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Valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers 1

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ramesh, Sriramachandran Prasanna Kumar, N. Murali Krishnan, D.A. Parveen Sultana, H. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utilizes advanced technology to analyze images and text on name boards, transforming it into readable text for machines using sophisticated algorithms, the text is then translated to Tamil. The Valluvan app utilizes text-to-speech conversion methods to deliver speech output in Tamil, enhancing users’ understanding of translated content. The paper presents an overview of the app, discussing its methodology, implementation details, experimental results, and analysis. An app with potential to empower native Tamil speakers by facilitating access to and comprehension of information from diverse language sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Operation Sequence Design for image Segmentation Based on Multi-Objective Evolutionary algorithms 49

Operation Sequence Design for Image Segmentation Based on Mu...

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49th Latin American Computing conference, CLEI 2023

作者： Pinto-Roa, Diego P. Mello-Roman, Julio Cesar Noguera, Jose Luis Vazquez Quintana, Ramon Roa, Fredy Gardel-Sotomayor, Pedro Esteban Universidad Nacional de Asunción Facultad Politécnica San Lorenzo Paraguay Universidad Católica Nuestra Señora de la Asunción Facultad de Ciencias y Tecnología Hernandarías Paraguay

ISBN: (纸本)9798350318876

image segmentation is one of the first steps in most image processing procedures. The segmentation aims to obtain a more meaningful or simplified image representation by grouping pixels with common characteristics, which allows regions or features of interest to be uniquely identified. The result of the segmentation has a significant impact on the subsequent steps. Segmentation is part of several superior applications such as artificial vision, medical, topographic, and astronomical image analysis. No single or universal segmentation process gets optimal performance for all image types. Hence, determining a function that fits specific image types or applications becomes a detailed, complex, and not trivial task requiring much time and effort. In this paper, we propose using Multi-Objective Evolutionary algorithms (MOEAs) as a training tool that combines operations that represent the techniques and strategies commonly used for generating image segmentation. As a result, sequences of operations are suitable for specific applications or image types. The objective functions used to guide the evolutionary process are sensitivity maximization (TPR) and specificity maximization (TNR), the basic components of ROC analysis. Sensitivity and specificity are commonly used as classification metrics to evaluate the quality of a proposed segmentation compared to an ideal segmentation. We used sensitivity and specificity as objective functions rather than accuracy because, as stated in [1], the dependence on prevalence makes accuracy less effective than a simultaneous consideration of sensitivity and specificity. Experiments were conducted on multiple images that share common characteristics obtained from image databases, specifically: i) benign and malignant melanoma images, ii) ophthalmoscopic retinal images, and iii) binary cell form images, where the segmentation generated by the proposed algorithm was compared with ideal segmentation. The results are quite promising and show t

关键词： image segmentation

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Machine Learning for Medical image Analysis: A Survey 1

引用

International conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Fettah, Amina Menassel, Rafik Gattal, Abdeljalil Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9783031352485

ISBN: (纸本)9783031352478

Machine learning techniques have made significant progress in recent years in the field of healthcare by assisting clinicians in treatment interventions, identification, detection along with the classification of a variety of diseases, including Brain Tumors, Breast Cancer, diagnosis of diabetic retinopathy, as well as, more recently in dealing with the COVID-19 pandemic and its associated medical challenges. To that end, medical image processing employs certain strategies and techniques for manipulating images, which include image acquisition, storage, presentation, and transmission. In this paper, we have overviewed several machine learning and deep learning approaches that are commonly utilized for various image processing tasks, such as image segmentation, image detection, image classification, and image compression on different medical imaging modalities, among which are computed tomography (CT), magnetic resonance imaging (MRI), and x-ray radiography. Focusing on supervised learning and deep learning techniques when applied to medical imaging analysis to enhance the performance in different medical applications using a number of algorithms such as CNNs that lead to efficient outcomes in automatic feature extraction and acceleration of the training process in the detection and classification of different diseases. The methods collected in this survey paper and the results are discussed, and the main conclusions are highlighted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Medical imaging

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Auto-encoder Convolutıonal Neural Network (AECNN) for Apple Fruit Flower Detection 1

引用

Congress on Smart Computing Technologies, CSCT 2023

作者： Rajagopal, Manikandan Sathesh Kumar, K. Nagaraja, P. Sivasakthivel, Ramkumar Sivaraman, G. Bangalore India Computer Science and Engineering Alliance College of Engineering and Design Alliance University Central Campus Anekal Main Road Karnataka Bangalore India Bangalore India Bangalore India Department of Computer Science M.G.R. College Tamil Nadu Hosur India

ISBN: (数字)9789819750818

ISBN: (纸本)9789819750801

The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided through the bloom frequency, i.e., the quantity of the flowers that are present in a plantation. The intensity of bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend closely on designed procedures which function just under explicit conditions and with restricted execution. This work comprises four significant advances, (I) system preparing for Fully Convolutional Network (FCN), (ii) preprocessing, (iii) component extraction, (iv) division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics processing Unit (GPU). Then, non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. The next phase of the work handles the highlight extraction for diminishing the quality of the prime assets which are needed for handling without compromising on data applicable. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate an image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired score maps to figure out the final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision (P) and Recall (R). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing unit

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Automatic regions detection in CT images based on Haralick textures 17

Automatic regions detection in CT images based on Haralick t...

引用

17th Iberian conference on Information systems and Technologies (CISTI)

作者： Caridade, Cristina M. R. Almeida, Duarte Rodrigues, Simao ISEC Polytech Coimbra Coimbra Portugal ISEC Appl Biomech Lab CICGE Res Ctr Geospatial Sci Coimbra Portugal

ISBN: (纸本)9789893334362

The identification of anomalies (such as bone fractures or tendonitis in muscles and soft tissues) through image processing and analysis techniques in Computed Tomography (CT) images is today of great importance to assist doctors and health professionals in making accurate diagnoses. The extraction of relevant information from the CT image is characterized by the calculation of gray level input image attributes. Statistical moments (SM) are calculated using the gray level distribution of an image and are therefore generally calculated from that image's histogram. These characteristics provide a statistical description of the relationship between different gray levels in the CT image. Haralick proposed a methodology for describing textures based on second order statistics, where characteristics are derived from co-occurrence matrices, which are constructed by counting different combinations of gray levels in an image according to certain directions. In this work, it is intended to automatically identify and extract regions in CT images based on textures as an aid for a quick and accurate diagnosis. CT images are first pre-processed for noise reduction and image enhancement, followed by the application of Haralick textures to segment and detect zones of interest. Classifiers trained on the Haralick invariant features showed good accuracy and performance. Despite the presence of low contrast and noise in some images, the proposed algorithms present promising results in the segmentation and automatic identification of regions of tomographic images, being an important contribution to support health professionals in the characterization of anomalies and their extension. Good results are expected for the next step of this work in the detection and segmentation of anomalies in CT images.

关键词： Digital image processing image segmentation Haralick textures

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An image Region Hierarchical Encryption Approach for Edge Intelligence Perception 24

An Image Region Hierarchical Encryption Approach for Edge In...

引用

6th International conference on Telecommunications and Communication Engineering

作者： Hou, Chengyu Li, Wenzao Xiao, Linsong Yao, Sai Chengdu Univ Informat Technol Chengdu Peoples R China

ISBN: (纸本)9798400709630

In the context. of smart cities, edge-aware machine are widely used. These systems involve scenarios where large volumes of image data are stored locally. They also involve scenarios where image data is uploaded to edge clouds, posing significant privacy risks. 'therefore, it is necessary to encrypt images containing sensitive information. However, edge computational devices typically have limited computational ability. To address the need t'or privacy protection, this paper proposes a partial image encryption algorithm based on object detection. First, our approach uses an object detection model to identify private areas in images (such as license plates) and applies a specific encryption strategy to license plate areas. At the same time, the computational burden on edge devices is reduced. Additionally, we introduce a chaotic mapping algorithm based on image segmentation and compare its performance with traditional chaotic mapping algorithms. Experimental results show that the improved algorithm performs better in encrypting sensitive areas while also exhibiting superior performance in gray value histogram analysis and scatter plot analysis.

关键词： machine vision encryption and decryption, image segmentation chaotic mapping

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Attention based image Caption Generation (ABICG) using Encoder-Decoder Architecture 5

Attention based Image Caption Generation (ABICG) using Encod...

引用

5th International conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Kulkarni, Uday Tomar, Kushagra Kalmat, Mayuri Bandi, Rakshita Jadhav, Pranav Meena, S.M. Kle Technological University Dept. of Cse Hubballi India

ISBN: (纸本)9781665474672

The image captioning is utilized to develop the explanations of the sentences describing the series of scenes captured in the image or picture forms. The practice of using image captioning is vast although it is a tedious task for the machine to learn what a human is capable of. The model must be built in a way such that when it reads the scene, it recognizes and reproduce to-the-point captions or descriptions. The generated descriptions must be semantically and syntactically accurate. Hence, availability of Artificial Intelligence (AI) and Machine Learning algorithms viz. Natural Language processing (NLP), Deep Learning (DL) etc. makes the task easier. Although majority of the existing machine-generated captions are valid, they do not focus on the crucial parts of the images, which results in lesser clarity of the captions. In the proposed paper, anew introduction to attention mechanism called Bahdanau's along with EncoderDecoder architecture is being used so as to reflect the image captions that are more accurate and detailed. It uses a pretrained Convolutional Neural Network (CNN) called InceptionV3 architecture to gather the features of images and then a Recurrent Neural Network (RNN) called Gated Recurrent Unit (GRU) architecture in order to develop captions. This model is trained on Flickr8k dataset and the captions generated are 10% more accurate than the present state of art. © 2023 IEEE.

关键词： Recurrent neural networks

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Eye Movement analysis And Prediction Using image processing

Eye Movement analysis And Prediction Using Image Processing

引用

2024 International conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kalaiselvi, V.K.G. Doss, D. Information Technology Sri Sairam Engineering College Chennai India

ISBN: (纸本)9798350352689

The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models and techniques within popular computer vision libraries, the real-time demands and challenges posed by extreme head positions, occlusions, and inadequate lighting often limit their *** this article, we propose a range of software optimization techniques and training strategies centered on data augmentation. These strategies aim to enhance a diverse array of models integral to real-time face alignment algorithms. We advocate for a broader set of evaluation criteria, enabling innovative assessments that mitigate common issues encountered in real-time monitoring *** experimental findings showcase that models developed using our suggested methodologies exhibit smoother tracking, enhanced speed, reduced size, heightened accuracy, and greater resilience in challenging conditions. © 2024 IEEE.

关键词： Eye movements

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image Impulse Noise Removal Using a Hybrid System Based on Self Organizing Map Neural Networks And Median Filters 11

Image Impulse Noise Removal Using a Hybrid System Based on S...

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11th IEEE International conference on systems and Control, ICSC 2023

作者： Mohammed Hamid, Mabroukah Fathi Hammad, Fatimah Hmad, Nadia College of Computer Technology Department of Software Engineering and Development Tripoli Libya College of Sciences and Natural Resources Aljafara University Aljafara Libya

ISBN: (纸本)9798350304886

Digital image restoration has become important for many image applications. Therefore, image Noise removal is an essential issue in an image processing fields. In this paper, we presented a hybrid system, based on Self Organizing Maps neural networks (SOM NN) and Median filter (MF), to eliminate Random Impulsive Noises (RIN) from grayscaled digital images. In our system we applied two main processes (features extraction process, detection process): in the features extraction process, feature vectors of two or three features, (central pixel value, standard deviation value) or (pixel value, standard deviation, Mean Difference value) respectively, were extracted. In the detection process, the self-organizing maps (SOM NN) are used as an impulse noise detector. This SOM NN module is trained using competitive learning algorithms. Then the detected corrupted pixel is modified using Median filter (MF) algorithm;otherwise, it is left unchanged. Finally, the results of this study are compared with the previous traditional and state-of-the-art methods results that applied on the same database. Our results are significantly outperforms other traditional methods and were comparable with the state-of-the-art methods results in terms of reconstruction quality and that are comparable to the FL using the MSE and PSNR measurements. © 2023 IEEE.

关键词： Impulse noise

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A Novel Visual Communication Scenario Based on image processing algorithms

A Novel Visual Communication Scenario Based on Image Process...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Jinhua Yang Li Hou Xijing University Shaanxi Xian China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REVISED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio.

关键词： Training Adaptation models image coding Machine learning algorithms Visual communication image synthesis Heuristic algorithms Semantics Generative adversarial networks Neural style transfer

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