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Research on Walking Speed of Sugarcane Harvester Based on Machine vision 21

Research on Walking Speed of Sugarcane Harvester Based on Ma...

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6th International conference on Intelligent Information processing, ICIIP 2021

作者： Shen, Zhonghua Chen, Wanwei Dong, Zhikang Guilin University of Technology China

ISBN: (纸本)9781450390637

During the operation of the sugarcane harvester, due to the uneven planting density of sugarcane, reasonable control of the walking speed of the sugarcane harvester has an important impact on the quality of sugarcane cutting. If the traveling speed of the sugarcane harvester can be adjusted according to the complex working conditions of the sugarcane field, then the sugarcane harvester can effectively reduce the broken rate, loss rate, impurity rate and reduction of sugarcane ratoons while ensuring a certain harvest rate. Blockage of sugarcane logistics channels. To this end, this paper proposes a scheme based on machine vision to assist the sugarcane harvester in harvesting operations. the machine vision is used to judge the sugarcane density in the working area in front of the sugarcane harvester, so as to reasonably select different grades of travel speed. First, the histogram is used to count the pixels of the original image, and the characteristics of the original image are analyzed. In order to make the image more ideal, the original image is equalized. the processed image is then based on the histogram technique to compare the sugar cane in the image. Perform segmentation;then use mathematical morphology to eliminate the noise after segmentation;finally, count the density of sugarcane inside the picture. through the above design, it can be seen that the core part of the design of the small spouted bed dryer is the design of the blower and heater temperature. According to the calculation of the required wind speed and wind temperature for drying, the rate and output of drying materials can be determined. © 2021 ACM.

关键词： computer vision

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Tesseract OCR for Hindi Typewritten Documents 6

Tesseract OCR for Hindi Typewritten Documents

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6th International conference on image Information processing, ICIIP 2021

作者： Kaur, Jaspreet Goyal, Vishal Kumar, Manish Punjabi University Department of Computer Scieance Patiala India Baba Farid College Deon Department of Computer Science Bathinda India

ISBN: (纸本)9781665433617

the development of Optical Character Recognition (OCR) in indian text is an active area of research today. the presence of a large number of characters in a set of alphabets, their complex combinations is a major challenge for the OCR designer. this OCR is giving very good results even for Hindi documents that are machine printed. But, it is not giving good results for typewriter typed Hindi documents. Even there is no OCR that is available to date which is capable of recognizing text from typewriter typed Hindi documents. this paper introduces an automated training data framework, provided only with labelled text images, thus removing the need for manually generated text. In contrast to the training images and images text as ground-Truth, this approach is based on the random, rule-based generation of meaningless text in an image file and their ground-Truth text file. In this paper we describe a dataset of typewriter type Hindi documents and Ground truth (GT) typewritten Hindi text images paired with their transcription. Typewriter typed documents can be incorporated into the repository of Plagiarism detection tools because text cannot be recognized by any OCR. thus, the functionality of the existing OCR needs to be extended for recognizing typewriter typed documents. © 2021 IEEE.

关键词： Typewriters

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Multi-Scale and Attention Residual Network for Single image Dehazing 6

Multi-Scale and Attention Residual Network for Single Image ...

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6th IEEE International conference on Intelligent Computing and Signal processing, ICSP 2021

作者： Zhou, Jingyuan Leong, Chak Tou Li, Congduan Sun Yat-Sen University School of Electronics and Communication Engineering Shenzhen Guangdong China Wuhan University School of Computer Science Wuhan Hubei China

ISBN: (纸本)9780738143705

image dehazing has always been a thorny ill-posed problem in low-level vision. In this paper, we propose a new model which combines feature pyramid network and attention based resblock, which is called the pyramid attention residual feature extraction network (PARFE-Net). the architecture of PARFE-Net can be summarized as follows:(I)the feature pyramid network module is used to extract features from the input image. Multi-scale can extract different features on different scales. the extracted features have great semantic differences in different depths.(II) Innovatively integrate channel-wise attention and pixel-wise attention and apply them to residual block. Attention mechanism can make the weight of the model higher in important places, which significantly affects the quality of the output image. (III) Combine the resblocks in II to get a large residual network, which is called iterative attention resblock. the features of different dimensions extracted from I are concatenated according to the channel dimension, which is used as the input of iterative attention resblock. the experiment is based on NYU2 and RESIDE. Compared with the commonly used dehazing model, the evaluation index PSNR and SSIM of our approach have achieved considerable or even better results, with better robustness. © 2021 IEEE.

关键词： Pixels

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GAN based indian sign language synthesis 21

GAN based indian sign language synthesis

引用

12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Krishna, Shyam Ukey, Janmesh Babu, J. Dinesh International Institute of Information Technology Bengaluru India Reliance Jio AICoE Hyderabad India

ISBN: (纸本)9781450391276

Controllable visual reproduction of sign language, termed Sign Language Synthesis (SLS), is a major and challenging task in sign language processing. Traditional methods have used computer animation to perform this task, but these have faced several limitations. Animation usually requires expensive equipment to perform motion capture and intensive manual oversight to ensure accuracy. Recently, Generative Adversarial Networks (GANs) have had very promising results in pose and motion transfer. this has been explored as a SLS method in Stoll, et al. [22] and related work. However, this work has required datasets with manual annotation, both in the form of requiring a corpus of manually selected "good"hand poses, as well a large corpus of videos of continuous signing which are annotated for the sequence of signs appearing in them. Most sign languages, however, face a dearth in data, especially annotated data, and this is the case for indian Sign Language (ISL). In this paper, we present a method for overcoming this issue in the first GAN based SLS model created specifically for ISL. We use a combination of separate generators for the hand and body to overcome the problem of requiring hand-picked "good"hand images from training videos. We further refine the output with another network to remove the artefacts appearing due to combining separate GAN outputs. We also experiment with creating continuous sign language output without requiring an annotated corpus, by stitching together individual signs obtained from a publicly available video lexicon of ISL. We show our model performs competitively in these tasks in both quantitative measures as well as in human perception tests. © 2021 ACM.

关键词： Generative adversarial networks

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Multi-scale Fusion based Multi-stage Small Object Detection in Aerial images ∗ 22

Multi-scale Fusion based Multi-stage Small Object Detection ...

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Proceedings of the 2022 6th International conference on Electronic Information Technology and computer Engineering

作者： Mingrui Yang Yu Wang Xindong Zhang State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University China

ISBN: (纸本)9781450397148

In aerial images, the objects are mostly small. the number of objects is large and the scale is diverse, so it is difficult to extract the features of multiple scale objects at the same time. the location distribution of object in aerial images is usually dense, making it difficult to locate. these factors bring great challenges to aerial image object feature extraction, and then reduce the performance of detection. therefore, a multi-scale fusion based multi-stage small object detection method (MSMSD) for aerial images is proposed in this paper. MSMSD adopts EfficientNet as feature extraction backbone and add deformable convolution blocks to achieve better detection performance on objects with irregular shapes. NAS-FPN is leveraged to fuse multi-scale features effectively. A cascade detection mechanism is also designed to reduce noisy detection and mis-detection in this task. In experiment section, the proposed MSMSD outperforms five benchmark object detection algorithms on two aerial image datasets. Experimental results demonstrate that MSMSD can handle the small object detection task in aerial images efficiently.

关键词： computer vision Multi-scale image processing Small object detection Deep learning

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Fusion of image quality assessment and transfer learning for COVID19 detection using CT scan image 21

Fusion of image quality assessment and transfer learning for...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Kiruthika, S. Masilamani, V. Joshi, Pratik Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamilnadu Chennai India

ISBN: (纸本)9781450391276

One of the main challenges in controlling the spread of COVID19 pandemic is to diagnose infection early. the most reliable method RT - PCR takes several hours to give results. Although the Anti-Body (Serological) test gives the results in a few hours, it is not accurate, and hence it is not reliable. Moreover, they are invasive. Another issue with these methods is that the number of labs performing these tests are very limited. It will be beneficial if the already existing clinical infrastructure is used for diagnosing COVID19 accurately in real time. Recently chest CT images are used by researchers to diagnose the COVID19 with impressive accuracy. the state of the art method for detecting COVID19 using CT chest images involves Deep Learning. Deep Learning is expected to provide accurate and reliable results only when the model is trained on a large data set. Due to non-availability of a large data set the existing models have been trained on a smaller size data set. therefore it would be better to design a model to give good accuracy with reliability. To achieve accuracy along with reliability we proposed a COVID19 detection model with the combination of deep learning model and the traditional machine learning model. the novelty of the proposed model is the fusion of image quality and deep learning. the proposed method outperformed the state of the art method in terms of accuracy, recall and F1 score (more than 99 % in almost all the metrics) on a benchmark data set. the efficacy of the selected features and also explainability of the method are demonstrated through various tests. © 2021 ACM.

关键词： Diagnosis

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Comparative Analysis of CNN Pre-trained Model for Stock Market Trend Prediction 6th

Comparative Analysis of CNN Pre-trained Model for Stock Ma...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Chauhan, Jitendra Kumar Ahmed, Tanveer Sinha, Amit Computer Science Department Bennett University Plot Nos 8 11 TechZone 2 Uttarpradesh Greater Noida201310 India Department of Information Technology ABES Engineering College Ghaziabad India

ISBN: (纸本)9783031530814

this research offers an in-depth comparative analysis of various pre-trained Convolutional Neural Network (CNN) models such as VGG16, ResNet50, InceptionV3, MobileNetV2, and Xception to predict stock market trends. Our approach involves the conversion of time-series financial data into 2D image-like structures through the application of two distinct techniques: the Gramian Angular Field (GAF) and the Markov Transition Field (MTF). By applying this transformation, we leverage the power of CNNs. We utilize the ideas of transfer learning and try to evaluate the performance of each model using several measures including predictive accuracy, precision, recall, F1-score, and computational efficiency. the analysis highlights the unique advantages and limitations of each model, thereby offering valuable insights into their suitability for stock market prediction tasks. this study is a significant contribution to the current body of literature on financial time series forecasting, providing a novel perspective on using pre-trained CNN models in the indian Financial Sector. It carries important implications for future work and practitioners in the finance and investment sectors, offering a tool for more e-market predictions. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Post-harvest Handling of Mangoes: An Integrated Solution Using Machine Learning Approach 6th

Post-harvest Handling of Mangoes: An Integrated Solution Usi...

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6th International conference on computer vision and image processing, CVIP 2021

作者： Guru, D.S. Raghavendra, Anitha Rao, Mahesh K. Department of Studies in Computer Science University of Mysore Manasagangotri Mysore570 006 India Infosys Limited Infosys Rd Hebbal Industrial Estate Hebbal Mysuru570027 India Maharaja Research Foundation Maharaja Institute of Technology Mysore Campus Belawadi S R Patna Mandya571 477 India

ISBN: (纸本)9783031113482

In this paper, different steps involved in harvesting and postharvest handling of mangoes are detailed and the possibilities of exploring Artificial Intelligence based solutions for each step are highlighted. A schematic diagram of overall post-harvesting is given. Various suitable methods of classical machine learning such as feature extraction methods, feature reduction methods, and classifiers are presented. Problems of classifying mangoes into mature, non- mature and matured class further into ripened and under-ripened are addressed with a proposal of machine learning approaches. Nevertheless, a hierarchical multi-classifiers fusion approach is designed for classification of ripened mangoes into perfect ripened, over ripened, over ripened with black spots on skin and without black spots on skin. Moreover, method of selecting the best wavelengths of NIR spectroscopy towards proposal of non-destructive method of finding internal defects in mangoes is also introduced. In addition, the most difficult issue of classifying ripened mangoes into naturally ripened and artificially ripened mangoes is also attempted, and a suitable classifier is presented. For each problem being addressed using classical machine learning methods, the corresponding counterparts from deep learning architectures are also highlighted. Results of extensive experimentation conducted to demonstrate the success of our approaches are presented on reasonably sized datasets of mango images created during the course of our research. A description on datasets along with difficulties faced while creating datasets are detailed. A comparative study on different approaches including deep learning based approaches is presented. Scope for future research in the similar directions is also explored. Overall, this paper is an attempt towards an integrated solution for postharvest handling of mangoes especially for automation of sorting and grading processes. © 2022, the Author(s), under exclusive license to Spr

关键词： Grading

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l2 Norm Prior-Based Modified Bright Channel for Low-Illumination images 6th

l2 Norm Prior-Based Modified Bright Channel for Low-Illumina...

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6th International conference on Mathematics and Computing, ICMC 2020

作者： Riya Gupta, Bhupendra Lamba, Subir Singh Design & Manufacturing PDPM Indian Institute of Information Technology JabalpurMP482005 India

ISBN: (纸本)9789811580604

Low-illumination image enhancement problem is a very challenging problem in many computer vision applications and, when it comes to nighttime low-illumination images, it becomes more challenging because the depth information of the low-illumination image is not known. Recently, bright channel prior-based methods are used to enhance the overall illumination of the image. the bright channel prior is based on statistical observation on the low-illumination image containing some regions with bright intensity pixels. In this paper, we propose an improved l2 norm-based prior bright channel to enhance the overall illumination of the image by maintaining the image contrast. this new generated bright channel is free from the block effect, which makes our method more robust than other methods. the experimental results show the effectiveness of the proposed method on the low-illumination images as well as on the nighttime low-illumination images. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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A Fuzzy based naturalness preserving image color compensation method for colorblindness 6

A Fuzzy based naturalness preserving image color compensatio...

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6th International conference on image Information processing, ICIIP 2021

作者： Singla, Anshu Meenakshi, M.S. Chitkara University Institute of Engineering Technology Chitkara University Computer Science and Engineering Punjab India

ISBN: (纸本)9781665433617

Eyesight is one of the key senses possessed by human beings to perceive the world of colors. Color blindness is a form of Color vision Deficiency (CVD) that affects about 0.5% of female and 8% of the male population [1]-[4]. the colorblind have difficulty perceiving specific colors called confusion colors. Hence, they face difficulty in handling routine activities [5]. With the increased use of the web, it is imperative to provide digital solutions to help the colorblind perceive colors. the authors propose a novel method using fuzzy based clustering to provide color compensation in images for red green color blindness. the method aims at optimizing the color enhancement by clustering the confusing colors and color compensating them. To achieve this, the authors have used the fuzzy C means algorithm and the delta E method to calculate the Euclidean distance between the original color and the compensated color. the delta E method ensures that the difference computed is within the perception of colors by humans. the efficiency of color compensation is measured by a) subjective evaluation by normal vision users b) evaluation by colorblind users and c) comparison with state-of-the-art method. the evaluation results affirm the efficiency. With the increased use of visualization and digitization of daily activities, the proposed method would aid colorblind users and help them to distinguish confusion colors in the images effectively. © 2021 IEEE.

关键词： Color

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