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Computer Vision for image Understanding: A Comprehensive Review 1st

Computer Vision for Image Understanding: A Comprehensive Rev...

引用

1st international conference on Advances in Emerging Trends and Technologies, ICAETT 2019

作者： Jácome-Galarza, Luis-Roberto Realpe-Robalino, Miguel-Andrés Chamba-Eras, Luis-Antonio Viñán-Ludeña, Marlon-Santiago Sinche-Freire, Javier-Francisco Escuela Superior Politécnica del Litoral Facultad de Ingeniería en Electricidad y Computación CIDIS 09-01-5863 Guayaquil Ecuador Carrera de Ingeniería en Sistemas Loja Ecuador Universidad Nacional de Loja Carrera de Ingeniería en Sistemas Loja Ecuador

ISBN: (纸本)9783030320218

Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language processing (using Recurrent Neural Networks and Long Short-Term Memory) plus image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. © 2020, Springer Nature Switzerland AG.

关键词： Computer vision

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image processing and machine learning techniques to detect and classify paddy leaf diseases: A review 1

引用

1st international conference on machine learning and Information processing, ICMLIP 2019

作者： Singh, Jay Prakash Pradhan, Chittaranjan Das, Srikanta Charana School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Management Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9789811518843

ISBN: (纸本)9789811518836

In Asian countries, paddy is one of the major staple foods, and the agricultural sector is the largest employer in India’s economy. Hence, an effective mechanism should be adopted for food security and its production. Due to infections caused by pests like virus, fungus and bacteria, there is a huge loss in quality and quantity of the rice. This results in a huge loss to the farmers. This survey presents the different image processing techniques for paddy disease identification and further classification. The challenges involved in each step of diseases detection and classification are analyzed and discussed. image preprocessing, segmentation, feature extraction and classification are the main stages involved in paddy disease identification and classification. All these four stages are well addressed here. This survey gives a complete overview along with the challenges of each stage and also focuses the research challenge and research gap. This survey concludes that automatic paddy leaf disease detection, and classification requires much more advancement. There is a need for more advanced techniques to automate the system effectively and efficiently. © Springer Nature Singapore Pte Ltd 2020.

关键词： Surveys

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Pix2Pix-based stain-to-stain Translation: A Solution for Robust stain Normalization in Histopathology images Analysis 1

Pix2Pix-based Stain-to-Stain Translation: A Solution for Rob...

引用

1st international conference on machine Vision and image processing, MVIP 2020

作者： Salehi, Pegah Chalechale, Abdolah RAZI University Image Processing Research Lab Kermanshah Iran

ISBN: (纸本)9781728168326

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to Interpretive disparity among pathologists more is one of the main challenges in designing robust and flexible systems for automated analysis. To address the staining color variations, several methods for normalizing stain have been proposed. In our proposed method, a stain-to-stain Translation (stst) approach is used to stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images, which learns not only the specific color distribution but also the preserves corresponding histopathological pattern. We perform the process of translation based on the pix2pix framework, which uses the conditional generator adversarial networks (cGANs). Our approach showed excellent results, both mathematically and experimentally against the state of the art methods. We have made the source code publicly available 1. © 2020 IEEE.

关键词： Generative adversarial networks

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Alzheimer's Disease Prediction Using Convolutional Neural Network Models Leveraging Pre-existing Architecture and Transfer learning 9

Alzheimer's Disease Prediction Using Convolutional Neural Ne...

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Joint 9th international conference on Informatics, Electronics and Vision (ICIEV) / 4th international conference on Imaging, Vision and pattern recognition (ICIVPR)

作者： Abed, Mahjabeen Tamanna Fatema, Umme Nabil, Shanewas Ahmed Alam, Md Ashraful Reza, Md Tanzim BRAC Univ Dept Comp Sci & Engn 66 Mohakhali Dhaka 1212 Bangladesh

ISBN: (纸本)9781728193311

Early Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) can be diagnosed through proper examination of several brain biomarkers. In recent times, several high-dimensional classification techniques have been suggested to discriminate between AD and MCI on the basis of T1-weighted MRI of patients. These techniques have been implemented mostly from scratch, making it really difficult to achieve any meaningful result within a short span of time. Therefore, the classification of AD is usually a very daunting and time-consuming task. In our study, we trained high dimensional Deep Neural Network (DNN) models with transfer learning in order to achieve meaningful results very quickly in terms of detecting AD from fMRI image. The fMRI image dataset has been collected from Alzheimer's Disease Neuroimaging Initiative (ADNI). We have used three different DNN models for our study: VGG19, Inception v3, and ResNet50 to classify AD, MCI, and Cognitively Normal (CN) patients. Firstly, we implemented some pre-processing steps on the images and divided them into training, testing, and validation sets. Secondly, we initialized these DNN models with the weights from pre-existing models trained on the imageNet dataset. Finally, we trained and evaluated all the DNN models. After a relatively short amount of training (15 epochs), we achieved an approximate of 90% accuracy with VGG19, 85% accuracy with Inception v3, and 70% with ResNet50. Thus, we achieved excellent classification accuracy in a very short time with our research. Contribution - Classification between early-stage and late- stage AD at improved accuracy with transfer learning.

关键词： VGG19 Inception Residual Network (ResNet) Convolutional Neural Network (CNN) Transfer learning Magnetic Resonance Imaging (MRI)

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An Adaptive structural learning of Deep Belief Network for image-based Crack Detection in Concrete structures Using SDNET2018 1

An Adaptive Structural Learning of Deep Belief Network for I...

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1st international conference on image processing and Robotics (ICIP)

作者： Kamada, Shin Ichimura, Takumi Iwasaki, Takashi Prefectural Univ Hiroshima Res Org Reg Oriented Studies Adv Artificial Intelligence Project Res Ctr Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan Prefectural Univ Hiroshima Res Org Reg Oriented Studies Adv Artificial Intelligence Project Res Ctr Fac Management & Informat SystMinami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan Mitsui Consultants Co Ltd MCC Lab Infrastruct Syst Grp Shinagawa Ku Gate City Ohsaki West Tower 15F1-11-1 Osaki Tokyo 1410032 Japan

ISBN: (纸本)9781728165417

We have developed an adaptive structural Deep Belief Network (Adaptive DBN) that finds an optimal network structure in a self-organizing manner during learning. The Adaptive DBN is the hierarchical architecture where each layer employs Adaptive Restricted Boltzmann machine (Adaptive RBM). The Adaptive RBM can find the appropriate number of hidden neurons during learning. The proposed method was applied to a concrete image benchmark data set SDNET2018 for crack detection. The dataset contains about 56,000 crack images for three types of concrete structures: bridge decks, walls, and paved roads. The fine-tuning method of the Adaptive DBN can show 99.7%, 99.7%, and 99.4% classification accuracy for three types of structures. However, we found the database included some wrong annotated data which cannot be judged from images by human experts. This paper discusses consideration that purses the major factor for the wrong cases and the removal of the adversarial examples from the dataset.

关键词： Adaptive structural learning Deep Belief Network Deep learning Crack Detection SDNET2018 Adversarial Data

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PSI (ψ) Invariant Features for Face recognition

PSI (ψ) Invariant Features for Face Recognition

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international conference on Mathematical Modelling and Scientific Computing

作者： Cholin, Ajaykumar S. Vinay, A. Bhat, Aditya D. Deshpande, Arnav Ajay Murthy, K. N. B. Natarajan, S. PES Univ Ctr Pattern Recognit & Machine Intelligence 100 Feet Ring RdBanashankari Stage 3 Bengaluru 560085 Karnataka India

ISBN: (纸本)9789811513381;9789811513374

Over last few decades, mathematics has played a crucial role in developing efficient algorithms for Face recognition (FR) used in biometric systems. FR using machine learning (ML) techniques has impacted FR systems tremendously, towards efficient and accurate models for FR. Existing FR systems used in biometrics use ML techniques to learn patterns in the images by extracting various features from them and often require pre-processed face image data for the learning process. In this paper, we have used various pre-processing techniques and compared them in the deployed FR framework. It was observed that the steerable Pyramid (SP) filter was the most efficient pre-processing technique among all techniques used for pre-processing in this work. Though existing feature extraction methods such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), ORB (Oriented FAst and Rotated BRIEF) have been used in the past, they have not been accurate enough in various vision based biometric systems. Hence, a novel PSI (Pose Scale and Illumination) invariant SURF-RootSIFT technique is proposed by extending the well known SIFT-RootSIFT feature extraction technique which is achieved by calculating the Bhattacharya Coefficient between the feature vectors. A framework which uses the proposed novel feature extraction technique is deployed in this work. This paper demonstrates that the novel SURF-RootSIFT based framework is proven to perform more accurately and efficiently than the other techniques, with 99.65, 99.74 and 97.93% accuracy on the Grimace, Faces95 and Faces96 databases respectively.

关键词： Face recognition image pre-processing SIFT SURF RootSIFT Bhattacharya coefficient VLAD

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A Novel Enhancement and Segmentation of Color Retinal image Based on Fuzzy Measure and Fuzzy Integral 1st

A Novel Enhancement and Segmentation of Color Retinal Image ...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam Brainware University Barasat Kolkata700124 India University of Calcutta Kolkata700098 India Netaji Subhash Engineering College Kolkata700152 India

ISBN: (纸本)9789811390418

image enhancement and segmentation are predominating methods in image processing and are widely used in ophthalmology for the diagnosis of various eye diseases such as diabetic retinopathy, glaucoma. Especially, retinal image segmentation is vastly required to extract certain features that can facilitate in diagnosis and treatment of eye. Due to the acquisition process, color retinal images can suffer from poor contrast, thus enhancement is essential in ophthalmology. In this work, a novel fuzzy color image enhancement and segmentation technique has been suggested to overcome the problem of low and variation of contrast, and segment in retinal images. This paper provides a special fuzzy computation on retinal image such as fuzzy measure which is used for enhancement and fuzzy integral is applied for segmentation. The proposed algorithm can accomplish excellent contrast and a segment of an image object that endows feasibility in diagnosis from retinal image. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ophthalmology

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A study on Reversible image Watermarking Using Xilinx System Generator 1st

A Study on Reversible Image Watermarking Using Xilinx System...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Das, Subhajit Sunaniya, Arun Kumar National Institute of Technology SilcharAssam788010 India

ISBN: (纸本)9789811390418

The present work explains the design and implementation of the difference expansion method based reversible image watermarking using Xilinx System Generator and MATLAB. The method represents structural design for hiding information bits into the cover image by depending on the difference of neighborhood pixels of the cover image. The MATLAB-Simulink and Xilinx System Generator tools are a most preferable choice to improve the progress time and hardware resources by reducing the complication of image representation over FPGA environment. The reversible image watermarking using difference expansion is verified by taking a (4 × 4) sized test image and can be applicable for the larger size of cover images. The outcome of the results shows that the MATLAB-Simulink and Xilinx System Generator can be combined all the way through the graphical user interface by proposed structural design for image processing applications. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Simulink

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image processing Using Case-Based Reasoning: A Survey 1st

Image Processing Using Case-Based Reasoning: A Survey

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1st international conference on Electronic Systems and Intelligent Computing, ESIC 2020

作者： Barman, Rahul Biswas, Saroj Kr. Sarkar, Sunita Purkayastha, Biswajit Soni, Badal Department of Computer Science and Engineering National Institute of Technology Silchar SilcharAssam788010 India Department of Computer Science Assam University SilcharAssam India

ISBN: (纸本)9789811570308

Case-based reasoning (CBR) is a technique which solves a problem using past experiences, where a case base stores these past experiences called cases. CBR is used to solve different kinds of problems where past information is available. With the advent of modern and efficient digital tools, huge amount of image data is being captured. Hence, huge amount of image data is stored in different application fields. Different organizations use CBR to find valuable and useful information from this image data. Therefore, in the recent times, there is a huge use of CBR in image data applications. This paper discusses the framework of CBR in image processing and reports different application models in different applications. From literature, it is observed that the performance of CBR in image processing is significant and acceptable. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Disorder detection of tomato plant(solanum lycopersicum) using IoT and machine learning 1

Disorder detection of tomato plant(solanum lycopersicum) usi...

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1st international conference on Emerging Electrical Energy, Electronics and Computing Technologies 2019, ICE4CT 2019

作者： Khan, Saiqa Narvekar, Meera Computer Engineering Department M.H.SabooSiddik College of Engineering Mumbai India Computer Engineering Department D.J.Sanghvi College of Engineering Mumbai India

India is an agricultural country and this sector accounts for 18 percent of India's GDP. This sector is the backbone of the country and focuses on better yield by using pesticides and fertilizers to prevent plant disorders which directly affects the yield. The primary method adopted for detecting disorders is through visual observation and other methods are quite expensive. Many authors have proposed solutions to this problem such as IoT for grapes, or system designed for accurate disorder detection using machine learning with limited scope. This paper showcases a prototype that uses multi-modal analysis through sensor data, computer vision. The main objective of this system is to accurately detect disorders in tomato plant using IoT, machine learning, Cloud Computing, and image processing. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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