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Real-Time Retrieving Vedic Sanskrit Text into Multi-Lingual Text and Audio for Cultural Tourism Motivation 2

Real-Time Retrieving Vedic Sanskrit Text into Multi-Lingual ...

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2nd international conference for Advancement in Technology, ICONAT 2023

作者： Chand, Anchal Agarwal, Piyush Sharma, Sachin Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India

ISBN: (纸本)9781665475174

There are many uses for handwritten writing that has been digitally transformed. helping both those working in the field of education and visitors better understand the local culture. The Vedas are the best informational source for understanding Indian culture;the only drawback is that it exists in physical form. The ability to access cultural knowledge and historic heritage is facilitated by the real-time retrieval and translation of Vedic Sanskrit text. The tourist will benefit from this in understanding Indian culture and traditions and its significance. The manual Sanskrit text is translated into an easily editable format using the suggested methods in this study. The language translation and the audio generation in a specific selected language are the features of the methodology used. The proposed method has a four-step process i) image processing, ii) text extraction, iii) text-to-speech conversion, and iv) multi language translation. The OTSUs threshold method is employed in the suggested method to differentiate between the image's foreground and background. Tesseract OCR is used for image processing. The LSTM is utilised in the real-time text extraction process. The outcome demonstrates that the suggested technique was effective in rendering the Sanskrit text into a variety of languages for the purpose of promoting cultural tourism. © 2023 IEEE.

关键词： computer vision

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Entity Linking Supported Multimodal Data: Fusing Text and image features for Higher Accuracy 2

Entity Linking Supported Multimodal Data: Fusing Text and Im...

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Chen, Yuke Wu, Peng Zhao, Xing Dai, Yang Nanjing University of Science and Technology School of Cyberspace Security Nanjing China Nanjing University of Science and Technology School of Intellgent Manufacturing Nanjing China

ISBN: (纸本)9798350331417

To address the problem of the low accuracy of traditional text-only data entity linking methods, this paper proposes a new multimodal entity-linking model that leverages the richness and complementarity of multi-modal information by effectively integrating text and image characteristics to enhance the accuracy of entity linking. The proposed method uses Bert model and the CNN-RNN model to stratify the image and text characteristics that contain the references, respectively;then the mechanism of co-attention and the method of gate fusion are imported to learn the correlation between text and images automatically;and the weight and importance of the characteristics are adjusted to achieve accurate alignment and interaction between the text and the images. Finally, the cosine similarity are used to measure the similarities between the candidate entities and mentions. Furthermore, experimental research is proposed with the RMEL and WMEL Multimodal Entity Linking *** results show that the proposed method outperforms other entity-linked models. © 2023 IEEE.

关键词： Co-Attention Hierarchical feature extraction Multimodal Entity Linking Multimodal fusion

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A method of complex conjugate artifact elimination for SS-OCT systems based on adversarial generative model 2

A method of complex conjugate artifact elimination for SS-OC...

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Zhang, Bo Zheng, Runan Wang, Feng Southeast University School of Biological Science and Medical Engineering Nanjing China Suzhou MicroClear Medical Instruments Co. Ltd. Suzhou China

ISBN: (纸本)9798350331417

Swept source optical coherence tomography is a common medical instrument used for ophthalmic clinical examination. However, the presence of complex conjugate artifacts may cause severe occlusion of eye cross-sectional structure in B-scan images. Existing high-speed complex conjugate artifact elimination methods often require additional hardware in the system, increasing the system complexity and manufacturing cost. In this article, we propose a two-step complex conjugate artifact elimination method. Firstly, we detect the position of complex conjugate artifacts morphological image processing methods. Subsequently, a proposed adversarial generative model is used to translate images with complex conjugate artifacts into images without complex conjugate artifacts. We trained and tested the model on a self-built dataset to verify the feasibility of this method. Quantitative and qualitative analysis of the results show that this method can effectively suppress complex conjugation artifacts and preserve the correct artifact-free fundus structure. © 2023 IEEE.

关键词： artifact elimination deep learning image generation image process SS-OCT

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A Hybrid of Inference and Stacked Classifiers to Indoor Scenes Classification of RGB-D images 12

A Hybrid of Inference and Stacked Classifiers to Indoor Scen...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Ahmadi, Shokouh S. Khotanlou, Hassan Bu-Ali Sina University RIV Lab. Department of Computer Engineering Hamedan Iran

ISBN: (纸本)9781665412162

Scene classification makes it easier to semantic scene understanding and aids to further processes and inference, using an assignment of pre-defined classes. Under this motive, we proposed an approach to classify indoor scene objects. The proposed method utilizes a stacked classifier model and refines classification results considering segment consistency. Furthermore, the challenging and messy indoor scene images have been addressed, as dealing daily. Finally, this approach simplicity and affordably obtains desirable classification results. © 2022 IEEE.

关键词： Semantics

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Conditional Generative Adversarial Networks in computer vision: An Introduction and Outlook 2

Conditional Generative Adversarial Networks in Computer Visi...

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2nd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2022

作者： Gao, Mengkun Department of Computer Science University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9781510657717

Conditional Generative Adversarial Networks (CGAN) is an architecture-variant Generative Adversarial Network (GAN) that inputs conditional data into generator and discriminator simultaneously. CGAN has been applied in a wide range of fields in recent years. One of the most famous usages is its application in computer vision, which enables to perform images and videos transformation with specified conditions. In this paper, we first introduce the structure of GAN, which supports every CGAN system, and the basic CGAN that has been demonstrated in computer vision. Then, we review different implementations of CGAN in two major computer vision domains, i.e., the image processing domain and the videos processing domain. Besides, we review three representative research orientations in each domain, which all implement CGAN or variants of CGAN. Moreover, there is a comparison or declaration of several outstanding approaches in each direction. © 2022 SPIE. All rights reserved.

关键词： Generative adversarial networks

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Facial Emotion Recognition Using Keras and CNN 2

Facial Emotion Recognition Using Keras and CNN

引用

2nd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Maurya, Aakash Sharma, Vandana Galgotias University Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9781665437899

Deep Learning is penetrating most of the recognition and classification tasks performed through a computer or machine. Human facial emotion recognition plays an important and vital role in the interpersonal relationship. Deep learning (DL) based emotion detection gives much better and exact results than traditional methods with image processing. It has been recognized for a many year and it is a vital topic in the fields of computer vision and machine learning. Artificial Intelligence (AI) system is capable to emotion discovery through facial expressions. By using Neural Network classifier training, five kinds of different emotional orders are attained from pics. Through this we proposed a computer vision (CV) based deep learning architecture for emotion detection from images. We collect the datasets from social media platforms and various websites. In this dataset, data will be presented in the form of image type with some facial expressions. The performance of the proposed method is evaluated using datasets for restaurants which are collected from different social media platforms and websites. Our aim is to understand the emotions of different people through their expressions. © 2022 IEEE.

关键词： Emotion Recognition

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Diabetic Retinopathy Detection using Squeezenet 2

Diabetic Retinopathy Detection using Squeezenet

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Nanthini, K. Sivabalaselvamani, D. Sharvanthika, K.S. Ojha, Shubham Kumar Siva, M. Department of Computer Applications Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350340235

Diabetic retinopathy (DR), which causes lesions on the retina and impairs vision, is a common complication of diabetes mellitus. It can cause blindness if it is not caught in time. With early identification and treatment of DR, the risk of vision loss can be considerably decreased. Ophthalmologists must manually diagnose DR retina fundus images, which is more expensive, time-consuming, and error-prone than computer-aided diagnosis technologies. Recently, deep learning has become one of the most well-liked techniques forenhancing performance, especially in classifying and interpreting medical pictures. Deep learning models can detect specific lesions or abnormalities associated with diabetic retinopathy such as microaneurysms, hemorrhages, exudates, and neovascularization. By training CNNs on annotated retinal images with labeled lesions, these models can learn to identify and localize the presence of these abnormalities, aiding in early detection and diagnosis. The images have been converted to grayscale using an image batch processor and the images are resized are the pre-processing steps. Using deep learning algorithms like Squeezenet, diabetic retinopathy images are classified into No DR indications, mild NPDR, moderate NPDR, Severe NPDR, extremely severe NPDR, PDR, and Advanced PDR cases. Squeezenet produced an accuracy of 94.7% without augmentation and had an accuracy of 97 percent. Then the pre-processed images are undergone. © 2023 IEEE.

关键词： Classification Diabetic retinopathy Retino images Squeezenet

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Improvement of Human Tracking Based on an Accurate Estimation of Feet or Head Position 12

Improvement of Human Tracking Based on an Accurate Estimatio...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Dadgar, Alireza Baleghi, Yasser Ezoji, Mehdi Babol Noshirvani University of Technology Department of Electrical and Computer Engineering Babol Iran

ISBN: (纸本)9781665412162

In this paper a method is presented to estimate the position of feet/head of objects in various camera views. In this method, first, all objects in the scene are detected using the background subtraction. Then, human and non-human objects are separated via the support vector machine (SVM) that is trained based on local binary patterns (LBP) features. The basic idea of the next step of this work is that the feet/head of an object are the group of pixels that are projected to small region on ground/top plane by corresponding homography matrix. This idea is expressed via an optimization problem which avoids partitioning out small group of pixels. Experimental results show that the proposed methods can improve the accuracy of the object tracking. © 2022 IEEE.

关键词： Local binary pattern

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image De-hazing techniques for vision based applications - A survey 2

Image De-hazing techniques for Vision based applications - A...

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2nd international conference for Advancement in Technology, ICONAT 2023

作者： Santhosh Krishna, B.V. Rajalakshmi, B. Dhammini, U. Monika, M.K. Nethra, C. Ashok, K. New Horizon College of Engineering Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665475174

Haze is defined as a poor condition described by an iridescent atmospheric appearance that reduces clarity and visibility. The main reason for this is lot of toxic elements like dust particles, smoke in the atmosphere scattering and absorbing sun light. This poor intelligibility causes various computer vision applications to fail, including intelligent transportation, video surveillance, element recognition, and in a method to perform operations on image to get better image. There is a problem in domain of image processing wherein image recovery by various degradations is a challenge. Pictures and videos taken in outdoor environments usually suffer from reduced contrast, faded colors and with reduced visibility due to airborne particles, which directly affect image quality. This can lead to problems recognizing objects captured in blurry or still images. Several images clean up techniques have been developed to solve this problem, each with their own strengths and weaknesses, but effective image recovery is daunting task. Recently, many learning-based methods (predictive analytics and natural language processing) have tried to overcome the shortcomings of mechanical representation of properties and alleviated the challenge of efficiently reconstructing images by spending with reduce cost and comparatively reduced time. This overview delves into latest techniques for imaging with no-fog. In addition, hardware execution of many real time dehaze methods have been methodically outlined by this paper. The study done in this paper paves a way for researches in image dehazing domain as-well-as will direct them for doing further enhancement on the basis of achievements done currently. © 2023 IEEE.

关键词： Visibility

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Multiscale Models for Real-Time Human Pose Estimation 2

Multiscale Models for Real-Time Human Pose Estimation

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2nd international conference on Frontiers of Artificial Intelligence and Machine Learning, FAIML 2023

作者： Liu, Zongyou Li, Wenbai Wang, Hongtao Computer Scienc Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798400707544

Although the lightweight OpenPose uses the depth-wise separable convolution to form the backbone to improve the computing efficiency of the model on the CPU, the lightweight OpenPose also has the following shortcomings. (1) The model has weak feature processing ability. (2) The model cannot identify multi-scale features well. In order to solve the above problems, a multi-scale model is proposed. We adjusted the network structure of the lightweight OpenPose model with reference to the network structure of RES2NET. The modified model not only recognizes multi-scale features but also has strong feature processing capabilities. The AP value of our model on the COCO2017 validation set is 0.412. The AP value of Multiscale Models is 0.037 higher than lightweight OpenPose on the COCO2017 validation set. Our future direction is to discover the influence of the model’s ability to extract multi-scale features on the model AP value. © 2023 Copyright held by the owner/author(s).

关键词： computer vision

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