored images are captured, and the second section is known as recognition where the user is compared with all the records of the data set, indicating whether this is registered and the recognition percentage. The project was implemented with a universe of the size of twenty-five users, of which six are men (24%) and nineteen are women (76%), developing tests for five weeks.

关键词： Facial recognition Computer vision Eigenfaces image processing

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Transformed directional tri concomitant triplet patterns for image retrieval 4th

Transformed directional tri concomitant triplet patterns for...

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4th International conference on Computer Vision and image processing, CVIP 2019

作者： Hussain, Chesti Altaff Rao, D. Venkata Mastani, S. Aruna Department of ECE JNTU College of Engineering Anatapuramu India QIS Institute of Engineering and Technology Ongole India

ISBN: (纸本)9789811540141

Content-based image retrieval (CBIR) is an accurate characterization of visual information. In this paper, we have proposed a new technique entitled as Transformed Directional Tri Concomitant Triplet Patterns (TdtCTp) for CBIR. TdtCTp consist of three stages to obtain detail directional information about pixel progression. In first stage, structural rule based approach is proposed to extract directional information in various direction. Further, in second stage, microscopic information and correlation between each sub-structural elements are extracted by using concomitant conditions. Finally, minute directional intensity variation information and correlation between the sub-structural elements are extracted by integrating first two stages. Retrieval accuracy is estimated using traditional distance measure technique in terms of average retrieval precision and average retrieval rate on publicly available natural and medical image databases. Performance analysis shows that TdtCTp descriptor outperforms the existing methods in terms of retrieval accuracy. © Springer Nature Singapore Pte Ltd 2020.

关键词： Content based retrieval

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Distributed image Retrieval Base on LSH Indexing on Spark 1st

Distributed Image Retrieval Base on LSH Indexing on Spark

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1st International conference on Big Data and Security, ICBDS 2019

作者： Hou, Zelei Huang, Chao Wu, Jiagao Liu, Linfeng School of Computer Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

With the advent of the era of big data, how to process massive image, video and other multimedia data timely and accurately has become a new challenge in related fields. Aiming at the computational bottleneck and inefficiency of traditional image content retrieval system, in this paper, a distributed image retrieval framework base on Location Sensitive Hash (LSH) indexing is proposed on Spark, which combines with the distributed storage and computing characteristics of Spark big data platform. Then, distributed K-means based Bag of visual Word (BoVW) algorithm and LSH algorithm are proposed to build LSH index vectors on Spark in parallel. The experiment shows that the retrieval time of our framework can be reduced significantly, and both retrieval recall and precision are high compared with the traditional retrieval method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Indexing (of information)

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Implementation of image processing and machine learning in high resolution aerial image datasets for lake resource usage, aquaculture, and coastal community

Implementation of image processing and machine learning in h...

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2020 IEEE Region 10 conference, TENCON 2020

作者： Belarmino, Mark Daniel B. Libatique, Nathaniel Joseph C. Ateneo de Manila University Electronics Computer and Communications Engineering Department Quezon City Philippines Ateneo Innovation Center Ateneo de Manila University Electronics Computer and Communications Engineering Department Quezon City Philippines

ISBN: (纸本)9781728184555

Last May 2019, fish farms in Taal Lake suffer from fish kill resulting in an estimated loss of 405 tons of fish. It was reported that the measured water sample from the lake shows significant loss of dissolved-oxygen due to over-crowding of fish farm. With the crisis mentioned, recent studies utilize satellite remote sensors to map and monitor the aquaculture inside the lake. The maps are being used as reference material for progress monitoring, as decision-support and lake management tool by the local government and regulatory agencies. Aerial maps were captured using Unmanned Aerial Vehicle (UAV) as it has better resolution than satellite imagery. This study implements image processing and Mask Regional Convolutional Neural Network (Mask RCNN) on high resolution images to create an object detection and segmentation model for aquaculture structures and coastal settlement. To create the detection model, the image dataset undergoes preprocessing before feeding into the training process. Finally, an analytical software was developed to utilize segmented maps for zone management plan implementation, lake resource usage calculation, and gauge the population of settlers along the coastline. This provides meaningful visual and statistical data regarding aquaculture population, lake resource usage, local settlement population and zone development plan status. © 2020 IEEE.

关键词： Lakes

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MR image Denoising Using Adaptive Wavelet Soft Thresholding

MR Image Denoising Using Adaptive Wavelet Soft Thresholding

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International conference on VLSI, communications, and Signal processing (VCAS)

作者： Sahu, Sima Singh, Harsh Vikram Singh, Amit Kumar Kumar, Basant Dr APJ Abdul Kalam Tech Univ Lucknow Uttar Pradesh India Kamla Nehru Inst Technol KNIT Dept Elect Sultanpur Uttar Pradesh India NIT Dept Comp Sci & Engn Patna Bihar India Motilal Nehru NIT Dept ECE Allahabad Uttar Pradesh India

ISBN: (纸本)9789813297753;9789813297746

Medical imaging has played an important role in medical disease detection, diagnosis, and research related findings and therapy. Removal of noise in a medical image is mandatory step for quality assessment. This paper presents a feature preserved Magnetic Resonance (MR) denoising algorithm which is based on well-accepted multiresolution and statistical modeling. An adaptive wavelet soft thresholding method is designed to remove Gaussian noise from MR image. Orthogonality property of the wavelet transform is used to find the noise variance, using a Median Absolute Deviation (MAD) estimator. Distribution parameters of the wavelet coefficients are estimated by modeling the coefficients using a Normal Inverse Gaussian (NIG) probability density function (PDF). The threshold value is calculated considering the noise and signal information and the wavelet coefficients are updated accordingly. Estimation and analysis of the performance of the proposed method is performed using peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance evaluation parameters;these parameters demonstrate the noise suppression ability of the proposed method. Superiority of the proposed denoising method is further established through visual inspection of the denoised images obtained from proposed as well as other contemporary reported methods.

关键词： Multiresolution Denoising Soft thresholding Statistical modeling

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PROBENET: PROBING DEEP NETWORKS 26

PROBENET: PROBING DEEP NETWORKS

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26th IEEE International conference on image processing (ICIP)

作者： Lee, Jae-Hyeok Kim, Seong Tae Ro, Yong Man Korea Adv Inst Sci & Technol Sch Elect Engn Image & Video Syst Lab Daejeon South Korea

ISBN: (纸本)9781538662496

Despite the rapid progress of deep learning research in recent years, interpreting deep network is still quite challenging. Interpreting deep networks is essential to both end-users and developers since it gives confidence in the usage of the deep network. This paper deals with a method for interpreting deep networks, especially visual interpretation. In order to get visual interpretation from a target deep network, we propose a ProbeNet that provides a decomposed visual interpretation of the target deep network. The ProbeNet decomposes the feature representations of the point of the target deep network into human interpretable units. Furthermore, the ProbeNet provides kernel-level analysis about the target deep network. In experiments, visual interpretation of two different target deep networks showed the usefulness of the ProbeNet to interpret target deep networks.

关键词： ProbeNet Deep network probing Deep network interpretation Human-understandable

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visual Place Recognition by DTW-based sequence alignment 27

Visual Place Recognition by DTW-based sequence alignment

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27th Signal processing and communications Applications conference (SIU)

作者： Hafez, A. H. Abdul Tello, Ammar Alqaraleh, Saed Hasan Kalycnou Univ Comp Engn Dept Gaziantep Turkey

ISBN: (纸本)9781728119045

Place recognition, also called visual localization, facilitates the autonomous navigation capabilities of the future of driverless cars. This paper proposes a new place recognition algorithm that considers the appearancebased methodology to localize the vehicle by utilizing visual route map, i.e. a sequence of images, or sets of features extracted from these images, that were recorded over different times and dates for the route environments. These reference sequences are accurately labeled and annotated using GPS tags or manually using odometry information. The dynamic time warping (DTW) algorithm is used to achieve image sequence alignment and find the best match for each frame from the test sequence. The proposed algorithm considered hand-crafted features like SIFT, HOG, and LDB. Experiments, using common challenging and benchmark datasets, i.e. "UQ St Lucia" and "Nordland", have been conducted, and it has been observed that the proposed technique has significantly improved the performance of well-known appearance-based descriptors SIFT, HOG, and LDB as compared to its individual performance and to some of the state-of-the-art localization and mapping methods such as ABLE (Binary-appearance Loop-closure).

关键词： visual place recognition Dynamic time warping image sequence matching

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A Vision Based Fish processing System

A Vision Based Fish Processing System

引用

IEEE visual communications and image processing (VCIP)

作者： Zongjian Zhang Litao Yu Jian Zhang Qiang Wu University of Technology Sydney

The following topics are dealt with: learning (artificial intelligence); video coding; feature extraction; data compression; convolutional neural nets; image coding; image representation; computer vision; image colour... 详细信息

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

关键词： Lighting Estimation Fish Supply chains Size measurement Artificial intelligence Servers

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Forget about the LiDAR: Self-supervised depth estimators with MED probability volumes 34

Forget about the LiDAR: Self-supervised depth estimators wit...

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34th conference on Neural Information processing Systems, NeurIPS 2020

作者： Gonzalez, Juan L. Kim, Munchurl School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of

Self-supervised depth estimators have recently shown results comparable to the supervised methods on the challenging single image depth estimation (SIDE) task, by exploiting the geometrical relations between target and reference views in the training data. However, previous methods usually learn forward or backward image synthesis, but not depth estimation, as they cannot effectively neglect occlusions between the target and the reference images. Previous works rely on rigid photometric assumptions or on the SIDE network to infer depth and occlusions, resulting in limited performance. On the other hand, we propose a method to "Forget About the LiDAR" (FAL), with Mirrored Exponential Disparity (MED) probability volumes for the training of monocular depth estimators from stereo images. Our MED representation allows us to obtain geometrically inspired occlusion maps with our novel Mirrored Occlusion Module (MOM), which does not impose a learning burden on our FAL-net. Contrary to the previous methods that learn SIDE from stereo pairs by regressing disparity in the linear space, our FAL-net regresses disparity by binning it into the exponential space, which allows for better detection of distant and nearby objects. We define a two-step training strategy for our FAL-net: It is first trained for view synthesis and then fine-tuned for depth estimation with our MOM. Our FAL-net is remarkably light-weight and outperforms the previous state-of-the-art methods with 8× fewer parameters and 3× faster inference speeds on the challenging KITTI dataset. We present extensive experimental results on the KITTI, CityScapes, and Make3D datasets to verify our method’s effectiveness. To the authors’ best knowledge, the presented method performs the best among all the previous self-supervised methods until now. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Stereo image processing

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Identifying, Classifying and Searching Graphic Symbols in the NOTAE System 1

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16th Italian Research conference on Digital Libraries (IRCDL)

作者： Boccuzzi, Maria Catarci, Tiziana Deodati, Luca Fantoli, Andrea Ghignoli, Antonella Leotta, Francesco Mecella, Massimo Monte, Anna Sietis, Nina Sapienza Univ Roma Dipartimento Storia Antropol Relig Arte Spettacol Rome Italy Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest Rome Italy Sapienza Univ Roma Informat & Stat Fac Ingn Informaz Rome Italy

ISBN: (数字)9783030399054

ISBN: (纸本)9783030399054;9783030399047

The use of graphic symbols in documentary records from the 5th to the 9th century has so far received scant attention. What we mean by graphic symbols are graphic signs (including alphabetical ones) drawn as a visual unit in a written text and representing something other or something more than a word of that text. The Project NOTAE represents the first attempt to investigate these graphic entities as a historical phenomenon from Late Antiquity to early medieval Europe in any written sources containing texts generated for pragmatic purposes (contracts, petitions, official and private letters, lists etc.). Identifying and classifying graphic symbols on such documents is a task that requires experience and knowledge of the field, but software applications may come in help by learning to recognize symbols from previously annotated documents and suggesting experts potential symbols and likely classification in newly acquired documents to be validated, thus easing the task. This contribution introduces the NOTAE system that, in addition to the aforementioned task, provides non expert users with tools to explore the documents annotated by experts.

关键词： Graphic symbols Paleography image processing Clustering

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