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Journal articles on the topic 'Database searching. Image processing. Color vision'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Li, Chang Chun, Shi Feng Wang, Jing Yu, and Hua Guan Liu. "Study on Image Processing of Wheel Valve Hole Based on Machine Vision." Applied Mechanics and Materials 63-64 (June 2011): 541–46. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.541.

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This paper discusses the basic principle for automatic searching the wheel valve hole based on machine vision. Image acquisition and image processing have been done, and we analyzed the factors that impact the image quality of wheel valve hole. This paper argues that many parameters such as the wheel speed, painting color, the distance between the camera and the valve hole, edge detection operator, and they will affect the quality of the image acquisition and image processing of valve hole.

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Rakhimov, Bakhtiyar Saidovich, Feroza Bakhtiyarovna Rakhimova, Sabokhat Kabulovna Sobirova, Furkat Odilbekovich Kuryazov, and Dilnoza Boltabaevna Abdirimova. "Review And Analysis Of Computer Vision Algorithms." American Journal of Applied sciences 03, no. 05 (2021): 245–50. http://dx.doi.org/10.37547/tajas/volume03issue05-39.

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Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific in

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Pandey, Adi Sugita, I. Gede Pasek Suta Wijaya, and Fitri Bimantoro. "Haar Wavelet Untuk Ekstraksi Fitur Energi, Standar Deviasi, Dan Histogram Dalam Sistem Temu Kembali Citra." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 2, no. 1 (2020): 40–49. http://dx.doi.org/10.29303/jtika.v2i1.67.

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Image retrieval initially uses a query in the form of text to search for images in the database. Image search using text query has a weakness because of the limited description of information stored or given by humans to the metadata on an inconsistent image that greatly affects the duration of searching an image in a database. Content based image retrieval (CBIR) is an image processing application to find the image sought in a large image database based on a query or user request. CBIR technique utilizes features that exist in images, namely color, texture, and shape. These features will be u

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Yakin, Syamsul, Tasrif Hasanuddin, and Nia Kurniati. "Application of content based image retrieval in digital image search system." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 1122–28. http://dx.doi.org/10.11591/eei.v10i2.2713.

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Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage

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Karthik, Durga, Vijayarekha K, and Surya K. "CHARACTERISATION OF TABLETS FOR DEFECT IDENTIFICATION USING IMAGE PROCESSING TECHNIQUES FOR QUALITY CONTROL IN PHARMACEUTICAL INDUSTRY." Asian Journal of Pharmaceutical and Clinical Research 10, no. 11 (2017): 270. http://dx.doi.org/10.22159/ajpcr.2017.v10i11.20033.

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Objective: Our aim is to characterize the parameters for identifying defective tablets from the manufacturing line using image processing techniques.Methods: Manufactured tablets might have defects such as broken chips, missing tablet, and color variation. Images of tablets are captured using machine vision camera. The features are detected using feature extraction for a tablet without defects and are stored in a database. The stored details are used for identifying defective tablets during manufacturing.Results: The characteristics such as color, shape, number of pills, area, and perimeter of

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Das, M., and E. M. Riseman. "FOCUS: A system for searching for multi-colored objects in a diverse image database." Computer Vision and Image Understanding 94, no. 1-3 (2004): 168–92. http://dx.doi.org/10.1016/j.cviu.2003.10.018.

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Tiwari, Priyesh, Shivendra Nath Sharan, Kulwant Singh, and Suraj Kamya. "Content Based Image Retrieval using Multi-level 3D Color Texture and Low Level Color Features with Neural Network Based Classification System." International Journal of Circuits, Systems and Signal Processing 15 (April 8, 2021): 265–70. http://dx.doi.org/10.46300/9106.2021.15.30.

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Content based image retrieval (CBIR), is an application of real-world computer vision domain where from a query image, similar images are searched from the database. The research presented in this paper aims to find out best features and classification model for optimum results for CBIR system.Five different set of feature combinations in two different color domains (i.e., RGB & HSV) are compared and evaluated using Neural Network Classifier, where best results obtained are 88.2% in terms of classifier accuracy. Color moments feature used comprises of: Mean, Standard Deviation,Kurtosis and

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Bhatta, Janardan. "Large-scale image search with text for information retrieval." Journal of Innovations in Engineering Education 4, no. 1 (2021): 87–89. http://dx.doi.org/10.3126/jiee.v4i1.35390.

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Searching images in a large database is a major requirement in Information Retrieval Systems. Expecting image search results based on a text query is a challenging task. In this paper, we leverage the power of Computer Vision and Natural Language Processing in Distributed Machines to lower the latency of search results. Image pixel features are computed based on contrastive loss function for image search. Text features are computed based on the Attention Mechanism for text search. These features are aligned together preserving the information in each text and image feature. Previously, the app

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Chen, Min, and Simone A. Ludwig. "Color Image Segmentation Using Fuzzy C-Regression Model." Advances in Fuzzy Systems 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/4582948.

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Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much mo

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Joshi, Shilpa, and P. T. Karule. "Detection of Hard Exudates Based on Morphological Feature Extraction." Biomedical and Pharmacology Journal 11, no. 1 (2018): 215–25. http://dx.doi.org/10.13005/bpj/1366.

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In diabetic patients, the chances of vision loss are higher. These issues related to vision can be diagnosed using diabetic retinopathy. It is one of the very important diseases amongst all retinal pathologies. One of the simplest changes observed on the eye due to diabetes is lesions in yellow or white color i.e. hard exudates (EX). It appears bright in fundus images and hence it is the most important to detect using image processing algorithm. In this work the proposed algorithm used is based on morphological feature extraction. Post processing techniques are required to separate out EX from

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Mai, Nicole Tham Ley, Syahmi Syahiran Bin Ahmad Ridzuan, and Zaid Bin Omar. "Content-based Image Retrieval System for an Image Gallery Search Application." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1903. http://dx.doi.org/10.11591/ijece.v8i3.pp1903-1912.

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Content-based image retrieval is a process framework that applies computer vision techniques for searching and managing large image collections more efficiently. With the growth of large digital image collections triggered by rapid advances in electronic storage capacity and computing power, there is a growing need for devices and computer systems to support efficient browsing, searching, and retrieval for image collections. Hence, the aim of this project is to develop a content-based image retrieval system that can be implemented in an image gallery desktop application to allow efficient brow

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Heravi, Hamed, Roghaieh Aghaeifard, Ali Rahimpour Jounghani, Afshin Ebrahimi, and Masumeh Delgarmi. "EXTRACTING FEATURES OF THE HUMAN FACE FROM RGB-D IMAGES TO PLAN FACIAL SURGERIES." Biomedical Engineering: Applications, Basis and Communications 32, no. 06 (2020): 2050042. http://dx.doi.org/10.4015/s1016237220500428.

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Biometric identification of the human face is a pervasive subject which deals with a wide range of disciplines such as image processing, computer vision, pattern recognition, artificial intelligence, and cognitive psychology. Extracting key face points for developing software and commercial devices of face surgery analysis is one of the most challenging fields in computer image and vision processing. Many studies have developed a variety of techniques to extract facial features from color and gray images. In recent years, using depth information has opened up new approaches to researchers in t

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Zhao, Jiandong, Hongqiang Wu, and Liangliang Chen. "Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing." Journal of Advanced Transportation 2017 (2017): 1–21. http://dx.doi.org/10.1155/2017/6458495.

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Adverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are categorized into 5 types including dry, wet, snow, ice, and water. Then, according to the original image size, images are segmented; 9-dimensional color eigenvectors and 4 texture eigenvectors are extracted to construct road surface state characteristics database. Next, a recognition method of road

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Kasimov, D. R. "Searching and describing objects in satellite images on the basis of modeling reasoning." Computer Optics 44, no. 5 (2020): 772–81. http://dx.doi.org/10.18287/2412-6179-co-716.

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The article presents an approach to a problem of contextual search and description of objects in raster satellite images, which consists in modeling reasoning on the basis of structured cases. As a result of image processing, an adjacency graph of color regions is constructed. The object is characterized by color, attributes of the form of segments of the border and the shape of the object as a whole. A structured case is represented in the form of a beam graph, whose arcs are ordered according to a positive bypass of the region boundaries. Using a graph matching algorithm, occurrences of case

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Yang, Bo, Bang Gui He, and Yu Chen. "Research on Online Visual Inspection System for Multi-Feature Trademark Defect." Applied Mechanics and Materials 397-400 (September 2013): 2322–25. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2322.

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Modern printing industry detective techniques, which have transferred from traditional single discrete manual operation into line detection based on machine vision. The line detection research under the highly automated process requirements, which is based on color graphics, gravure text, high-speed stamping and laser hologram hot stamping technology, is the multi-feature defect detection and recognition. By CDD study linear measurement techniques and computer vision technology, the research built the visual inspection platform, and design detection scheme combined with the specific requiremen

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Fan, Hui, Meng Han, and Jinjiang Li. "Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks." Applied Sciences 9, no. 5 (2019): 1009. http://dx.doi.org/10.3390/app9051009.

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Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level semantic information such as materials. An end-to-end deep convolutional neural network is proposed to further improve the image shadow removal effect. The network mainly consists of two network models, an encoder–decoder network and a small refinement network. The former predict

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Sarı, Cihan, Albert Ali Salah, and Alkım Almıla Akdag Salah. "Automatic detection and visualization of garment color in Western portrait paintings." Digital Scholarship in the Humanities 34, Supplement_1 (2019): i156—i171. http://dx.doi.org/10.1093/llc/fqz055.

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Abstract Paintings give us important clues about how males and females were perceived over centuries in the Western culture. In this article, we describe a system that allows scholars to automatically visualize how the clothing colors of male and female subjects changed over time. Our system analyzes a large database of paintings, locates portraits, automatically classifies each portrait’s subject as either male or female, segments the clothing areas and finds their dominant color. An interactive, web-based visualization is proposed to allow further exploration of the results. To test the accu

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Linda, G. Merlin, G. Themozhi, and Sudheer Reddy Bandi. "Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 01 (2019): 1941012. http://dx.doi.org/10.1142/s0219691319410121.

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In recent decades, gait recognition has garnered a lot of attention from the researchers in the IT era. Gait recognition signifies verifying or identifying the individuals by their walking style. Gait supports in surveillance system by identifying people when they are at a distance from the camera and can be used in numerous computer vision and surveillance applications. This paper proposes a stupendous Color-mapped Contour Gait Image (CCGI) for varying factors of Cross-View Gait Recognition (CVGR). The first contour in each gait image sequence is extracted using a Combination of Receptive Fie

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Adjabi, Insaf, Abdeldjalil Ouahabi, Amir Benzaoui, and Sébastien Jacques. "Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition." Sensors 21, no. 3 (2021): 728. http://dx.doi.org/10.3390/s21030728.

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Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF m

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Sun, Lilei, Huijie Sun, Junqian Wang, Shuai Wu, Yong Zhao, and Yong Xu. "Breast Mass Detection in Mammography Based on Image Template Matching and CNN." Sensors 21, no. 8 (2021): 2855. http://dx.doi.org/10.3390/s21082855.

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In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions ar

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"Feature Fusion for Image Retrieval using Image Processing." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (2019): 3157–62. http://dx.doi.org/10.35940/ijitee.l2646.1081219.

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Image processing and computer vision uses Content-based image retrieval (CBIR) function to solve the issue of image retrieval, which means, solving the issue of image searching in expansive databases. The actual data of the image will be evaluated when a search is performed that refers to content-based. The term content can be any attribute of an image like colour-shade, various symbols or shapes, sizes, or any other data. There are various approaches for image retrieval but the most prominent are by comparing the main image with the subsets of the relatable images whether it matches or not an

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"Semantic Image Annotation using Ontology And SPARQL." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (2020): 3363–68. http://dx.doi.org/10.35940/ijitee.h7062.019320.

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Based on user’s interest or requirements, the search and retrieve images from large scale the databases, the contentbased image retrieval (CBIR) technique has become the primary emerging area in research for digital image processing which makes the visual contents to use. Most promising tools for image searching are Google Images and Yahoo Image search. They are used for annotations based on textual of the images. In this, the images are annotated manually with the help of keywords and then the retrieval is carried by using various search methods based on text. Due to this method, the system p

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Claro, Maíla De Lima, Leonardo De Moura Santos, Wallinson Lima e Silva, Flávio Henrique Duarte De Araújo, Nayara Holanda De Moura, and André Macedo Santana. "Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction." CLEI Electronic Journal, August 1, 2016. http://dx.doi.org/10.19153/cleiej.19.2.4.

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The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images

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"Raspberry Pi Based Home Surveillance System using SMTP." International Journal of Recent Technology and Engineering 9, no. 1 (2020): 2792–94. http://dx.doi.org/10.35940/ijrte.a3036.059120.

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Different Technologies are emerging in the field of Home Surveillance now a days. Surveillance systems are being used to reduce man power and to increase security of a home. Technologies like Computer Vision and Internet of Things (IOT) are one of them. In this project a surveillance system has been implemented employing a single board computer i.e. Raspberry Pi 3 which will act like a central processing unit with the help of python language and a module named as Open Source Computer Vision(Open CV).To make it more automated a local database of authorized persons has been made. It will store t

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"Improved Technique to Diagnose Skin Cancer using Advanced Image Processing and Machine Learning Techniques." International Journal of Innovative Technology and Exploring Engineering 8, no. 9S (2019): 10–17. http://dx.doi.org/10.35940/ijitee.i1002.0789s19.

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Skin cancer is being classified among the mortal and exaggerating forms of cancer since decade. Notwithstanding, the early diagnosis of skin cancer is very important and it is an extravagant procedure. The presence of human skin is tough to examine and to model. This is because of its complex surface. The difficulty of the irregular edge, tone, appearance of thick hair and other alleviating features generate the skin tough to be analyzed. Human skin has some non-identical sorts of textures that diseased skin can characterize between the textures of the healthy one. In consequence, considerable

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Singh, Swati, Sheifali Gupta, Ankush Tanta, and Rupesh Gupta. "Extraction of Multiple Diseases in Apple Leaf Using Machine Learning." International Journal of Image and Graphics, February 24, 2021, 2140009. http://dx.doi.org/10.1142/s021946782140009x.

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This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular m

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