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2nd international conference on computer Science and Software Engineering (CSSE)

作者： Ai, De-Zhi Wu, Yuan Gu, Li-Dan Wang, Chao Zhejiang Ocean Univ Sch Math Phys & Informat Sci Zhoushan Zhejiang Peoples R China

ISBN: (纸本)9781450371728

In the field of optics and computer vision, image defogging has attracted much attention due to its wide application. On the premise of fully analyzing the characteristics of foggy images, this paper proposes a new method to improve the estimation of atmospheric light by improving the divergence information. Experimental results show that compared with the traditional defogging algorithm, this algorithm has a faster processing speed, and the recovered image details are prominent, which can effectively remove the haze in the image and has a better visual effect.

关键词： image dehazing Dark channel prior Atmospheric light Divergence

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Age Invariant Face Recognition using Frangi2D Binary Pattern 19

Age Invariant Face Recognition using Frangi2D Binary Pattern

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2nd international conference on image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Afroze, Sabah Beham, M. Parisa Tamilselvi, R. Maraikkayar, S. M. Seeni Mohamed Aliar Rajakumar, K. Sethu Inst Technol Dept Elect & Commun Engn Pulloor 626115 Tamil Nadu India

ISBN: (纸本)9781450360920

The field of computer vision is devoted to discovering algorithms, data representations and computer architectures that embody the principles underlying visual capabilities. computer vision is an interdisciplinary field that deals with how computers can be made for gaining high level understanding from digital images or videos. While very promising result has been shown on face recognition related problems, age invariant face recognition still relics a challenge. Facial appearance of a human varies over time, which results in substantial intra-class variations. In order to address this problem, we propose Frangi2D method for normalization, Linear Binary pattern (LBP) for feature extraction and Sparse Representation Classifier (SRC). Extensive results on a well-known public domain face aging dataset: MORPH. The experimental results show the superiority of our proposed method in age invariant face recognition.

关键词： Age Invariant Face recognition Frangi 2D sparse representation classifier (SRC) Local binary pattern (LBP)

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Rough Terrain Autonomous Vehicle Control Using Google Cloud vision API 2

Rough Terrain Autonomous Vehicle Control Using Google Cloud ...

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2nd international conference on Power and Embedded Drive Control, ICPEDC 2019

作者： Sugadev, M. Yogesh Sanghamreddy, Prithvi Kumar Samineni, Sai Krishna Sathyabama Institute of Science and Technology ECE Department Chennai-119 India

ISBN: (纸本)9781728124148

computer vision (CV) represents a technology using which information contained in a visual scenery is obtained by capturing and digital processing of the images. computer vision is becoming more popular and widely used in automated vehicles, robotics to emulate human vision capability and industrial automation. Google cloud vision API is a remote real time image processing platform to process, classify and acquire required metadata from image and videos. In this work, design and implementation of a rough terrain autonomous vehicle using Rocker Bogie mechanism and Google cloud vision API for obstacle avoidance is presented. Rocker Bogie mechanism is employed for maneuvering the robot on rough terrains. © 2019 IEEE.

关键词： computer vision

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Classification of Varietal Type of Philippine Rice Grains Using image processing through Multi-View 3d Reconstruction 2019

Classification of Varietal Type of Philippine Rice Grains Us...

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2nd international conference on Computing and Big Data (ICCBD)

作者： Buenaventura, Jan Rei S. Kobayashi, Jun T. Valles, Larah Mae P. De Goma, Joel C. Balan, Ariel Kelly D. Mapua Univ Sch Informat Technol Makati Philippines

ISBN: (纸本)9781450372909

Number of previous works have been carried out to study the classification of rice in the flat 2D image. In computer vision, 3D reconstruction is a field that has great theoretical research value. This paper attempts to see the performance by using features extracted from the 3D reconstructed image of a rice grain in classifying different varieties of it. We identify 250 rice grains belonging to five (5) varietal groups of rice in the Philippines. The data gathered was in the form of a video from a camera then it was split into images. After applying image enhancement operations, the images where used to build the 3D model using Agisoft Photoscan software. After the reconstruction, 18 color features and 21 texture features were extracted from the image. After the feature selection, 32 features were selected for this study. The proposed method uses BPNN architecture to classify the rice grain's varietal type.

关键词： Rice classification Color analysis Texture analysis 3D model reconstruction BPNN classifier computer vision image processing

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vision-based Automatic Detection of Suspended Solids in Bottled Mineral Water 2

Vision-based Automatic Detection of Suspended Solids in Bott...

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2nd IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Sheng, Ziye Zhang, Yunwei Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9781728151021

Bottled mineral water must be tested to determine whether there exist suspended solids before leaving the factory. At present, it is mainly observed by manual methods. This method is time-consuming and laborious. It relies on artificial subjective feelings, and the detection effect is unsatisfying. Aiming at this problem, an automatic detection method for testing suspended solids in bottled mineral water based on computer vision technology is established in this paper, which includes image acquisition, object recognition of suspended solids, quantity statistics, size estimation, and other image analysis processing. On the basis of this, an automatic detection device for suspended solids in bottled mineral water is designed, and the testing of suspended solids in bottled mineral water is accomplished. The testing results show that the method proposed in this paper can find out suspended solids in bottled mineral water both qualitatively and quantitatively with high precision. The statistics for quantity of suspended solids is accurate. The above device and method could be applied to the detection of bottled mineral water before water leaving the factory with the features of accurate detection, saving labor, improving work efficiency, and easy operation.

关键词： computer vision Bottled mineral water Suspended solids detection image processing

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image Segmentation in Constrained IoT Servers 2nd

Image Segmentation in Constrained IoT Servers

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2nd international conference on Recent Trends in Advanced Computing, Disruptive Innovation (ICRTAC-DTI)

作者： Sharma, Nishant Sultana, Parveen H. Singh, Rahul Patil, Shriniwas Pareek, Sumit Vellore Inst Technol Vellore 632014 Tamil Nadu India

image segmentation forms an important concept in the computer vision technology. image segmentation breaks the image into boundaries that differentiate meaningful components. For computer vision to realize its full potential it is essential that the image segmentation algorithms give accurate results in a fast and efficient way. In hierarchical architecture based IoT networks set up to "see" the world, methods of computer vision need more analysis. The need for low cost setup for IoT networks in terms of memory and their computational capabilities demands research for developing methods that are resource sensitive and can be successfully integrated into such networks of low end IoT servers. Addressing this need, a refined graph cut segmentation technique for low to medium resolution images and for constrained devices is presented in the paper. Implementation and analysis of the refined graph cut segmenter for linux based IoT servers is discussed. A comparison with the contemporary segmentation methods under similar constraints is also presented. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Foreground extraction Graph-cut segmentation IoT image processing MM-cut max-flow RGCS Segmentation

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Measurement of Stain Area in Metal Surface with Particle Analysis 2

Measurement of Stain Area in Metal Surface with Particle Ana...

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2nd international conference on Applied Information Technology and Innovation (ICAITI)

作者： Riandini Dewantoro, Shahnan Kamil Istoni, Reza Politekn Negeri Jakarta Dept Elect Engn KampusUI Depok Indonesia

ISBN: (纸本)9781728130163

Measurement of the object dimension in terms of digital image processing is one of the computer vision applications. The digital image is obtained from the camera / webcam, where calibration and settings are carried out to convert the output of the camera which is a pixel of images into physical unit values (length and area). The distance between the two points on the image is known by counting the number of pixels in the image. Regression analysis method is used to get the conversion equation between variable number of pixels into units of length. The conversion equation is used to measure the length and area of 10 pieces of template cut by a CNC machine with sizes from 1x1cm to 10x10cm. The validity of the pixel calibration results in units of length was tested by measuring 10 template sizes. Measurement of the area of the stain / defect due to corrosion on metal plates is carried out on a number of samples with a random stain size, by counting the number of pixels that exist with the help of the developed LabVIEW-NI vision application. Data acquisition and analysis systems have worked in accordance with the design concept, pixel data can be measured and converted into physical units of millimeters or centimeters, with a measurement resolution of the instrument of 0.1220 mm.

关键词： image processing computer vision digital image corrosion regression analysis

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A Critical Review of Object Detection using Convolution Neural Network 2

A Critical Review of Object Detection using Convolution Neur...

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2nd international conference on Communication, Computing and Digital Systems (C-CODE)

作者： Nisa, Sehar Un Imran, Muhammad Shaheed Zulfikar Ali Bhutto Inst Sci & Technol SZ Islamabad Pakistan

ISBN: (纸本)9781538696095

To recognize an object, for a human, is an easy task but for machines, to perform the same task with same efficiency is a complex task. For computer systems, images are sets of numeric values that have no meaning in itself. To make these numbers useful, diverse techniques have been proposed. Comparative to others, deep learning approaches achieved state-of-the-art performance in many computer vision applications, such as object detection, image classification, image retrieval, human pose estimation. To detect object of interest, Convolutional Neural Network (CNN) has been observed widely successful method. Few factors are there to get better accuracy and performance for instance efficient model, larger datasets and hardware support. This study aims to review CNN methods for object detection by highlighting the contribution and challenges from few recent research papers. Also how well to use these CNN techniques in combination to other methods for best suited results with other. Better performance such as increased accuracy, fast processing reduce error rates also introduced few new concerns and issues in parallel regarding the discussed methods such as time consumption, anonymous behavior of Neural Network. To address these issues a conceptual model is presented using CNN and Lease Square Support Vector Machine (LS-SVM).

关键词： computer vision Convolutional Neural Network detection image Lease Square Support Vector Machine object

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Different shape and color targets detection using auto indexing images in computer vision system 2

Different shape and color targets detection using auto index...

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2nd international conference on Sustainable Engineering Techniques (ICSET)

作者： Taban, D. A. Al-Zuky, A. A. AlSaleh, A. H. Mohamad, H. J. Mustansiriyah Univ Dept Phys Coll Sci Baghdad Iraq Mustansiriyah Univ Dept Comp Sci Coll Sci Baghdad Iraq

One of the main challenges in computer vision is to determine the number of different types, shapes, locations and color targets within the image plane for use in computer control systems. In this study, an algorithm introduced to detect the number of targets (one or two), their shapes (square or circle) and colors (red, green or blue). A new technique presented as a digital indexing code table to present the studied color targets images. The indexing table technique depends on decimal and binary numbers. In this study, there were 42 different cases represents all the input images. There is a special case considered for the similarity of input images in case it has the same shape and color, but a change in rotation and space between two image targets. This solved using referencing to indicate the same target in each case. Thus, the classification results were 100% for the presented algorithm for all input cases.

关键词： shapes and color recognition area calculation object indexing image processing computer vision classification system

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Making the V in VQA Matter: Elevating the Role of image Understanding in Visual Question Answering

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international JOURNAL OF computer vision 2019年第4期127卷 398-414页

作者： Goyal, Yash Khot, Tejas Agrawal, Aishwarya Summers-Stay, Douglas Batra, Dhruv Parikh, Devi Georgia Tech Atlanta GA 30332 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Army Res Lab Adelphi MD USA Facebook AI Res Menlo Pk CA USA

The problem of visual question answering (VQA) is of significant importance both as a challenging research question and for the rich set of applications it enables. In this context, however, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in VQA models that ignore visual information, leading to an inflated sense of their capability. We propose to counter these language priors for the task of VQA and make vision (the V in VQA) matter! Specifically, we balance the popular VQA dataset (Antol et al., in: ICCV, 2015) by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset is publicly available at http://***/ as part of the 2nd iteration of the VQA Dataset and Challenge (VQA v2.0). We further benchmark a number of state-of-art VQA models on our balanced dataset. All models perform significantly worse on our balanced dataset, suggesting that these models have indeed learned to exploit language priors. This finding provides the first concrete empirical evidence for what seems to be a qualitative sense among practitioners. We also present interesting insights from analysis of the participant entries in VQA Challenge 2017, organized by us on the proposed VQA v2.0 dataset. The results of the challenge were announced in the 2nd VQA Challenge Workshop at the IEEE conference on computer vision and Pattern Recognition (CVPR) 2017. Finally, our data collection protocol for identifying complementary images enables us to develop a novel interpretable model, which in addition to providing an answer to the given (image, question) pair, also provides a counter-example ba

关键词： Visual question answering VQA VQA challenge

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