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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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THE QUICK AND HIGH QUALITY image INTERPOLATION FOR SINGLE image USING MULTI-FILTERING AND WEIGHTED MEAN 23

THE QUICK AND HIGH QUALITY IMAGE INTERPOLATION FOR SINGLE IM...

引用

23rd IEEE international conference on image processing (ICIP)

作者： Yamaguchi, Takuro Ikehara, Masaaki Keio Univ EEE Dept Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781467399616

image upsampling from one input image gathers considerable attention in the field of computer vision. The problem is ill-posed because the number of known low-resolution (LR) pixels is less than that of unknown high-resolution (HR) pixels. Therefore, quality of an up sampled image depends on prior assumptions. image interpolation methods are one of the image upsampling technologies and are faster than other image upsampling technologies such as Super-Resolution. However, these methods tend to cause jaggies and blurs in edge and texture regions. We use the idea of Multi-surface Fitting (MF) to solve these problems. MF uses plural local functions to estimate an HR pixel and it reduces blurs. Moreover, we utilize filtering instead of calculation of each local function in order to reduce a computational cost. And we introduce new weights to estimate edge directions. By these ideas, our method has both high quality and a low computational cost.

关键词： image interpolation filtering multi-surface fitting weighted mean

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

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

引用

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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Algorithms and Approaches for processing an Output Signal from Lab-on-a-Chip Devices in Bioanalysis 12

Algorithms and Approaches for Processing an Output Signal fr...

引用

12th international conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Lizanets, Danylo Walczak, Rafal Wroclaw Univ Technol Fac Microelect & Photon Div Microengn & Photovolta PL-50370 Wroclaw Poland

ISBN: (纸本)9786176079132

Algorithms and approaches used for implementing software for processing an image output signal from microfluidic devices are presented in this paper. Such microdevices (often called a lab-on-a-chip) are utilized in analysis of biological cells;their behaviors are investigated under different sets of conditions. We present here algorithms for cell detecting, processing and automatic recognition of shape and size of cells in lab-chips for bioanalysis.

关键词： image processing lab-on-a-chip computer vision bioanalysis

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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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HetroCV: Auto-tuning Framework and Runtime for image processing and computer vision Applications on Heterogeneous Platform 44

HetroCV: Auto-tuning Framework and Runtime for Image Process...

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44th Annual international conference on Parallel processing Workshops (ICPPW)

作者： Wang, Daihou Foran, David J. Qi, Xin Parashar, Manish Rutgers State Univ Rutgers Discovery Informat Inst Piscataway NJ 08854 USA Rutgers Canc Inst New Jersey New Brunswick NJ USA

ISBN: (纸本)9781467375894

With the wide adoption of high-performance processors and accelerators, large-scale computer vision applications have gained great performance improvement. However, it often requires extensive experiments and expertise to achieve optimal performance from manually-tuned programs, and the programs often need to be re-tuned when transplanted to a different platform, or using a different system configuration. To overcome this problem, in this paper we proposed HetroCV, a programmer-directed auto-tuning framework and runtime for computer vision applications on heterogeneous CPU-MIC platform. In HetroCV auto-tuning framework, computation units in the application pipeline are categorized in to one of three patterns: Map, Stencil and MapReduce, and program statistics are extracted from units' meta-information. Machine learning is adopted to train models for each pattern using the tuned parameters and program statistics from trial-run sets, so that when a new unit is presented, HetroCV autotuner can use the corresponding trained model to generate optimized tuning parameters. In HetroCV runtime, performance models for processor and co-processor are built to predict the prospective execution time of each computation unit in the application pipeline. We adopted the maximum-throughput mapping strategy, thus each unit would be mapped dynamically to the processor/co-processor queue, which would generate the minimum overall execution time. Experiments on two medical image processing applications running on heterogeneous platform composed of Intel Xeon CPU and Intel Phi co-processor showed advanced performance over naive OpenMP tuning and Genetic Algorithm (GA) based heuristic tuning.

关键词： Online auto-tuning runtime heterogeneous platform Intel MIC architecture image processing

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A Novel Mismatching Elimination Algorithm Based on Distribution of Features 20

A Novel Mismatching Elimination Algorithm Based on Distribut...

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20th IEEE international conference on computer Supported Cooperative Work in Design (CSCWD)

作者： Zhu, Qidan Liu, Chuanjia Cai, Chengtao Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

ISBN: (纸本)9781509019151

Catadioptric panoramic image's application to computer visual field gains its popularity in recent years. However, due to its complicated imaging relationship, most existing mismatching elimination algorithms cannot directly operate on the unprocessed panoramic images. Those above algorithms usually need to unwarp the panoramic images before further processing. In order to solve the above problems, based on the distribution characteristics of features in the panoramic image, a novel mismatching elimination algorithm is proposed in this paper. Under different scene conditions, the novel algorithm can eliminate the mismatching features and improve the matching accuracy effectively. Experiments on the image databases confirm its effectiveness.

关键词： catadioptric panoramic image computer vision mismatching elimination image unwarping

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Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection 1

引用

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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Enhancing stereo image quality based on adaptive hole-filling of depth image for kinect camera

Enhancing stereo image quality based on adaptive hole-fillin...

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

作者： Cho, Ji-Min Yun, Young-Ji Nam, Seung-Woo Chien, Sung-Il School of Electronics Engineering Kyungpook National University Daegu Korea Republic of SW Content Research Lab Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)1601324421

Kinect camera produces depth images as well as color images. Yet undefined depth regions called holes are often observed in the depth image and are quite harmful in generating a second view image to construct a stereo pair for 3D viewing. An adaptive hole-filling method that can fill in even very large holes more naturally is proposed so that depth values for holes of various sizes are suitably estimated depending on the location of holes in the background or foreground. Removal of dot noise inside a hole is also introduced to further enhance the quality of the newly generated stereo image. © CSREA Press.

关键词： Filling

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Content Based image Retrieval with Hadoop 2nd

引用

2nd international conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Gaber, Heba Marey, Mohammed Amin, Safaa E. Tolba, Mohamed F. Ain Shams Univ Comp & Informat Sci Cairo Egypt

ISBN: (纸本)9783319483085;9783319483078

Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It allows processing of extremely large video files or image files on data nodes. This can be used for implementing Content Based image Retrieval (CBIR) algorithms on Hadoop to compare and match query images to the previously stored terabytes of an image descriptors databases. This work presents the implementation for one of the well-known CBIR algorithms called Scale Invariant Feature Transformation (SIFT) for image features extraction and matching using Hadoop platform. It gives focus on utilizing the parallelization capabilities of Hadoop MapReduce to enhance the CBIR performance and decrease data input\output operations through leveraging Partitioners and Combiners. Additionally, image processing and computer vision tools such as Hadoop image processing (HIPI) and Open computer vision (OpenCV) are integration is shown.

关键词： Hadoop HIPI OpenCV Big data Crowd sourcing MapReduce Combiners Partitioners

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