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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1803 条记录，以下是181-190 订阅

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Automated Detection and Classification Techniques of Acute Leukemia using image processing: A Review 2

Automated Detection and Classification Techniques of Acute L...

引用

international conference on Science and Technology-computer (ICST)

作者： Bagasjvara, R. G. Candradewi, Ika Hartati, Sri Harjoko, Agus Univ Gadjah Mada DCSE FMIPA Yogyakarta Indonesia

ISBN: (纸本)9781509043576

Hematological disorders of leukocytes (white blood corpuscles) are very frequent in medical practices. Each year, the number of new cases of leukemia increased with a high mortality rate what can cause a delay in diagnosis and treatment of leukemia. The hematologist faces difficulty in classifying white blood cells included cancerous leukemia cells. Currently, the diagnosis of hematological disorder was made by microscopic analysis of peripheral blood smear by hematological experts. However, this kind of diagnosis is complex, costly process, time- consuming and inherently subjective, hence to avoid such problems a classification technique based on computer vision is required. This document provides a survey of many kinds of literature which talk about leukemia detection using image processing. In this year, many improvements were made in this field. With this document, we provide a new perspective from many kinds of literature so future research can be developed effectively for their research.

关键词： Leukemia Segmentation Features Extraction white blood cell classification

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SVM-based method for perceptual image recognition 2

SVM-based method for perceptual image recognition

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2nd international conference on image, vision and Computing, ICIVC 2017

作者： Cheng, Yang Zhu, Bin An, Fang Department of Industrial Design Zhejiang University City College Hangzhou China College of Computer Science and Technology Zhejiang University Zhejiang China

ISBN: (纸本)9781509062379

Perceptual image of a product plays a significant role in decision making when users choose a product whose basic function is homogeneous nowadays. Designers try to design products that meet the all kinds of demands of users. However, a big gap between designers and users exists owning to the subjectivity of designers' experience. An objective model to recognize perceptual image of products is proposed by extracting low-level texture features of product pictures. To build up ground truth of perceptual image dataset, 210 chair samples are collected and rated by the students who are majored in industrial design using five-point semantic difference method to three pairs of perceptual image words. Several low-level texture features are explored and extracted. We adopt SVM (support vector machine) to classify perceptual image and SVR (support vector regression) to predict perceptual image score. The experiment results show the model is effective in perceptual image recognition. This model can be applied to help designers understand users' perceptual image better and accelerate design progress. © 2017 IEEE.

关键词： Support vector machines

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Content Based image Retrieval with Hadoop 2nd

引用

2nd international conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Gaber, Heba Marey, Mohammed Amin, Safaa E. Tolba, Mohamed F. Ain Shams Univ Comp & Informat Sci Cairo Egypt

ISBN: (纸本)9783319483085;9783319483078

Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It allows processing of extremely large video files or image files on data nodes. This can be used for implementing Content Based image Retrieval (CBIR) algorithms on Hadoop to compare and match query images to the previously stored terabytes of an image descriptors databases. This work presents the implementation for one of the well-known CBIR algorithms called Scale Invariant Feature Transformation (SIFT) for image features extraction and matching using Hadoop platform. It gives focus on utilizing the parallelization capabilities of Hadoop MapReduce to enhance the CBIR performance and decrease data input\output operations through leveraging Partitioners and Combiners. Additionally, image processing and computer vision tools such as Hadoop image processing (HIPI) and Open computer vision (OpenCV) are integration is shown.

关键词： Hadoop HIPI OpenCV Big data Crowd sourcing MapReduce Combiners Partitioners

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DARPA benchmark image processing on SIMD parallel machines

DARPA benchmark image processing on SIMD parallel machines

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1996 IEEE 2nd international conference on Algorithms and Architectures for Parallel processing (ICA3PP 96)

作者： Cucchiara, R Piccardi, M Univ of Ferrara Ferrara Italy

ISBN: (纸本)0780335295

The aim of this paper is to present a comparative analysis of the execution times of low-level vision algorithms on two different SIMD parallel machines. The set of algorithms is part of the DARPA image Understanding benchmark, a widely-accepted platform for performance comparison of parallel systems in the field of computer vision. The considered computer architectures represent two opposite solutions in terms of granularity in approaching the SIMD paradigm, one with a coarse-grain array of floating-point processors and the other with a fine-grain array of single-bit processing elements. For these reasons, the set of algorithms was implemented on both systems taking into account machine specificities. In this work some insights into implementation issues and a comparative analysis of the assessed execution times are presented.

关键词： parallel image processing SIMD arrays benchmark

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Comparative Analysis of Cluster Based Superpixel Segmentation Techniques 2

Comparative Analysis of Cluster Based Superpixel Segmentatio...

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2nd international conference on Trends in Electronics and Informatics, ICOEI 2018

作者： Patel, Soham Kadhiwala, Bintu Computer Engineering Department Sarvajanik College of Engineering and Technology Surat Gujarat India

ISBN: (纸本)9781538635704

Superpixel, becomes popular for use in computer vision applications, is an image patch that adheres well to image boundaries. It has better alignment with intensity edges than rectangular patch and also capable of carrying more information. Intuitively, superpixels segment similar pixels to create visually meaningful entities while completely reducing the number of primitives for consequent image processing tasks. The main objective of superpixel segmentation, also known as image over-segmentation, is to create a consistent pixels grouping. This paper aims to present an insight of existing cluster based superpixel segmentation algorithms. Additionally, the parametric comparison of these algorithms is highlighted to establish possible research direction. © 2018 IEEE.

关键词： Superpixels

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Multiclass-Classification of Algae using Dc-GAN and Transfer Learning 2

Multiclass-Classification of Algae using Dc-GAN and Transfer...

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2nd international conference on image processing and Robotics (ICIPRob)

作者： Abdullah Khan, Ziaullh Mumtaz, Wajid Mumtaz, Abdul Samad Bhattacharjee, Subrata Kim, Hee-Cheol Inje Univ Dept Digital Antiaging Healthcare Gimhae South Korea Natl Univ Sci & Technol Dept Comp Engn Islamabad Pakistan Quaid I Azam Univ Dept Plant Sci Islamabad Pakistan Inje Univ Dept Comp Engn Gimhae South Korea

ISBN: (纸本)9781665407717

The growth of algae is a natural process and highly increase in concentration has a bad impact on water bodies as well as other creatures. The monitoring and classification of algae by using the traditional method is a tedious and time-consuming task. The reliable and robust development of the alternative method is crucial to do these tasks, however, advanced machine learning, computer vision, and deep learning are excessively used to address this problem. In this paper, we have used the transfer learning technique, in which various pre-train models are used to train on our custom dataset. We conducted a series of experiments to classify genera of harmful algae bloom (HAB). Furthermore, we compare each pre-train architecture performance on our unique dataset. As the transfer learning model needs more data to train it, we used a direct generative adversarial network (Dc-GAN) to enhance the quantity of data. In this work the four popular pre-train models are used, namely, VGG-16, Alex Net, Google Net, and ResNet-18. Among these, the ResNet-18 model performed well with the highest accuracy of 97.10%. The transfer learning model approach would be an effective tool for rapid operational response to algae bloom events. The experimental results show that the transfer learning method is more effective and reliable to detect and classify algae.

关键词： transfer learning algae computer vision Dc-GAN

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Some studies on camera calibration and image processing for mobile robot navigation

Some studies on camera calibration and image processing for ...

引用

2nd Indian international conference on Artificial Intelligence, IICAI 2005

作者： Hui, Nirrnal Baran Pratihar, Dilip Kumar Department of Mechanical Engineering Indian Institute of Technology Kharagpur- 721 302 India

ISBN: (纸本)0972741216

Camera calibration and image processing, two important issues of mobile robot navigation, are studied in the present work. image of an object is captured by using a CCD camera and stored in the computer memory with the help of a vision board. Three image processing algorithms such as median filtering, thresholding, and edge detection are applied to analyze the image. The optimal camera parameters and threshold value are determined optimized by using a genetic algorithm to minimize the distorted image plane error (DIPE). A case study is shown. Copyright © IICAI 2005.

关键词： CCD cameras

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A cost effective method for automobile security based on detection and recognition of human face 2

A cost effective method for automobile security based on det...

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2nd international conference on image, vision and Computing, ICIVC 2017

作者： Gulzar, Kanza Jun, Sang Tariq, Omar School of Software Engineering Chongqing University Chongqing401331 China

ISBN: (纸本)9781509062379

The proposed system for automobile security is a face detection and recognition application that control the automobile to be operated or restricted. This system is established for all types of door locks and particularly for automobiles. By using this methodology, resulted a better quality product with respect to documentation standards, code optimization, user acceptance due to adequately efficient, maintainable, consistence and cheaper software. With the up-to-date and influential technology, the system is not only expected to be workable, but also sufficiently efficient in terms of execution speed and response time. In our work we studied the limitations of the face recognition techniques and try to give the best solution with benefits of both fisherface and eigenface recognition methods at minimum cost. As the main aim of this work is to develop a low cost security system for a common man to keep his automobile save but it does not only protects the automobile but also assist in catching the thief by saving his image. This paper defines an efficient face detection and recognition algorithms using a cross platform of EmguCV *** wrapper to the Intel OpenCV library, Visual Studio and C#.Net, with hardware components essentially a computer Stick and an Arduino uno. © 2017 IEEE.

关键词： computer vision

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Principle Component Analysis Based Hand Gesture Recognition for Android Phone Using Area Features 2

Principle Component Analysis Based Hand Gesture Recognition ...

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2nd international conference on Multimedia and image processing (ICMIP)

作者： Qiao, Yu Feng, Zhiquan Zhou, Xiaoyan Yang, Xiaohui Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China

ISBN: (纸本)9781509059546

Hand gesture has been used in different applications and implemented on different platforms. Hence, a real-time and robust approach with high recognition accuracy is important in smart devices. This paper describes a novel method of hand gesture recognition using Principle Component Analysis (PCA) implemented in Android phone. Area features are adopted to do the gesture recognition. It solves these problems such as different size of gesture image captured, different angle of gesture's rotation and flipped gesture. Experiment results show that the average recognition rate of the proposed method is 93.95%. Moreover, the computation complexity of the proposed method is low, and it can be adopted in real-time applications.

关键词： hand gesture recognition area feature human-computer interaction Principal Component Analysis machine learning

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Segmentation of images Using Deep Learning: A Survey 2

Segmentation of Images Using Deep Learning: A Survey

引用

2nd international conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Sharif, Hanan Rehman, Faisal Rida, Amina Sharif, Aman Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan Islamabad Pakistan

ISBN: (纸本)9781665498197

In this present era of technology, segmentation of image is a fundamental and significant component of computer vision and image processing applications. These applications include medical image analysis, compression of images, surveillance, security footage, and many others. Researchers have proposed and developed various models, theories, and algorithms in this advancing field of image segmentation. Due to the success of a wide range of computer vision applications, work whose goal was to use deep learning algorithms for the development and usability of image segmentation was completed not long ago. In this study, a detailed review of the literature on image segmentation is being written, which covers a vast and extensive amount of work done by researchers at the time of writing this review. This review investigates the similarity and differences between the proposed models by researchers in the past. Along with similarities, we have also discussed the strength and challenges of the model under the umbrella of deep learning algorithms. In the end, future work has been discussed along with keeping in view the past work of researchers. © 2022 IEEE.

关键词： image segmentation

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