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Recent Advancements in image-Based Prediction Models for Diagnosis of Plant Diseases 3rd

Recent Advancements in Image-Based Prediction Models for Dia...

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3rd international conference on computer vision and image processing (cvip)

作者： Verma, Shradha Chug, Anuradha Singh, Amit Prakash GGSIP Univ USICT Delhi India

ISBN: (纸本)9789813290884;9789813290877

India plays a significant role in the world as a major contributor to the overall food industry. Farming being a major occupation, crop protection from plant diseases has become a serious concern. The occurrence of plant diseases is hugely dependent on environmental factors which are uncontrollable. Ongoing agricultural research and the advanced computational technologies can be coalesced to determine an effective solution, which can improve the yield and result in better harvests. The objective is to minimize the economic and production losses. Researchers have developed several modeling techniques, viz. Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Deep Learning for prediction and preferably early detection of diseases in plants. Mostly, images of the diseased plant are given as input to these prediction models. Early detection leads to minimizing the pesticide usage, hence, resulting in lowering the expense along with the ecological impact. Also, it is essential that the prediction techniques are nondestructive in nature. This paper delves into the Machine Learning based plant disease prediction models, along with a brief study of diverse imaging techniques, viz., RGB, multi- and hyperspectral, thermal, fluorescence spectroscopy, etc., catering to different features/parameters of a plant, useful in the prediction of diseases. Around 35 research papers from reputed peer-reviewed journals were studied and analyzed for this systematic review. It highlights the latest trends in plant disease diagnosis as well as identifies the future scope with the application of modern era technologies.

关键词： Imaging Machine Learning Plant diseases Neural Networks Deep Learning Agriculture

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A novel graph theoretic image segmentation technique 4th

A novel graph theoretic image segmentation technique

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4th international conference on computer vision and image processing, cvip 2019

作者： Chandel, Sushmita Bhatnagar, Gaurav Indian Institute of Technology Jodhpur Karwar India

ISBN: (纸本)9789811540141

In this paper, a novel graph theoretic image segmentation technique is proposed, which utilizes forest concept for clustering. The core idea is to obtain a forest from the image followed by construction of average value super pixels. Thereafter, a merging criterion is proposed to merge these super pixels into two big classes thereby binarizing and thresholding the image separating background from foreground. Extensive experimentation and comparative analysis are finally performed on a diverse set of images to validate the technique and have noted the significant improvements. © Springer Nature Singapore Pte Ltd 2020.

关键词： image segmentation

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Face Verification Using Single Sample in Adolescence 5th

Face Verification Using Single Sample in Adolescence

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5th international conference on computer vision and image processing, cvip 2020

作者： Sumithra, R. Guru, D.S. Aradhya, V. N. Manjunath Raghavendra, Anitha Department of Studies in Computer Science University of Mysore Manasagangotri Mysuru Karnataka570 006 India Department of Computer Application JSS Science and Technology University Mysuru Karnataka India Department of E&CE Maharaja Institute of Technology Mysuru Karnataka India

ISBN: (纸本)9789811610912

Recognition of facial aging using face images has enormous applications in forensic and security control. In this study, an attempted to verify the face images of adolescence and adults by using a single reference sample have been made. To study this problem, we have designed our model by considering with-patch based and without-patch based face images. Both local and pre-trained deep features have been extracted during feature extraction. Further, the well-known subspace techniques such as Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) have been adopted for dimensionality reduction. To measure the model’s goodness, we have created our dataset consisting of two images of 64 persons, each with an age gap of 10 years between adolescents (15 years old) and adults (25 years old). The comparative analysis between with-patch and without-patch based images, and PCA and FLD have been studied effectively. The pre-trained networks give the highest matching characteristics at 96% using top projection vectors. The experimental results reveal that the deep learning model outperforms the state-of-the-art method for face verification. Therefore, the obtained results are promising and encouraging for face biometric applications. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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image-Based Facial Expression Recognition Using Local Neighborhood Difference Binary Pattern 3rd

Image-Based Facial Expression Recognition Using Local Neighb...

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3rd international conference on computer vision and image processing (cvip)

作者： Saurav, Sumeet Singh, Sanjay Yadav, Madhulika Saini, Ravi Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vanasthali Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Automatic facial expression recognition (FER) has gained enormous interest among the computer vision researchers in recent years because of its potential deployment in many industrial, consumer, automobile, and societal applications. There are a number of techniques available in the literature for FER;among them, many appearance-based methods such as local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP) have been shown to be very efficient and accurate. In this paper, we propose a new descriptor called local neighborhood difference binary pattern (LNDBP). This new descriptor is motivated by the recent success of local neighborhood difference pattern (LNDP) which has been proven to be very effective in image retrieval. The basic characteristic of LNDP as compared with the traditional LBP is that it generates binary patterns based on a mutual relationship of all neighboring pixels. Therefore, in order to use the benefit of both LNDP and LBP, we have proposed LNDBP descriptor. Moreover, since the extracted LNDBP features are of higher dimension, therefore a dimensionality reduction technique has been used to reduce the dimension of the LNDBP features. The reduced features are then classified using the kernel extreme learning machine (K-ELM) classifier. In order to, validate the performance of the proposed method, experiments have been conducted on two different FER datasets. The performance has been observed using well-known evaluation measures, such as accuracy, precision, recall, and F1-score. The proposed method has been compared with some of the state-of-the-art works available in the literature and found to be very effective and accurate.

关键词： Facial expression recognition (FER) Local neighborhood difference pattern (LNDP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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Human Gait Abnormality Detection Using Low Cost Sensor Technology 5th

Human Gait Abnormality Detection Using Low Cost Sensor Techn...

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5th international conference on computer vision and image processing, cvip 2020

作者： Jain, Shaili Nandy, Anup Machine Intelligence and Bio-Motion Laboratory Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha769008 India

ISBN: (纸本)9789811610912

Detection of gait abnormality is becoming a growing concern in different neurological and musculoskeletal patients group including geriatric population. This paper addresses a method of detecting abnormal gait pattern using deep learning algorithms on depth images. A low cost Microsoft Kinect v2 sensor is used for capturing the depth images of different subject’s gait sequences. A histogram-based technique is applied on depth images to identify the range of depth values for the subject. This method generates segmented depth images and subsequently median filter is used on them to reduce unwanted information. Multiple 2D convolutional neural network (CNN) models are trained on segmented images for pathological gait detection. But these CNN models are only restricted to spatial features. Therefore, we consider 3D-CNN model to include both spatial and temporal features by stacking all the images from a single gait cycle. A statistical technique based on autocorrelation is applied on entire gait sequences for finding the gait period. We achieve a significant detection accuracy of 95% using 3D-CNN model. Performance evaluation of the proposed model is evaluated through standard statistical metrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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A survey on the current state of the art on deep learning 3D reconstruction 17

A survey on the current state of the art on deep learning 3D...

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17th IEEE international conference on Intelligent computer Communication and processing, ICCP 2021

作者： Maxim, Bogdan Nedevschi, Sergiu Technical University of Cluj-Napoca Computer Science Departament Cluj-Napoca Romania

ISBN: (纸本)9781665409766

Efficient 3D reconstruction from different kind of inputs is a long standing effort of computer vision. Recent advancements in the field of machine learning, specifically deep learning, have started an interest in studying how well these techniques apply to the 3D reconstruction problem. Current efforts employ two main research directions: techniques applied to a single object, trying to reconstruct the surface as closely as possible from different kind of inputs and techniques applied to scenes made from multiple objects, which deal with topological representations, color, illumination and resource consumption. With a plethora of applications in computer graphics and computer vision, the results given by the deep learning techniques start to gain a serious position in the field of 3D reconstruction, yet no survey exist on the recent advancements on these new techniques. This survey summarizes the recent trend and applications of the deep-learning 3D reconstruction methods. We focus on learnable reconstruction methods from inputs like single image, multi-image and point cloud, using different representations, such as voxels, meshes and signed distance fields. Starting by presenting the datasets used, we follow by showing the main deep learning methods using different representations, presenting advantages and disadvantages. The second half of this survey focuses on scene reconstructions and open problems. Finally we conclude with a discussion of the importance of the 3D reconstruction and its possible applications in different fields such as automotive and mixed reality. © 2021 IEEE.

关键词： Surveys

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Denoising images with varying noises using autoencoders 4th

Denoising images with varying noises using autoencoders

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4th international conference on computer vision and image processing, cvip 2019

作者： Agarwal, Snigdha Agarwal, Ayushi Deshmukh, Maroti Department of Computer Science and Engineering National Institute of Technology SrinagarUttarakhand India

ISBN: (纸本)9789811540172

image processing techniques are readily used in the field of sciences and computer vision for the enhancement of images and extraction of useful information from them. A key step used in image processing involves the removal of different kinds of noises from the images. Noises can arise in an image during the process of storing, transmitting or acquiring the images. A model qualifies as a satisfactory de-noising model if it satisfies image preservation along with noise removal. There can be various kind of noises in an image such as Gaussian, salt and pepper, Speckle etc. A model which can denoise a different kind of noises is considered to be superior to others. In this paper, we have designed a model using autoencoder which can remove several kinds of noises from images. We have performed a comparative study between the accuracy of each kind using PSNR, SSIM and RMSE values. An increase in the PSNR and SSIM values was seen from the original and noisy image to the original and reconstructed image while a decrease was seen in the value of RMSE. © Springer Nature Singapore Pte Ltd 2020.

关键词： Salt and pepper noise

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Research on super-resolution reconstruction of single-frame image of infrared focal plane polarization imaging 8

Research on super-resolution reconstruction of single-frame ...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Yuan, Hongwu Wang, Yiqing Xu, Guoming Wang, Feng School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China Key Laboratory of Polarization Imaging Detection Technology in Anhui Province Hefei China Institute of Intelligent Information Anhui Xinhua University Hefei China School of Internet Anhui University Hefei China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Since the low resolution of infrared focal plane arrays may degrade the performance of polarization imaging significantly, it is necessary to study the super-resolution reconstruction method for superior image resolution and contrast. Four typical single-frame image reconstruction methods are studied in this paper, and the comparison of reconstruction result of these methods is conducted by using subjective and objective evaluation. The experiments show that the reconstruction method based on generative adversarial network performs poorly in the evaluation indexes such as peak signal-to-noise ratio and structural similarity, but its reconstructed image has good visual effects, rich texture and details, and has a strong ability to suppress background noise, and using the reconstructed images for polarization information parsing can significantly improve the accuracy of polarization information parsing and the effect of polarization image fusion. © 2022 SPIE

关键词： image fusion

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A new hybrid architecture for real-time detection of emergency vehicles 4th

A new hybrid architecture for real-time detection of emergen...

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4th international conference on computer vision and image processing, cvip 2019

作者： Jonnadula, Eshwar Prithvi Khilar, Pabitra Mohan Department of Computer Science and Engineering National Institute of Technology Rourkela India

ISBN: (纸本)9789811540172

VANET is a vital part of wireless networking. Vehicular movement is expanding indefinitely everywhere and is causing terrible problems to daily life. Almost all of the traffic lights now feature a fixed green light sequence and so green light sequence is determined without taking the existence of emergency vehicles into consideration. Consequently emergency vehicles such as ambulances, fire engines, police vehicles etc. are struck in traffic which might cause loss of valuable life and property. In this paper we present a new hybrid architecture for detection of emergency vehicles in real time. This hybrid architecture is based on the mixed features of image processing and machine learning. We also show the percentage decrease in the search space for the processing which results in faster detection of emergency vehicles. © Springer Nature Singapore Pte Ltd 2020.

关键词： image processing

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Colour sensitive image segmentation using quaternion Algebra 4th

Colour sensitive image segmentation using quaternion Algebra

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4th international conference on computer vision and image processing, cvip 2019

作者： Maity, Sandip Kumar Biswas, Prabir ARC Document Solution Kolkata India Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789811540141

In colour image segmentation, a few vector filters are useful, and they are not well applicable. The segmentation tools which are still available, not also colour sensitive. In this paper, we present an approach for color sensitive segmentation method by using the color sensitivity property of hyper complex or quaternion algebra. The filter is based on quaternion convolution and impulse response is determined using modified gray centered RGB color cube. This method demands that every color can be segmented by choosing suitable impulse response. © Springer Nature Singapore Pte Ltd 2020.

关键词： Color

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