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Journal articles on the topic 'Automatic image description'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

Francisco, Manuela. "Reflection on Human and Automatic Image Description in Elearning Context." EDEN Conference Proceedings, no. 1 (October 21, 2020): 368–75. http://dx.doi.org/10.38069/edenconf-2020-rw-0040.

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Images have a strong presence in educational contexts, particularly in online environments. When images add vital information for the understanding of a given subject, this information must be perceived by all students, including those who have a visual impairment. Although most web tools and platforms have a field for the description or alternative text, most images do not contain this information. Since 2016, some platforms and web services have been providing features, based on Artificial Intelligence, which present a brief description of the images to screen readers. Are these descriptions

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Predić, Bratislav, Daša Manić, Muzafer Saračević, Darjan Karabašević, and Dragiša Stanujkić. "Automatic Image Caption Generation Based on Some Machine Learning Algorithms." Mathematical Problems in Engineering 2022 (April 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/4001460.

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This paper is dedicated to machine learning, the branches of machine learning, which include the methods for solving this issue, and the practical implementation of the solution to the automatic image description generation. Automatic image caption generation is one of the frequent goals of computer vision. Image description generation models must solve a larger number of complex problems to have this task successfully solved. The objects in the image must be detected and recognized, after which a logical and syntactically correct textual description is generated. For that reason, description

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Ramesh, J. "Image Generator: Harnessing Stable Diffusion." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48827.

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Abstract - Recent advancements in artificial intelligence have significantly improved the ability to create realistic images from text descriptions. This project presents "Stable Diffusion," a cutting-edge text-to-image generation model that produces photorealistic visuals through a distinctive iterative refinement technique. The model begins with a noise-filled image and progressively adjusts it to match the given textual prompt. This method, known as text-to-image synthesis, automates the creation of images based on input text. The process iterates until the output converges, ultimately gene

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Bernardi, Raffaella, Ruket Cakici, Desmond Elliott, et al. "Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures." Journal of Artificial Intelligence Research 55 (February 23, 2016): 409–42. http://dx.doi.org/10.1613/jair.4900.

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Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image dataset

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Pandey, Subash, Rabin Kumar Dhamala, Bikram Karki, Saroj Dahal, and Rama Bastola. "Automatic Image Captioning Using Neural Networks." Journal of Innovations in Engineering Education 3, no. 1 (2020): 138–46. http://dx.doi.org/10.3126/jiee.v3i1.34335.

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Automatically generating a natural language description of an image is a major challenging task in the field of artificial intelligence. Generating description of an image bring together the fields: Natural Language Processing and Computer Vision. There are two types of approaches i.e. top-down and bottom-up. For this paper, we approached top-down that starts from the image and converts it into the word. Image is passed to Convolutional Neural Network (CNN) encoder and the output from it is fed further to Recurrent Neural Network (RNN) decoder that generates meaningful captions. We generated t

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Lee, Jin Young. "Impact of Video Compression and Multimodal Embedding on Scene Description." Electronics 8, no. 9 (2019): 963. http://dx.doi.org/10.3390/electronics8090963.

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Scene description refers to the automatic generation of natural language descriptions from videos. In general, deep learning-based scene description networks utilize multimodalities, such as image, motion, audio, and label information, to improve the description quality. In particular, image information plays an important role in scene description. However, scene description has a potential issue, because it may handle images with severe compression artifacts. Hence, this paper analyzes the impact of video compression on scene description, and then proposes a simple network that is robust to c

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López-Sánchez, Marco, Betania Hernández-Ocaña, Oscar Chávez-Bosquez, and José Hernández-Torruco. "Supervised Deep Learning Techniques for Image Description: A Systematic Review." Entropy 25, no. 4 (2023): 553. http://dx.doi.org/10.3390/e25040553.

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Automatic image description, also known as image captioning, aims to describe the elements included in an image and their relationships. This task involves two research fields: computer vision and natural language processing; thus, it has received much attention in computer science. In this review paper, we follow the Kitchenham review methodology to present the most relevant approaches to image description methodologies based on deep learning. We focused on works using convolutional neural networks (CNN) to extract the characteristics of images and recurrent neural networks (RNN) for automati

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Hodosh, M., P. Young, and J. Hockenmaier. "Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics." Journal of Artificial Intelligence Research 47 (August 30, 2013): 853–99. http://dx.doi.org/10.1613/jair.3994.

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The ability to associate images with natural language sentences that describe what is depicted in them is a hallmark of image understanding, and a prerequisite for applications such as sentence-based image search. In analogy to image search, we propose to frame sentence-based image annotation as the task of ranking a given pool of captions. We introduce a new benchmark collection for sentence-based image description and search, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. We introduce a number

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Jivarajani, Kushal. "Automatic Synthesis of Realistic Human Faces from Text using GANs." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 7263–71. http://dx.doi.org/10.22214/ijraset.2023.53433.

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Abstract: The field of image generation has witnessed significant advancements in recent years, particularly through the application of Generative Adversarial Networks (GANs). Many capable generative adversarial networks (GANs) models have emerged in recent times which help in synthesizing, generating real-like images and playing around with images using text. However, most existing tasks are limited to generating simple images such as flowers from captions.This project aims to create a deep learning based model and a system that tries to generate realistic human facial images from a given tex

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Tadeusiewicz, Ryszard, Marek Ogiela, and Piotr Szczepaniak. "Notes on a Linguistic Description as the Basis for Automatic Image Understanding." International Journal of Applied Mathematics and Computer Science 19, no. 1 (2009): 143–50. http://dx.doi.org/10.2478/v10006-009-0013-7.

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Notes on a Linguistic Description as the Basis for Automatic Image UnderstandingThe main paradigm of image understanding and a concept for its practical machine realisation are presented. The crucial elements of the presented approach are the formalisation of human knowledge about the class of images that are to be automatically interpreted, a linguistic description and the realization of cognitive resonance.

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Bolodurina, I. P., A. O. Lositsky, E. L. Borshchuk, A. E. Voronina, A. Yu Zhigalov, and L. S. Grishina. "Intelligent system for automatic image description of optical coherence tomography." Journal of Physics: Conference Series 2514, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1742-6596/2514/1/012004.

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Abstract The development and implementation of an intelligent system for automatic image description of an optical coherence tomograph allows us to reduce the time of image processing and, consequently, to accelerate the diagnosis of diseases. To develop this system, it is necessary to solve many problems of binary classification for the presence of markers characterizing pathologies of various types or their absence. In this regard, this work is aimed at developing models that effectively solve many problems of binary classification of optical coherence tomography (OCT) images to describe the

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Chu, Yan, Xiao Yue, Lei Yu, Mikhailov Sergei, and Zhengkui Wang. "Automatic Image Captioning Based on ResNet50 and LSTM with Soft Attention." Wireless Communications and Mobile Computing 2020 (October 20, 2020): 1–7. http://dx.doi.org/10.1155/2020/8909458.

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Captioning the images with proper descriptions automatically has become an interesting and challenging problem. In this paper, we present one joint model AICRL, which is able to conduct the automatic image captioning based on ResNet50 and LSTM with soft attention. AICRL consists of one encoder and one decoder. The encoder adopts ResNet50 based on the convolutional neural network, which creates an extensive representation of the given image by embedding it into a fixed length vector. The decoder is designed with LSTM, a recurrent neural network and a soft attention mechanism, to selectively foc

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Alcalde-Llergo, José Manuel, Aurora Ruiz-Mezcua, Rocío Ávila-Ramírez, Andrea Zingoni, Juri Taborri, and Enrique Yeguas-Bolívar. "Automatic Identification and Description of Jewelry Through Computer Vision and Neural Networks for Translators and Interpreters." Applied Sciences 15, no. 10 (2025): 5538. https://doi.org/10.3390/app15105538.

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Identifying jewelry pieces presents a significant challenge due to the wide range of styles and designs. Currently, precise descriptions are typically limited to industry experts. However, translators and interpreters often require a comprehensive understanding of these items. In this study, we introduce an innovative approach to automatically identify and describe jewelry using neural networks. This method enables translators and interpreters to quickly access accurate information, aiding in resolving queries and gaining essential knowledge about jewelry. Our model operates at three distinct

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S R, Sreela, and Sumam Mary Idicula. "Dense Model for Automatic Image Description Generation with Game Theoretic Optimization." Information 10, no. 11 (2019): 354. http://dx.doi.org/10.3390/info10110354.

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Due to the rapid growth of deep learning technologies, automatic image description generation is an interesting problem in computer vision and natural language generation. It helps to improve access to photo collections on social media and gives guidance for visually impaired people. Currently, deep neural networks play a vital role in computer vision and natural language processing tasks. The main objective of the work is to generate the grammatically correct description of the image using the semantics of the trained captions. An encoder-decoder framework using the deep neural system is used

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Nahlik, Jan, Jaromír Kukal, Jan Kohout, Jan Mareš, Pavel Hrnčiřík, and Pavel Vařacha. "Digital Image Analysis for Morphological State Characterization of a Culture of Filamentous Microorganisms in Production of Antibiotics." Journal of Biomimetics, Biomaterials and Biomedical Engineering 43 (November 2019): 74–83. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.43.74.

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The paper deals with the application of specific methods of digital image analysis for the monitoring of morphological changes in cultures of filamentous microorganisms. First, a sequence of digital image preprocessing and processing steps is proposed for the treatment of microscopic images of a filamentous culture. The preprocessing step include band pass filtering by the Difference of Gaussians filter featuring a novel approach to the task of parameters tuning, as well as the optimization of image porosity and image objects separation quality. In the processing step, the resulting enhanced i

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Phyu, Phyu Khaing, Mie Aung Mie, and San Myint. "Natural Language Description Generation for Image using Deep Learning Architecture." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1575–81. https://doi.org/10.5281/zenodo.3591081.

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Automatic natural description generation of an image is currently a challenging task. To generate a natural language description of the image, the system is implemented by combining with the techniques of computer vision and natural language processing. This paper presents different deep learning models for generating the natural language description of the image. Moreover, we discussed how the deep learning model, which works for the natural language description of an image, can be implemented. This deep learning model consists of Convolutional Neural Network CNN as well as Recurrent Neural N

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Ravulaplli, Lakshmi Tulasi. "A Novel Bi-LSTM Based Automatic Image Description Generation." Ingénierie des systèmes d information 28, no. 2 (2023): 527–34. http://dx.doi.org/10.18280/isi.280230.

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Florea, Corneliu, Andrei Racoviţeanu, Laura Florea, and Bogdan Florea. "Automatic Real-Estate Image Analysis for Retrieval and Classification." Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section 68, no. 2 (2022): 35–45. http://dx.doi.org/10.2478/bipie-2022-0009.

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Abstract Nowadays, the real estate sector is also benefiting from the advance of the technology. Digital images accompany announcements advertising for renting or selling a property. Computer vision and image analysis contribute to the understanding of these images towards better consumer experience. In this paper we show how retrieval and description can be used efficiently to isolate cvasi-duplicate images and identify similar examples to the ones that have drawn viewer’s interest. Three problems related to real-estate imaging are identified and solutions are proposed and evaluated on large

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HELMY, TAREK. "A COMPUTATIONAL MODEL FOR CONTEXT-BASED IMAGE CATEGORIZATION AND DESCRIPTION." International Journal of Image and Graphics 12, no. 01 (2012): 1250001. http://dx.doi.org/10.1142/s0219467812500015.

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Automatic image categorization and description are key components for many applications, i.e., multimedia database management, web content analysis, human–computer interactions, and biometrics. In general, image description is a difficult task because of the wide variety of objects potentially to be recognized and the complexity and variety of backgrounds. This paper introduces a computational model for context-based image categorization and description. First, for a given image, a classifier is trained by the associated text features using advanced concepts, so that it can assign the image to

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Jishan, Md. Asifuzzaman, Khan Raqib Mahmud, Abul Kalam Al Azad, Mohammad Rifat Ahmmad Rashid, Bijan Paul, and Md. Shahabub Alam. "Bangla language textual image description by hybrid neural network model." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 757–67. https://doi.org/10.11591/ijeecs.v21.i2.pp757-767.

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Automatic image captioning task in different language is a challenging task which has not been well investigated yet due to the lack of dataset and effective models. It also requires good understanding of scene and contextual embedding for robust semantic interpretation of images for natural language image descriptor. To generate image descriptor in Bangla, we created a new Bangla dataset of images paired with target language label, named as Bangla natural language image to text (BNLIT) dataset. To deal with the image understanding, we propose a hybrid encoder-decoder model based on encoder-de

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Qi, Li Ying, and Ke Gang Wang. "Information System in Image Classification Based on SVM and Color Clustering Analysis." Advanced Materials Research 886 (January 2014): 572–75. http://dx.doi.org/10.4028/www.scientific.net/amr.886.572.

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Effective use of the color feature of Content Based Image Retrieval (CBIR) and Image classification is an important basic research, but there are some shortcomings in the color histogram representation method, such as high dimension, pixels spatial information is ignored and so on. Although color feature data can reduce the dimension by quantification, but some useful image color information will be discard. In this paper, the image color information processing in space constrained fuzzy clustering to obtain a lower dimensional color feature data of the image characteristics of domain colors d

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Amin, Asjad, and Muhammad Qureshi. "A Novel Image Retrieval Technique using Automatic and Interactive Segmentation." International Arab Journal of Information Technology 17, no. 3 (2019): 404–10. http://dx.doi.org/10.34028/iajit/17/3/14.

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In this paper, we present a new region-based image retrieval technique based on robust image segmentation. Traditional content-based image retrieval deals with the global description of a query image. We combine the state-of-the-art segmentation algorithms with the traditional approach to narrow the area of interest to a specific region within a query image. In case of automatic segmentation, the algorithm divides a query image automatically and computes Zernike moments for each region. For interactive segmentation, our proposed scheme takes as input a query image and some information regardin

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Kong, Zhenglun, Ting Li, Junyi Luo, and Shengpu Xu. "Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning." Journal of Healthcare Engineering 2019 (January 31, 2019): 1–10. http://dx.doi.org/10.1155/2019/2912458.

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Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal flu

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Jishan, Md Asifuzzaman, Khan Raqib Mahmud, Abul Kalam Al Azad, Mohammad Rifat Ahmmad Rashid, Bijan Paul, and Md Shahabub Alam. "Bangla language textual image description by hybrid neural network model." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 757. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp757-767.

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Automatic image captioning task in different language is a challenging task which has not been well investigated yet due to the lack of dataset and effective models. It also requires good understanding of scene and contextual embedding for robust semantic interpretation of images for natural language image descriptor. To generate image descriptor in Bangla, we created a new Bangla dataset of images paired with target language label, named as Bangla Natural Language Image to Text (BNLIT) dataset. To deal with the image understanding, we propose a hybrid encoder-decoder model based on encoder-de

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Hussnain, Zille, Sander Oude Elberink, and George Vosselman. "AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 609–16. http://dx.doi.org/10.5194/isprs-archives-xli-b1-609-2016.

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In mobile laser scanning systems, the platform’s position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of

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Hussnain, Zille, Sander Oude Elberink, and George Vosselman. "AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 609–16. http://dx.doi.org/10.5194/isprsarchives-xli-b1-609-2016.

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In mobile laser scanning systems, the platform’s position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of

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Wei, Dehua, Xiukun Wei, and Limin Jia. "Automatic Defect Description of Railway Track Line Image Based on Dense Captioning." Sensors 22, no. 17 (2022): 6419. http://dx.doi.org/10.3390/s22176419.

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The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can b

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Qian, Chun Hua, He Qun Qiang, and Sheng Rong Gong. "Orange Feature Extraction and Description Based on Image Processing." Applied Mechanics and Materials 713-715 (January 2015): 1804–7. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1804.

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Automatic orange quality classification based on computer image processing is accurate and efficient. In this paper, we discuss the orange feature extraction and description method based on image processing. Design an orange image edge detection method based on Canny operator, color characteristics description methods based on HIS model and shape characteristics description methods based on Fourier descriptor operator. The experiment result proof that Canny operator is high SNR,high accuracy and low computation;HIS model is more accord with human vision and low computation also; shape characte

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Simaran, Singh, Pandey Pallavi, Kumar Atul, and Srivastava Vibha. "Automatic Caption Generation for Chest X-Ray Using CNN Algorithm." Advancement in Image Processing and Pattern Recognition 6, no. 3 (2023): 11–16. https://doi.org/10.5281/zenodo.7952035.

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<em>The automatic caption generation of chest X-ray report is a hot research topic at present. Image captioning aims to automatically describe the relationship of an image with a sentence, and this work has attracted research from both computer vision and natural language processing research communities. This research paper proposes a novel approach to automatically generating captions for medical images using Convolutional Neural Network (CNN) algorithm. The system was trained on a large dataset of medical images and their corresponding captions, and was evaluated using a variety of metrics i

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Ricci, Riccardo, Yakoub Bazi, and Farid Melgani. "Machine-to-Machine Visual Dialoguing with ChatGPT for Enriched Textual Image Description." Remote Sensing 16, no. 3 (2024): 441. http://dx.doi.org/10.3390/rs16030441.

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Image captioning is a technique that enables the automatic extraction of natural language descriptions about the contents of an image. On the one hand, information in the form of natural language can enhance accessibility by reducing the expertise required to process, analyze, and exploit remote sensing images, while on the other, it provides a direct and general form of communication. However, image captioning is usually restricted to a single sentence, which barely describes the rich semantic information that typically characterizes remote sensing (RS) images. In this paper, we aim to move o

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Ogiela, Lidia. "Cognitive informatics in image semantics description, identification and automatic pattern understanding." Neurocomputing 122 (December 2013): 58–69. http://dx.doi.org/10.1016/j.neucom.2013.06.001.

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Barreto, Artur Gomes, Juliana Martins de Oliveira, Francisco Nauber Bernardo Gois, Paulo Cesar Cortez, and Victor Hugo Costa de Albuquerque. "A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks." Bioengineering 10, no. 9 (2023): 1098. http://dx.doi.org/10.3390/bioengineering10091098.

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The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions. Additionally, there is a lack of model generalizat

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Hundzina, M. A., and M. N. Zhdanovich. "Automatic Localization of License Plate for Car in Wolfram Mathematica." Science & Technique 21, no. 5 (2022): 367–73. http://dx.doi.org/10.21122/2227-1031-2022-21-5-367-373.

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Modern imaging devices make it possible to solve a complex of technical applied problems that require the synthesis and analysis of computer processing methods using threshold binarization, image classification, clustering, and the use of machine learning to determine areas of interest. Thus, segmentation algorithms are widely used for processing medical images. Computer technologies are used for the functioning of the intellectual environment, which allows to analyze the state of human health. The development of microelectronics makes it possible to increase the complexity of the applied imag

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Kesiman, Made Windu Antara, and Gede Aditra Pradnyana. "A Scheme Towards Automatic Word Indexation System for Balinese Palm Leaf Manuscripts." Journal of ICT Research and Applications 15, no. 2 (2021): 105–19. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.15.2.1.

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This paper proposes an initial scheme towards the development of an automatic word indexation system for Balinese lontar (palm leaf manuscript) collections. The word indexation system scheme consists of a sub module for patch image extraction of text areas in lontars and a sub module for word image transliteration. This is the first word indexation system for lontar collections to be proposed. To detect parts of a lontar image that contain text, a Gabor filter is used to provide initial information about the presence of text texture in the image. An adaptive sliding patch algorithm for the ext

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Luo, Xin, Guangling Lai, Xiao Wang, et al. "UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features." Remote Sensing 13, no. 18 (2021): 3605. http://dx.doi.org/10.3390/rs13183605.

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With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration

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Lee, C.-S., and F.-B. Hsiao. "Implementation of vision-based automatic guidance system on a fixed-wing unmanned aerial vehicle." Aeronautical Journal 116, no. 1183 (2012): 895–914. http://dx.doi.org/10.1017/s000192400000734x.

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Abstract This paper presents the design and implementation of a vision-based automatic guidance system on a fixed-wing unmanned aerial vehicle (UAV). The system utilises a low-cost ordinary video camera and simple but efficient image processing techniques widely used in computer-vision technology. The paper focuses on the identification and extraction of geographical tracks such as rivers, coastlines, and roads from real-time aerial images. The image processing algorithm primarily uses colour properties to isolate the geographical track of interest from its background. Hough transform is event

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Hou, Vincent D. H. "Automatic Page-Layout Scripts for Gatan Digital Micrograph®." Microscopy and Microanalysis 7, S2 (2001): 976–77. http://dx.doi.org/10.1017/s1431927600030956.

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The software DigitalMicrograph (DM) by Gatan, Inc., is a popular software platform for digital imaging in microscopy. in a service-oriented microscopy laboratory, a large number of images from many different samples are generated each day. It is critical that each printed image is properly labeled with sample identification and a description before printing. with DM, a script language is provided: from this, various analyses can be designed or customized and repetitive tasks can be automated. This paper presents the procedures and DM scripts needed to perform these tasks. Due to the major soft

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Kalam, Mr Md Abdul. "A STUDY ON A JOURNEY THROUGH STORIES IN SEQUENTIAL FRAMES." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03444.

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ABSTRACT: This project is aimed at educating machines to narrate visual stories through creating descriptive and coherent text from image sequences. Based on analyzing objects, actions, emotions, and scene changes, the system constructs narrative text reflecting the story in the images. It intends to simulate human storytelling by extracting temporal and contextual relationships among images. Applications are automatic photo album description, digital storytelling, and assistive technology for the visually impaired. By closing the gap between visual perception and language generation, this app

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Amma, Sreela Sreekumaran Pillai Remadevi, and Sumam Mary Idicula. "A Unified Visual Saliency Model for Automatic Image Description Generation for General and Medical Images." Advances in Science, Technology and Engineering Systems Journal 7, no. 2 (2022): 119–26. http://dx.doi.org/10.25046/aj070211.

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Reinhuber, Elke. "Synthography: A term for synthetically created photo-realistic images." Philosophy of Photography 15, no. 1 (2024): 173–86. http://dx.doi.org/10.1386/pop_00099_7.

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With the advent of AI-generated photorealistic images in easily accessible online resources, synthetic imaging suddenly is widely discussed, obscuring the quiet revolution that has transformed image-making in the digital realm over the last decades. ‘The decisive moment’ has been taken out of the photographer’s hands a long time ago and the numerous automatic mechanisms integrated into the apparatus and the editing pipeline question the idea of sole authorship. This reassessment and re-evaluation of photographic images demands for a precise, differentiated description for images that are not p

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Sioma, Andrzej. "Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis." Materials 13, no. 5 (2020): 1250. http://dx.doi.org/10.3390/ma13051250.

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This paper presents a method of acquisition and analysis of three-dimensional images in the task of automatic location and evaluation of defects on the surface of ceramic tiles. It presents a brief description of selected defects appearing on the surface of tiles, along with the analysis of their formation. The paper includes the presentation of the method of constructing a 3D image of the tile surface using the Laser Triangulation Method (LTM), along with the surface imaging parameters employed in the research. The algorithms of three-dimensional surface image analysis of ceramic tiles used i

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Yan, Liyuan, Korhan Cengiz, and Amit Sharma. "An improved image processing algorithm for automatic defect inspection in TFT-LCD TCON." Nonlinear Engineering 10, no. 1 (2021): 293–303. http://dx.doi.org/10.1515/nleng-2021-0023.

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Abstract The demand to improve image display in TFT-LCD, implementation of design for image processing is important. In order to meet the specific requirements of low-end Thin Film Transistor-Liquid-Crystal-Display (TFT-LCD) image display. This paper adopts a novel algorithm to conduct subsequent processing of the medical image after SCALER scaling, including contrast adjustment, gamma correction and dithering. Dithering algorithm is the focus of our research. After the study of some classical video image processing algorithms, and considering the real-time requirements, an intelligent algorit

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Dovgal, Sofiia, Egor Mukhaev, Marat Sabitov, and Lyubov' Adamcevich. "Development of a web service for processing data from electronic images of urban plans of land plots." Construction and Architecture 11, no. 1 (2023): 17. http://dx.doi.org/10.29039/2308-0191-2022-11-1-17-17.

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The article gives an idea of the content of urban planning plans for land plots (UPPLP), their purpose, as well as the relevance of developing a service for automatic recognition of data from an electronic image of a document. The existing services for automatic processing of documents are analyzed, and a technical solution developed by the authors is presented in the form of a web service for parsing and structuring electronic images of UPPLP. The description of the structure and operation of the web service, as well as the data conversion algorithm implemented in the solution is given.

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Yao, Guobiao, Alper Yilmaz, Fei Meng, and Li Zhang. "Review of Wide-Baseline Stereo Image Matching Based on Deep Learning." Remote Sensing 13, no. 16 (2021): 3247. http://dx.doi.org/10.3390/rs13163247.

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Strong geometric and radiometric distortions often exist in optical wide-baseline stereo images, and some local regions can include surface discontinuities and occlusions. Digital photogrammetry and computer vision researchers have focused on automatic matching for such images. Deep convolutional neural networks, which can express high-level features and their correlation, have received increasing attention for the task of wide-baseline image matching, and learning-based methods have the potential to surpass methods based on handcrafted features. Therefore, we focus on the dynamic study of wid

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Eremeev, Sergey, Artyom Abakumov, Dmitry Andrianov, and Tamara Shirabakina. "Vectorization Method of Satellite Images Based on Their Decomposition by Topological Features." Informatics and Automation 22, no. 1 (2023): 110–45. http://dx.doi.org/10.15622/ia.22.1.5.

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Vectorization of objects from an image is necessary in many areas. The existing methods of vectorization of satellite images do not provide the necessary quality of automation. Therefore, manual labor is required in this area, but the volume of incoming information usually exceeds the processing speed. New approaches are needed to solve such problems. The method of vectorization of objects in images using image decomposition into topological features is proposed in the article. It splits the image into separate related structures and relies on them for further work. As a result, already at thi

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KIM, SOO HYUNG, and JIN HYUNG KIM. "AUTOMATIC INPUT OF LOGIC DIAGRAMS BY RECOGNIZING LOOP-SYMBOLS AND RECTILINEAR CONNECTIONS." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 05 (1994): 1113–29. http://dx.doi.org/10.1142/s0218001494000553.

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An automatic system that reads an image of a logic diagram, digitized by a document scanner, and generates a description of the diagram in terms of logic symbols and their interconnections is proposed. The digitized image is converted into a set of line segments by way of a sequence of picture processing operations, Then symbols and connections are recognized by identifying loops and rectilinear polylines. Graphical description of a set of symbol models is provided as prior knowledge about input diagrams. Experiments show that the system can recognize more than 96% of logic symbols and connect

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Chen, Dali, Yang Liu, Shixin Liu, Fang Liu, and Yangquan Chen. "Framework of Specific Description Generation for Aluminum Alloy Metallographic Image Based on Visual and Language Information Fusion." Symmetry 12, no. 5 (2020): 771. http://dx.doi.org/10.3390/sym12050771.

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The automatic generation of language description is an important task in the intelligent analysis of aluminum alloy metallographic images, and is crucial for the high-quality development of the non-ferrous metals manufacturing industry. In this paper, we propose a methodological framework to generate the language description for aluminum alloy metallographic images. The framework consists of two parts: feature extraction and classification. In the process of feature extraction, we used ResNet (residual network) and CNN (convolutional neural network) to extract visual features from metallograph

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Nubila, B. Di, I. Gagliardi, D. Macchi, L. Milanesi, M. Padula, and R. Pagani. "Concept-based indexing and retrieval of multimedia documents." Journal of Information Science 20, no. 3 (1994): 185–96. http://dx.doi.org/10.1177/016555159402000304.

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In this work, we face the problem of multimedia document indexing with reference to a specific application field, the radiological ward where automatic information management by content is an urgent need. Here, a multimedia document is composed of text and images. The keystone of the approach is the image indexing which is performed in an indirect way: the description of the image (made by an expert, in our case a physician) is further synthesised and formalised to be used by the computer. In this Paper, we propose a concept-based indexing of the description of the images which is based on Far

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Fonollà, Roger, Quirine E. W. van der Zander, Ramon M. Schreuder, et al. "Automatic image and text-based description for colorectal polyps using BASIC classification." Artificial Intelligence in Medicine 121 (November 2021): 102178. http://dx.doi.org/10.1016/j.artmed.2021.102178.

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Omri, Mohamed, Sayed Abdel-Khalek, Eied M. Khalil, Jamel Bouslimi, and Gyanendra Prasad Joshi. "Modeling of Hyperparameter Tuned Deep Learning Model for Automated Image Captioning." Mathematics 10, no. 3 (2022): 288. http://dx.doi.org/10.3390/math10030288.

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Image processing remains a hot research topic among research communities due to its applicability in several areas. An important application of image processing is the automatic image captioning technique, which intends to generate a proper description of an image in a natural language automated. Image captioning is a recently developed hot research topic, and it started to receive significant attention in the field of computer vision and natural language processing (NLP). Since image captioning is considered a challenging task, the recently developed deep learning (DL) models have attained si

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