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International conference on image Information processing

作者： Neelam Dahiya Sheifali Gupta Sartajvir Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665433624

Remote sensing plays a vital role in analyzing land-use and land-cover (LULC) areas using hyperspectral imagery which was not feasible with multispectral imagery. The analysis of LULC is an important factor for environmental application. Classification is one of the methods used for the categorization of different types of class cateogries. The performance evaluation of various classifiers using hyperspectral dataset is essential to be performed for the categorization of various classes. Therefore, the main aim of this paper is to analyze and implement the various supervised classifiers such as random forest (RF), neural network (NN), and minimum distance classifier (MDC). The hyperspectral dataset has been used for these classifiers over a part of Haryana and Uttar Pradesh states, India. The results have shown that NN (88%) algorithm has achieved higher accuracy than other algorithms. These experimental outcomes highlights the potential of NN in handling complex problems and useful in the mapping of changes over LULC using hyperspectral imagery. This study is beneficial in applications such as crop monitoring, mapping seasonal variations, and snow detection.

关键词： Performance evaluation statistical analysis Snow Crops Artificial neural networks Information processing Classification algorithms

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Classifying Quality Grading and Size of Allium Cepa Using Digital image processing Algorithms

Classifying Quality Grading and Size of Allium Cepa Using Di...

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2020 International conference on Communications, Information System and Computer Engineering, CISCE 2020

作者： Deplomo, Bhai Nhuraisha I. Dela Cruz, Jennifer C. Balbin, Jessie R. Jose Rizal University Manila Philippines University of Makati Manila Philippines

ISBN: (纸本)9781728197616

This study introduced the system that can classify the size, color, and texture of the onion based on Philippine National Standard (PNS) using image processing methods such as Gaussian Blur, Canny Edge, Erosion, Dilation, Contouring, Color Masking, Pixel per metric ratio, RGB, and Blob. The Blob algorithm can detect the texture of the onion according to its grade;Grade A(Grade 1), Grade B (Grade 2), Grade C (Combination of Grade 1 and Grade 2), and Reject. The researcher conducted series of experimental testing to compare the quality grading using manual and automatic classification of bulb onion. The statistical method used for this study is T-test to compare the manual sorting and automated sorting. The t-values for red onion based on local standards is 0.1284, while the yellow onion is 0.0178. The results are less than the critical value of 2.01. It means that the null hypothesis is accepted. Over all, the system can determine the size, color, and texture bulbs of onion using image algorithms. © 2020 IEEE.

关键词： Pixels

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image restoration via DOST and total variation regularisation

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IET image processing 2019年第3期13卷 458-468页

作者： Bini, A. A. Indian Inst Informat Technol Kottayam Pala Kerala India

In this study, the author proposes a total variational (TV) driven image restoration using discrete orthogonal Stockwell transform (DOST) when the noise (in the image) is an outcome of a Poisson process. Stockwell transform or S-transform (ST) is well known for its efficiency in resolving spatio-frequency components with high accuracy compared with many other transforms such as short-term Fourier transform, wavelet transform etc. This property of ST makes it more suitable for many image processing applications such as image restoration and image inpainting. By deriving the objective function and constraints of the optimisation problem (image restoration problem) based on the ST coefficients, the model becomes more robust in terms of preserving high resolution in the spatio-frequency domain. images are modelled as an outcome of a Poisson process in many medical and telescopic imaging applications. The Poisson noise corruption is mainly due to the lack of a sufficient number of photons to reconstruct the data. In this study, corrupted images are restored due to the Poisson process (by which the data is formed) using the DOST under a non-local TV framework. The model is analysed and compared with the state-of-the-art Poisson noise removal methods using visual and statistical measures.

关键词： discrete transforms frequency-domain analysis image reconstruction image resolution image restoration optimisation stochastic processes

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Visible to Thermal image Synthesis using Light Weight Pyramid Network

Visible to Thermal Image Synthesis using Light Weight Pyrami...

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International conference on Emerging Technologies, ICET

作者： Shehryar Imtiaz Imtiaz Ahmad Taj Rab Nawaz Capital University of Science & Technology Islamabad Pakistan VisPRS Research Group Capital University of Science & Technology Islamabad Pakistan

ISBN: (纸本)9781665494380

We investigate Lightweight Pyramid Network as a general–purpose solution for image-to-image synthesis. Already employed techniques that are based upon deep convolutional neural networks (CNNs) have found reasonable success but with the trade-off of a large number of parameters that eventually result in high computational costs. In these methods, post processing of various types is incorporated as well to further refine the transformed image, thus making the whole process cumbersome and time taking. In this paper, we have made use of lightweight pyramid network (LPNet) for image synthesis that was primarily used for image deraining. We find that by using Laplacian-Gaussian image pyramid decomposition coupled with reconstruction and calculation of SSIM along with neural network, the heat signature in the resulting synthesized thermal image becomes much more enhanced and at the same time contours of various image objects stays prominent even without the use of any post processing techniques. The computations for training become less intensive due to the use of a shallow network. We further prove the efficacy of our approach by doing SSIM, PSNR and UQI Quantitative Analysis.

关键词： Training Laplace equations image synthesis statistical analysis Surveillance Thermal decomposition Neural networks

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Recent advances in large scale whole body MRI image analysis: Imiomics 20

Recent advances in large scale whole body MRI image analysis...

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5th International conference on Sustainable Information Engineering and Technology, SIET 2020

作者： Strand, Robin Ekström, Simon Breznik, Eva Sjöholm, Therese Pilia, Martino Lind, Lars Malmberg, Filip Ahlström, Håkan Kullberg, Joel Dept. of Information Technology Uppsala University Sweden Dept. of Surgical Sciences Uppsala University Sweden Dept. of Medical Sciences Uppsala University Uppsala Sweden

ISBN: (纸本)9781450376051

Due to the massive amount of medical image data being made available, in research and clinical work, computer-aided tools are valuable and have a great potential for a sustainable work situation for physicians and for generating disease understanding. High-end methods in the present era of big data and artifical intelligence are designed to efficiently find patterns in large scale image data. The amount of data is today often too big to be parsed by human experts, and computer-assisted methods often perform at least as well as human experts on well-defined problems where it is possible to quantify performance by a loss function. This paper gives an overview of a computer-assisted method, Imiomics. Imiomics enables statistical analyses of relations between whole body image image data in large cohorts and other non-imaging data, at an unprecedented spatial resolution. Its usefulness in medicine is illustrated by a number of medical applications, and some aspects of technical development that enable the analysis is also presented. We conclude that computer-assisted methods, such as Imiomics, are essential for efficient processing of the huge amount of data in today's medical research and, to some extent, clinical practice. © 2020 Owner/Author.

关键词： Medical applications

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A comparative study of data mining, digital image processing and genetical approach for early detection of liver cancer

A comparative study of data mining, digital image processing...

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2020 IEEE International conference on Computing, Power and Communication Technologies, GUCON 2020

作者： Sharma, Meenu Parveen, Rafat Jamia Millia Islamia Department of Computer Science New Delhi110044 India

ISBN: (纸本)9781728150703

Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. The largest internal organ of our body is the liver. It performs many of the life sustaining functions and elementarily has effect on every physiological process occurring in our body. Liver is the second most organ which is metastatistically affected by cancer. Now a days, liver cancer becomes the main cause of death worldwide. At that time, the early prediction of liver cancer becomes necessary for increasing patient survival. In this paper, a comparison is made to know which method from genetic, data mining and digital image processing is best suited for early detection of liver cancer. After doing comparison, in this paper we decide to explore genetical approach. All the statistical methods work on microarray expression dataset and come under the category of genetical approach. Microarray data analysis evolved as an advanced research topic during recent decades. The key reason for analyzing microarray dataset is to identify and predict the gene that displays various values of expression under various testing conditions. The purpose of this research paper is to examine various forms of parametric statistical methods proposed to analyze data on microarray expression to classify the genes expressed differently and to investigate the mechanism of disease growth. Additionally, we have estimated the best situation for each system where they are doing better. In our research, we use the simulated datasets to thoroughly analyze and compare the various types of parametric methods. © 2020 IEEE.

关键词： Data mining

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The Pitfalls of Simplicity Bias in Neural Networks 34

The Pitfalls of Simplicity Bias in Neural Networks

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34th conference on Neural Information processing Systems (NeurIPS)

作者： Shah, Harshay Tamuly, Kaustav Raghunathan, Aditi Jain, Prateek Netrapalli, Praneeth Microsoft Res Redmond WA 98052 USA Stanford Univ Stanford CA USA

ISBN: (纸本)9781713829546

Several works have proposed Simplicity Bias (SB)-the tendency of standard training procedures such as stochastic Gradient Descent (SGD) to find simple models-to justify why neural networks generalize well [1, 49, 74]. However, the precise notion of simplicity remains vague. Furthermore, previous settings [67, 24] that use SB to justify why neural networks generalize well do not simultaneously capture the non-robustness of neural networks-a widely observed phenomenon in practice [71, 36]. We attempt to reconcile SB and the superior standard generalization of neural networks with the non-robustness observed in practice by introducing piecewise-linear and image-based datasets, which (a) incorporate a precise notion of simplicity, (b) comprise multiple predictive features with varying levels of simplicity, and (c) capture the non-robustness of neural networks trained on real data. Through theoretical analysis and targeted experiments on these datasets, we make four observations: (i) SB of SGD and variants can be extreme: neural networks can exclusively rely on the simplest feature and remain invariant to all predictive complex features. (ii) The extreme aspect of SB could explain why seemingly benign distribution shifts and small adversarial perturbations significantly degrade model performance. (iii) Contrary to conventional wisdom, SB can also hurt generalization on the same data distribution, as SB persists even when the simplest feature has less predictive power than the more complex features. (iv) Common approaches to improve generalization and robustness-ensembles and adversarial training-can fail in mitigating SB and its pitfalls. Given the role of SB in training neural networks, we hope that the proposed datasets and methods serve as an effective testbed to evaluate novel algorithmic approaches aimed at avoiding the pitfalls of SB.

关键词： stochastic systems

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3rd International conference on Data Stream and Mining and processing, DSMP 2020

3rd International Conference on Data Stream and Mining and P...

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3rd International conference on Data Stream and Mining and processing, DSMP 2020

ISBN: (纸本)9783030616557

The proceedings contain 36 papers. The special focus in this conference is on Data Stream and Mining and processing. The topics include: Comparison analysis of clustering quality criteria using inductive methods of objective clustering;assessing the investment risk of virtual it company based on machine learning;methodological support for the management of maintaining financial flows of external tourism in global risky conditions;technology for determining the residual life of metal structures under conditions of combined loading according to acoustic emission measurements;expansion of the capabilities of chromatography-mass spectrometry due to the numerical decomposition of the signal with the mutual superposition of mass spectra;statistical methods for analyzing and processing data components when recognizing visual objects in the space of key point descriptors;sewer pipe defects classification based on deep convolutional network with information-extreme error-correction decision rules;critical modes of photography: Light sensitivity and resolution;multiclass image classification explanation with the complement perturbation images;image enhancement in automatic mode by recursive mean-separate contrast stretching;the principles of organizing the search for an object in an image, tracking an object and the selection of informative features based on the visual perception of a person;method of speech signal structuring and transforming for biometric personality identification;method of improving instance segmentation for very high resolution remote sensing imagery using deep learning;computer vision system for recognizing the coordinates location and ripeness of strawberries;novel nonparametric test for homogeneity and change-point detection in data stream;software for shelter’s fire safety and comfort levels evaluation.

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Automatic data augmentation for 3d medical image segmentation 23rd

Automatic data augmentation for 3d medical image segmentatio...

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23rd International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Xu, Ju Li, Mengzhang Zhu, Zhanxing Center for Data Science Peking University Beijing China Canon Medical Systems Beijing China School of Mathematical Sciences Peking University Beijing China

ISBN: (纸本)9783030597092

Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks. It could enrich diversity of training samples that is essential in medical image segmentation tasks because 1) the scale of medical image dataset is typically smaller, which may increase the risk of overfitting;2) the shape and modality of different objects such as organs or tumors are unique, thus requiring customized data augmentation policy. However, most data augmentation implementations are hand-crafted and suboptimal in medical image processing. To fully exploit the potential of data augmentation, we propose an efficient algorithm to automatically search for the optimal augmentation strategies. We formulate the coupled optimization w.r.t. network weights and augmentation parameters into a differentiable form by means of stochastic relaxation. This formulation allows us to apply alternative gradient-based methods to solve it, i.e. stochastic natural gradient method with adaptive step-size. To the best of our knowledge, it is the first time that differentiable automatic data augmentation is employed in medical image segmentation tasks. Our numerical experiments demonstrate that the proposed approach significantly outperforms existing build-in data augmentation of state-of-the-art models. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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Steps of Pre-processing for English to Mizo SMT System 1

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2nd International conference on Machine Learning, image processing, Network Security and Data Sciences, MIND 2020

作者： Devi, Chanambam Sveta Purkayastha, Bipul Syam Department of Computer Science Assam University Silchar788011 India

ISBN: (数字)9789811563188

ISBN: (纸本)9789811563171

The idea behind this present paper is to construct the pre-processing techniques on Bilingual Parallel Corpus. The parallel corpus is a common essential resource for the application of NLP. The parallel corpus is a huge number of parallel sentences or text of two different languages. We propose a new framework for the pre-processing steps of the Bilingual Corpus of SMT System. The purpose of this study the virtue of the pre-processing methods which are mainly used for training the translation. The pre-processing technique contributes to a main part of the statistical Machine Translation. In this paper, we survey the various steps of pre-processing methods used for the translation system and also how the complication of the translation method has succeeded in society so far. Experimental results of the pre-processing steps on Bible Domain (https://***/kjv). ‘English-Mizo’ parallel corpus (on bible domain) are discussed and all the results of pre-processing steps are mentioned. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Computer aided language translation

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