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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

共 1407 条记录，以下是291-300 订阅

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An Ensemble Approach for Moving Vehicle Detection and Tracking by Using Ni vision Module 7th

An Ensemble Approach for Moving Vehicle Detection and Tracki...

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7th international conference on computer vision and image processing, cvip 2022

作者： Singh, Pankaj Pratap Ramchiary, Preeti Bora, Jahirul Islam Bhuyan, Rakesh Prasad, Shitala Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Assam Kokrajhar India Institute for Infocomm Research A*Star Singapore Singapore

ISBN: (纸本)9783031314162

In the recent years, surveillance systems and video monitoring have been largely used for the management of traffic. Acquired images and video clips from the road traffic can be utilized in the Lab VIEW program environment. LabVIEW vision Assistant is focusing on to discover the moving vehicles. This approach finds a formation of resemblance in the frames for vehicle and non-vehicle objects, while the vehicles tracking progress through image sequences. For improving the adaptive background mixture model, there is indeed of background subtraction method. In addition, it constructs the system more precisely with rapid learning also. Therefore, its performance shows the adaptability on the occurrence of any real time videos. This evolve system robustly detect the vehicles on resolving the background objects which help to track the vehicles effectively. The various types of attributes related to moving vehicles are extracted which are used in feature extraction techniques for tracking the vehicles. The extracted features utilize in the module of LabVIEW environment and vision Assistant module works mainly in the detection of moving vehicles objects. This proposed work can help in reducing the cost of traffic monitoring systems and real automation of traffic observation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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image compression and denoising algorithm based on multi-resolution discrete cosine transform

Image compression and denoising algorithm based on multi-res...

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2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

作者： Zhao, Yanjun Belkasim, Saeid Computer Science Department Troy University TroyAL United States Computer Science Department Georgia State University AtlantaGA United States

ISBN: (纸本)1601324421

- Discrete cosine transform (DCT) and wavelet transform coding system are the most popular image compression methods. Although DCT has outstanding energy compaction properties, blocking artifacts impact its performance. Wavelet avoids blocking artifacts;it is also the most popular approach to doing image compression and denoising simultaneously. However wavelet has higher computational complexity. Exploring an image in different resolutions reveals its dominant information in comparison to redundant one. We propose a novel multi-resolution DCT;based on our multi-resolution DCT, we propose a novel algorithm to do image compression and denoising simultaneously. Our algorithm achieves multi-resolution analysis, avoids blocking artifacts, has excellent energy compaction property and is ideal for parallel computing. Compared to wavelet, our algorithm has good computation accuracy and efficiency. © CSREA Press.

关键词： Discrete cosine transforms

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A novel text localization scheme for camera captured document images 2nd

A novel text localization scheme for camera captured documen...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Khan, Tauseef Mollah, Ayatullah Faruk Department of Computer Science and Engineering Aliah University Kolkata700156 India

ISBN: (纸本)9789811078941

In this paper, a hybrid model for detecting text regions from scene images as well as document image is presented. At first, background is suppressed to isolate foreground regions. Then, morphological operations are applied on isolated foreground regions to ensure appropriate region boundary of such objects. Statistical features are extracted from these objects to classify them as text or non-text using a multi-layer perceptron. Classified text components are localized, and non-text ones are ignored. Experimenting on a data set of 227 camera captured images, it is found that the object isolation accuracy is 0.8638 and text non-text classification accuracy is 0.9648. It may be stated that for images with near homogenous background, the present method yields reasonably satisfactory accuracy for practical applications. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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vision Transformer for Plant Disease Detection: PlantViT 6th

Vision Transformer for Plant Disease Detection: PlantViT

引用

6th international conference on computer vision and image processing, cvip 2021

作者： Thakur, Poornima Singh Khanna, Pritee Sheorey, Tanuja Ojha, Aparajita Indian Institute of Information Technology Design and Manufacturing Jabalpur482001 India

ISBN: (纸本)9783031113451

With the COVID-19 pandemic outbreak, most countries have limited their grain exports, which has resulted in acute food shortages and price escalation in many countries. An increase in agriculture production is important to control price escalation and reduce the number of people suffering from acute hunger. But crop loss due to pests and plant diseases has also been rising worldwide, inspite of various smart agriculture solutions to control the damage. Out of several approaches, computer vision-based food security systems have shown promising performance, and some pilot projects have also been successfully implemented to issue advisories to farmers based on image-based farm condition monitoring. Several image processing, machine learning, and deep learning techniques have been proposed by researchers for automatic disease detection and identification. Although recent deep learning solutions are quite promising, most of them are either inspired by ILSVRC architectures with high memory and computational requirements, or light convolutional neural network (CNN) based models that have a limited degree of generalization. Thus, building a lightweight and compact CNN based model is a challenging task. In this paper, a transformer-based automatic disease detection model "PlantViT" has been proposed, which is a hybrid model of a CNN and a vision Transformer. The aim is to identify plant diseases from images of leaves by developing a vision Transformer-based deep learning technique. The model takes the capabilities of CNNs and the vision Transformer. The vision Transformer is based on a multi-head attention module. The experiment has been evaluated on two large-scale open-source plant disease detection datasets: PlantVillage and Embrapa. Experimental results show that the proposed model can achieve 98.61% and 87.87% accuracy on the PlantVillage and Embrapa datasets, respectively. The PlantViT can obtain significant improvement over the current state-of-the-art methods in plan

关键词： Large dataset

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Non-uniform deblurring from blurry/noisy image Pairs 4th

Non-uniform deblurring from blurry/noisy image Pairs

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

作者： Deepa, P.L. Jiji, C.V. Mar Baselios College of Engineering and Technology Thiruvananthapuram India College of Engineering Thiruvananthapuram India

ISBN: (纸本)9789811540141

In this paper, we address the problem of recovering a sharp image from its non-uniformly blurred version making use of a known but noisy version of the same scene. The recovery process includes three main steps - motion estimation, segmentation and uniform deblurring. The noisy image is first denoised and then used as a reference image for estimating the motion occurred in the non-uniformly blurred image. From the obtained motion vectors, the blurred image is segmented into image blocks encountered with uniform motion. To deblur these uniformly blurred segments, we use a two step process where we first generate an unnatural representation under an l0 minimization frame work followed by a hyper-Laplacian prior based non-blind deconvolution. The resulting deblurred segments are finally concatenated to form the output image. The proposed method gives better results in comparison with other state of the art methods. © Springer Nature Singapore Pte Ltd 2020.

关键词： Motion estimation

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image processing Techniques for Surface Characterization of Nanostructures

Image Processing Techniques for Surface Characterization of ...

引用

IEEE international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： John, Jisha Wilscy, M. Univ Kerala Dept Comp Sci Thiruvananthapuram 695581 Kerala India

ISBN: (纸本)9781509012770

The characterization of nanoscale images finds numerous applications in computer vision and image processing technologies. It is an emerging area of research and only few papers exist in literature on techniques for analyzing properties of nanostructures. The characterization of nanostructures in terms of surface morphology, particle size, porosity measurement etc helps in analyzing unique features and properties of the nanomaterials which makes them useful in many applications. This paper reviews the various algorithms and their advantages and drawbacks in analyzing the nanostructures.

关键词： nanostructures uniformity feature similarity nanoscale image

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Analysis of Loss Functions for image Reconstruction Using Convolutional Autoencoder 6th

Analysis of Loss Functions for Image Reconstruction Using Co...

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6th international conference on computer vision and image processing, cvip 2021

作者： Khare, Nishant Thakur, Poornima Singh Khanna, Pritee Ojha, Aparajita Computer Science and Engineering PDPM IIITDM Jabalpur India

ISBN: (纸本)9783031113482

In recent years, several loss functions have been proposed for the image reconstruction task of convolutional autoencoders (CAEs). In this paper, a performance analysis of a CAE with respect to different loss functions is presented. Quality of reconstruction is analyzed using the mean Square error (MSE), binary cross-entropy (BCE), Sobel, Laplacian, and Focal binary loss functions. To evaluate the performance of different loss functions, a vanilla autoencoder is trained on eight datasets having diversity in terms of application domains, image dimension, color space, and the number of images in the dataset. MSE, peak signal to noise ratio (PSNR), and structural similarity index (SSIM) metrics have been used as the performance measures on all eight datasets. The assessment shows that the MSE loss function outperforms two datasets with a small image dimension and a large number of images. At the same time, BCE excels on six datasets with high image dimensions and a small number of training samples in datasets compared with the Sobel and Laplacian loss functions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mean square error

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Robust image sharing scheme using one dimensional chaotic logistic map 1

引用

2nd international conference on computer vision and image processing, cvip 2017

作者： Saini, Vikrant Kumar Rajput, Amitesh Singh Raman, Balasubramanian Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

In today’s world, image sharing has been a vital area of digital industry. images are transmitted over an insecure transmission channel and are vulnerable to possible attacks. In this paper, we propose a novel technique for secure transmission of images. The method consist of two phases. In the first phase, user has to input a key of 256 bits. Arithmetic mean of the image and chaotic logistic map is used to generate key image. In the second phase, two layered Shamir’s secret sharing scheme(SSS) architecture is used with cascading XoR operation. Receiver can get the secret image back by applying reverse of the proposed method and secret key transmitted through the same insecure channel. In contrast to the previous schemes, our proposed scheme provides better utilization of bandwidth and robust against different possible attacks. © Springer Nature Singapore Pte Ltd. 2018.

关键词： One dimensional

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On the Performance of Convolutional Neural Networks Under High and Low Frequency Information 1

引用

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

作者： Yedla, Roshan Reddy Dubey, Shiv Ram Computer Vision Group Indian Institute of Information Technology Sri City Chittoor Andhra Pradesh India

ISBN: (数字)9789811611032

ISBN: (纸本)9789811611025

Convolutional neural networks (CNNs) have shown very promising performance in recent years for different problems, including object recognition, face recognition, medical image analysis, etc. However, generally the trained CNN models are tested over the test set which is very similar to the trained set. The generalizability and robustness of the CNN models are very important aspects to make it to work for the unseen data. In this letter, we study the performance of CNN models over the high and low frequency information of the images. We observe that the trained CNN fails to generalize over the high and low frequency images. In order to make the CNN robust against high and low frequency images, we propose the stochastic filtering based data augmentation during training. A satisfactory performance improvement has been observed in terms of the high and low frequency generalization and robustness with the proposed stochastic filtering based data augmentation approach. The experimentations are performed using ResNet50 model over the CIFAR-10 dataset and ResNet101 model over Tiny-imageNet dataset. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image classification

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Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection 1

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6th international conference on computer vision and image processing, cvip 2021

作者： Singha, Anu Saha, Priya Bhowmik, Mrinal Kanti Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Agartala India

ISBN: (数字)9783031113468

ISBN: (纸本)9783031113451

In technological advancement, there are several techniques have discovered for exact identification of hydrocarbons which is being used by oil industries to detect the oil reservoirs. In this study, we have investigated and proposed system of detection and prediction of hydrocarbons under earth subsurface through microscopic rock image modality. This system presents a robust watershed segmentation approach for determining porosity where convolutional neural networks are used for classification of sandstone and carbonate rock samples. The system is tested on microscopic images of sandstone and carbonate rock samples, and detection observed in rocks is based upon estimation of total porosity. Experimental comparison of proposed system shows outperform over state-of-the-art methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sandstone

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