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Journal articles on the topic 'Image Processing and Feature Extraction'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Hema, Dr A., and R. Saravanakumar. "A Survey on Feature Extraction Technique in Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 448–51. http://dx.doi.org/10.31142/ijtsrd12937.

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Zou, Ji, Chao Zhang, Zhongjing Ma, Lei Yu, Kaiwen Sun, and Tengfei Liu. "Image Feature Analysis and Dynamic Measurement of Plantar Pressure Based on Fusion Feature Extraction." Traitement du Signal 38, no. 6 (2021): 1829–35. http://dx.doi.org/10.18280/ts.380627.

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Footprint recognition and parameter measurement are widely used in fields like medicine, sports, and criminal investigation. Some results have been achieved in the analysis of plantar pressure image features based on image processing. But the common algorithms of image feature extraction often depend on computer processing power and massive datasets. Focusing on the auxiliary diagnosis and treatment of foot rehabilitation of foot laceration patients, this paper explores the image feature analysis and dynamic measurement of plantar pressure based on fusion feature extraction. Firstly, the autho

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Wei, Zhenfeng, and Xiaohua Zhang. "Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing." Traitement du Signal 38, no. 1 (2021): 181–90. http://dx.doi.org/10.18280/ts.380119.

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The new retail is an industry featured by online ecommerce. One of the key techniques of the industry is the product identification based on image processing. This technique has an important business application value, because it is capable of improving the retrieval efficiency of products and the level of information supervision. To acquire high-level semantics of images and enhance the retrieval effect of products, this paper explores the feature extraction and retrieval of ecommerce product images based on image processing. The improved Fourier descriptor was innovatively into a metric lear

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Zhang, Tao, Zhipeng Li, Myungsoo Shin, Chunxia Wang, Wenli Song, and Lei Lui. "Feature Extraction Method of Snowboard Starting Action Using Vision Sensor Image Processing." Mobile Information Systems 2022 (January 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/2829547.

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There is a lot of noise in the snowboard starting action image, which leads to the low accuracy of snowboard starting action feature extraction. We propose a snowboard starting action feature extraction using visual sensor image processing. Firstly, the overlapping images are separated by laser fringe technology. After separation, the middle point of the image is taken as the feature point, and the interference factors are filtered by laser. Secondly, the three-dimensional model is established by using visual sensing image technology, the action feature images are input in the order of recogni

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Ismaila, Folasade. M., O. Adeolu Afolabi, W. Oladimeji Ismaila, and Oluwaseun O. Alo. "Performance Evaluation of Selected Feature Extraction Techniques in Digital Face Image Processing." Performance Evaluation of Selected Feature Extraction Techniques in Digital Face Image Processing 9, no. 1 (2024): 9. https://doi.org/10.5281/zenodo.10670429.

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Digital image processing is the use of computer algorithms to analyze digital images. Digital image processing, involves many processing stages of which feature extraction stage is important. Feature extraction involves reducing the number of resources required to describe a large set of data. However, choosing a feature extraction techniques is a problem because of their deficiencies. Thus, this paper presents a comparative performance analysis of selected feature extraction techniques in human face images. 90 face images were acquired with three different poses viz: normal, angry and laughin

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APOSTOLESCU, Nicolae, and Dragos-Daniel ION-GUTA. "Image processing for feature detection and extraction." INCAS BULLETIN 16, no. 3 (2024): 3–18. http://dx.doi.org/10.13111/2066-8201.2024.16.3.1.

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The present paper aims to conduct an experiment that compares different methods of detecting objects in images. Programs were developed to evaluate the efficiency of SURF, BRISK, MSER, and ORB object detection methods. Four static gray images with sufficiently different histograms were used. The experiment also highlighted the need for image preprocessing to improve feature extraction and detection. Thus, a programmed method for adjusting pixel groups was developed. This method proved useful when one of the listed algorithms failed to detect the object in the original image, but succeeded afte

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Wang, Jucui, Mingzhi Li, Anton Dziatkovskii, Uladzimir Hryneuski, and Aleksandra Krylova. "Research on contour feature extraction method of multiple sports images based on nonlinear mechanics." Nonlinear Engineering 11, no. 1 (2022): 347–54. http://dx.doi.org/10.1515/nleng-2022-0037.

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Abstract This article solves the issue of long extraction time and low extraction accuracy in traditional moving image contour feature extraction methods. Here authors have explored deformable active contour model to research the image processing technology in scientific research and the application of multiple sports and the method. A B-spline active contour model based on dynamic programming method is proposed in this article. This article proposes a method of using it to face image processing and extracting computed tomography (CT) image data to establish a three-dimensional model. The Lyap

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Vega-Rodriguez, M. A. "Review: Feature Extraction and Image Processing." Computer Journal 47, no. 2 (2004): 271–72. http://dx.doi.org/10.1093/comjnl/47.2.271-a.

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Li, Zhe, Xiao Han, Liya Wang, Tongyi Zhu, and Futian Yuan. "Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing." Traitement du Signal 37, no. 6 (2020): 1009–18. http://dx.doi.org/10.18280/ts.370613.

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Facing the existing digital image libraries on landscape, researchers need to urgently solve a challenging problem: how to realize rational management and accurate retrieval of landscape images that contain feature information like hierarchy, layout, color system, and color matching. For accurate organization and labeling of landscape Images, this paper presents a novel method for feature extraction and image retrieval of landscape images based on image processing. Firstly, a color quantization process was designed for landscape images, and used to analyze the color composition and color space

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Rogayah, Rogayah, Waliya Rahmawanti, and Nur Azizah. "Colour-Based Extraction Methods for the Classification of Breast Milk (ASI)." CCIT Journal 14, no. 1 (2021): 21–27. http://dx.doi.org/10.33050/ccit.v14i1.966.

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The development of cellular devices makes accessing information in the form of text or images more easier. In line with the growing field of computer vision, various processes in image/image processing continue to increase. Image processing can be done by increasing image quality (image enhancement) and image recovery (image restoration). Feature extraction is divided into three types, namely feature form extraction, texture feature extraction, and color feature extraction. The application of color-based feature extraction methods has been widely used by researchers in the process of classific

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Dasari, Saritha, A. Rama Mohana Reddy, and B. Eswara Reddy. "Object identification using Interleaved feature extraction model." Computer Science, Engineering and Technology 2, no. 1 (2024): 41–47. http://dx.doi.org/10.46632/cset/2/1/6.

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Image mining spanning the system of image applications has proved to be of good use in most industrial image processing applications. It supports a large field of applications like medical diagnosis, agriculture, industrial work, space research, and the educational field. Image Mining involves extracting information as well as image detection and extracting the image segment. It is often seen that these steps are considered in isolation leading to completely independent flow of process. This paper overlaps the steps leading of feature extraction and object recognition to provide better results

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Song, Tianming, Xiaoyang Yu, Shuang Yu, Zhe Ren, and Yawei Qu. "Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm." Complexity 2021 (October 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/7523513.

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Medical image technology is becoming more and more important in the medical field. It not only provides important information about internal organs of the body for clinical analysis and medical treatment but also assists doctors in diagnosing and treating various diseases. However, in the process of medical image feature extraction, there are some problems, such as inconspicuous feature extraction and low feature preparation rate. Combined with the learning idea of convolution neural network, the image multifeature vectors are quantized in a deeper level, which makes the image features further

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Wen, Zhe, Qian Dong, Jie Zhu, and Ya Bin Fan. "Research on the Feature Parameter Extraction of Wheat Seeds’ Bad Point Based on Image Processing." Applied Mechanics and Materials 644-650 (September 2014): 4140–43. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4140.

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It is very important that study the feature parameter extraction of bad point of wheat seeds based on image processing for judging the quality of wheat. Using image processing extract and analyze the collected images information, and based on the collected information analyze the bad point information of wheat seed, then extract the feature parameters. Traditional bad point’s feature extraction methods are completed by the manual operation, and the efficient is lower. Currently, by means of image processing technology can extract the bad point’s feature of wheat seed automatically. To this end

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Mohan, Dr J., K. Rakesh, B. Raviteja, and N. Santhosh kumar. "Medical Image Enhancement using Histogram Processing and Feature Extraction of Cancer Classification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40445.

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Magnetic Resonance Imaging (MRI) is a critical tool in medical diagnostics, particularly for cancer detection and classification. However, the quality of MRI images often suffers from noise, low contrast, and intensity inhomogeneity, which can hinder accurate diagnosis. Image enhancement techniques, such as histogram processing, play a crucial role in improving image quality by enhancing contrast and emphasizing important fea- tures. This paper explores the application of histogram processing for MRI image enhancement, focusing on contrast enhancement and noise reduction techniques. The enhanc

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ganesh , N. Bharath. "Plant Disease Detection by Image Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40956.

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Plant disease detection using image processing is a crucial step in ensuring agricultural productivity. This approach leverages techniques such as image prepossessing, feature extraction, and machine learning algorithms to identify and classify diseases from leaf images. By enabling early and accurate detection, the system helps reduce crop loss and supports sustainable farming practices. Key Words: Plant Disease Detection,Image Processing, Machine Learning,Deep Learning,Convolutional Neural Networks (CNN),Image Segmentation,Feature Extraction,Classification Algorithms,Plant Health Monitoring,

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Y, Sun. "Research on Rolling Bearing Fault Feature Extraction and Diagnosis Method Based on Image Processing." Physical Science & Biophysics Journal 5, no. 2 (2021): 1–7. http://dx.doi.org/10.23880/psbj-16000184.

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In economic construction, there are many large and important machinery and equipment. Some equipment will continue to work in a harsh working environment, so many and various failures will occur. Rolling bearings are one of the widely used parts in rotating machinery. They are generally composed of inner ring, outer ring, rolling element and holding. The frame is composed of four parts, the failure of the bearing is particularly important, and its safe operation has a vital impact on the entire equipment, Feature extraction is the key link in the subsequent identification of fault types, Altho

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Angel, Jisha .M Jean, and Shivkhumar Vijayalakshmi. "Optical Character Recognition from Images." International Journal of Engineering and Management Research 14, no. 2 (2024): 160–64. https://doi.org/10.5281/zenodo.11171827.

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Analysis of document images for information extraction has become very prominent in recent past. Wide variety of information, which has been conventionally stored on paper, is now being converted into electronic form for better storage and intelligent processing. This needs processing of documents using image analysis, processing methods. This article provides an overview of various methods used for digital image processing using three main components: Pre-processing, Feature extraction and the Classification. Pre-processing feature extraction and classification. Classification is an important

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Liu, Xiaowen, and Juncheng Lei. "Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing." Journal of Physics: Conference Series 2083, no. 4 (2021): 042007. http://dx.doi.org/10.1088/1742-6596/2083/4/042007.

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Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space thr

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Shi, Chun Hua. "Image processing and feature extraction of microscopic." MATEC Web of Conferences 44 (2016): 01086. http://dx.doi.org/10.1051/matecconf/20164401086.

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Bai, Shizhen, and Fuli Han. "Tourist Behavior Recognition Through Scenic Spot Image Retrieval Based on Image Processing." Traitement du Signal 37, no. 4 (2020): 619–26. http://dx.doi.org/10.18280/ts.370410.

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The monitoring of tourist behaviors, coupled with the recognition of scenic spots, greatly improves the quality and safety of travel. The visual information is the underlying features of scenic spot images, but the semantics of the information have not been satisfactorily classified or described. Based on image processing technologies, this paper presents a novel method for scenic spot retrieval and tourist behavior recognition. Firstly, the framework of scenic spot image retrieval was constructed, followed by a detailed introduction to the extraction of scale invariant feature transform (SIFT

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Niharika, A., and Prasanna Kumar S.C Dr. "A Study on Deep Learning in Bio-Medical Image Processing." Journal of Advanced Research in Artificial Intelligence & It's Applications 1, no. 3 (2024): 65–77. https://doi.org/10.5281/zenodo.13309547.

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<em>The goal of image fusion is to create a single image that is more instructive and useful for later applications by first extracting and then merging the most significant information from several source photos. Image fusion has advanced significantly as a result of deep learning, and the fused results are promising due to neural networks' strong feature extraction and reconstruction capabilities. Recent advances in deep learning technologies have led to a boom in picture fusion. But there isn't a thorough examination and critique of the most recent deep learning techniques in various fusion

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Dong, Lei, Niangang Jiao, Tingtao Zhang, Fangjian Liu, and Hongjian You. "GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images." Applied Sciences 14, no. 4 (2024): 1528. http://dx.doi.org/10.3390/app14041528.

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This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges when applied to satellite SAR images due to the presence of speckle noise, leading to increased matching errors. The SAR–SIFT method is explored and analyzed in-depth, considering the unique characteristics of satellite SAR images. To enhance the efficiency of matching id

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Huang, Chuan Bo, and Zi Ping Zhou. "Texture Feature Extraction for Color Images Based on Quaternion Representation." Advanced Materials Research 712-715 (June 2013): 2336–40. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2336.

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In order to get texture features of color image, we proposed a new algorithm which is adapted to extract the texture features for color images in this paper. Firstly, the proposed method adopt quaternion to color image processing that can represent the color image in a holistic manner and parallel processing the R, G and B components. Secondly, we can obtain the directional information and texture features by Quaternion Gabor Filter. The experimental results show that texture feature obtained by our method has good the discrimination power and classifing performance.

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Wang, Yingnan, Yueming Yang, and Peiye Zhang. "Gesture Feature Extraction and Recognition Based on Image Processing." Traitement du Signal 37, no. 5 (2020): 873–80. http://dx.doi.org/10.18280/ts.370521.

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Gesture recognition has become increasingly popular, in response to the growing demand for intelligent and personalized human-computer interaction (HCI) and human-to-human interaction. However, gesture recognition raises a high requirement on the background color of the gesture image, and faces difficulty in extracting multiple gesture features. To solve these problems, this paper presents a novel approach for gesture feature extraction and recognition based on image processing. Firstly, the workflow of the proposed gesture recognition method was given, and a series of preprocessing was perfor

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Hsu, Wei-Yen, and Jing-Wen Lin. "High-Quality Text-to-Image Generation Using High-Detail Feature-Preserving Network." Applied Sciences 15, no. 2 (2025): 706. https://doi.org/10.3390/app15020706.

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Multistage text-to-image generation algorithms have shown remarkable success. However, the images produced often lack detail and suffer from feature loss. This is because these methods mainly focus on extracting features from images and text, using only conventional residual blocks for post-extraction feature processing. This results in the loss of features, greatly reducing the quality of the generated images and necessitating more resources for feature calculation, which will severely limit the use and application of optical devices such as cameras and smartphones. To address these issues, t

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Dr., A. Hema, and Saravanakumar R. "A Survey on Feature Extraction Technique in Image Processing." International Journal of Trend in Scientific Research and Development 2, no. 4 (2019): 448–51. https://doi.org/10.31142/ijtsrd12937.

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Image processing is a method to convert an image into digital form and perform some operations on it. It is a type of signal dispensation in which input is image, video frame or photograph and output may be image or characteristics associated with that image. Image processing basically include the nthree steps as importing the image with optical scanner or by digital photography. Analyzing and manipulating the image which includes data compression and image enhancement and spotting pattern that are not to human eyes like satellite photographs. Output is the last stage in which result can be al

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Lavanya, R., G. K. Rajini, and G. Vidhya Sagar. "Retinal vessel feature extraction from fundus image using image processing techniques." International Journal of Engineering & Technology 7, no. 2 (2018): 687. http://dx.doi.org/10.14419/ijet.v7i2.8892.

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Retinal Vessel detection for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy etc. This paper deals with image processing techniques for automatic analysis of blood vessel detection of fundus retinal image using MATLAB tool. This approach uses intensity information and local phase based enhancement filter techniques and morphological operators to provide better accuracy.Objective: The effect of diabetes on the eye is called Diabetic Retinopathy. At the early stages of the disease, blood

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Rao, P. Srinivas. "COUNTERFEIT CURRENCY DETECTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27717.

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This research addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN),and theefficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of dist

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Kishori, Patil, and Chobe Santosh. "Leaf Disease Detection using Deep Learning Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3172–75. https://doi.org/10.35940/ijeat.C5965.029320.

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India is a nation of agriculture and over 70 per cent of our population relies on farming. A portion of our national revenue comes from agriculture. Agriculturalists are facing loss due to various crop diseases and it becomes tedious for cultivators to monitor the crop regularly when the cultivated area is huge. So the plant disease detection is important in agriculture field. Timely and accurate disease detection is important for the loss caused due to crop diseases which affects adversely on crop quality and yield. Early diagnosis and intervention can reduce the loss of plant due to disease

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Ma, Guangmei. "Image key information processing using convolutional neural network and rotational invariant-hierarchical max pooling algorithm." PLOS One 20, no. 5 (2025): e0324504. https://doi.org/10.1371/journal.pone.0324504.

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In the information age, the effectiveness of image processing determines the quality of a large number of image analysis tasks. A fusion algorithm-based processing technique was proposed to process key image information. A feature dictionary was introduced as the matching template model and the standard model. The convolutional layer sampling feature block optimization was carried out using image segmentation ideas. The optimal threshold of the image to be segmented was obtained using the least squares method. The feature extraction layer was structurally supplemented and expressed at multiple

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Hu, Yanzhu, Song Wang, and Xinbo Ai. "Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method." Algorithms 11, no. 8 (2018): 117. http://dx.doi.org/10.3390/a11080117.

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This paper aims to improve the source tracking efficiency of distributed vibration signals generated by phase-sensitive optical time-domain reflectometry (Φ-OTDR). Considering the two dimensions (time and length) of Φ-OTDR signals, the authors saved and processed these signals as images after particle filtering. The filtering method could save 0.1% of hard drive space without sacrificing the original features of the signals. Then, an integrated feature extraction method was proposed to further process the generated image. The method combines three individual extraction methods, namely, texture

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B.A. Mohammed and Z.M. Abood. "Performance Evolution Ear Biometrics Based on Features from Accelerated Segment Test." Mustansiriyah Journal of Pure and Applied Sciences 1, no. 3 (2023): 71–84. http://dx.doi.org/10.47831/mjpas.v1i3.49.

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In the field of image processing, feature extraction is very important. Various image pre-processing procedures, including scaling, downscaling, resizing, normalization, etc., are applied to the sampled image before features are acquired. Features that would be relevant for image classification and recognition are then extracted using feature extraction methods. Many problems arose in biometric methods (fingerprint, iris, face), which led to the search for new biometrics to identify a person, avoid disease obstacles, continuous change with age, and others. The aim of this work is to present an

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Zheng, Wenfeng, Siyu Lu, Youshuai Yang, Zhengtong Yin, and Lirong Yin. "Lightweight transformer image feature extraction network." PeerJ Computer Science 10 (January 31, 2024): e1755. http://dx.doi.org/10.7717/peerj-cs.1755.

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In recent years, the image feature extraction method based on Transformer has become a research hotspot. However, when using Transformer for image feature extraction, the model’s complexity increases quadratically with the number of tokens entered. The quadratic complexity prevents vision transformer-based backbone networks from modelling high-resolution images and is computationally expensive. To address this issue, this study proposes two approaches to speed up Transformer models. Firstly, the self-attention mechanism’s quadratic complexity is reduced to linear, enhancing the model’s interna

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Abdulhamid, Mohanad, and Gitonga Muthomi. "Study of Feature Extraction of Retinal Scans." Scientific Bulletin 24, no. 1 (2019): 5–13. http://dx.doi.org/10.2478/bsaft-2019-0001.

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Abstract In this paper, the retina is discussed as part of the feature of extraction of retinal scans for use in security systems as a means of identification. The design system contains a method of image acquisition and processing of the image. A computer system is also incorporated for matching and verifying the image captured to an already present representation of unique features of the retina that are stored as templates for matching and identification. It should then either allow or deny the user depending on the results of the matching process. This paper shows the development of the st

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Kainat, Jaweria, Syed Sajid Ullah, Fahd S. Alharithi, Roobaea Alroobaea, Saddam Hussain, and Shah Nazir. "Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques." Complexity 2021 (December 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/9736179.

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Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. Durin

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Ma, Jingxuan. "Progress and Application of Unsupervised Feature Extraction Methods." Applied and Computational Engineering 106, no. 1 (2024): 99–104. http://dx.doi.org/10.54254/2755-2721/106/20241313.

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Unsupervised feature extraction is crucial in machine learning and data mining for handling high-dimensional and unlabeled data. However, existing methods often ignore feature relationaships, resulting in suboptimal feature subsets. This paper reviews the current state of unsupervised feature extraction methods, discussing the limitations of traditional methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), particularly in terms of interpretability, sensitivity to outliers, and computational resource challenges. In recent years, improvement strategies such

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Reuben, B., and K. Ambujam. "Neural network approach to detect renal calculi." i-manager’s Journal on Image Processing 10, no. 4 (2023): 31. http://dx.doi.org/10.26634/jip.10.4.20329.

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Back Propagation Network is the most commonly used algorithm in training neural networks. It is employed in processing the images and data to implement an automated kidney stone classification. The conventional technique for classifying medical resonance kidney images and detecting stones relies on human examination. This method is not accurate since it is impractical to handle large amount of data. Magnetic Resonance (MR) Images may inherently possess noise caused by operator errors. This causes earnest inaccuracies in classification features and diseases in image processing. However, the usa

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Jaiswal, Rachana, and Srikant Satarkar. "Role of Hybrid Level Set in Fetal Contour Extraction." Signal & Image Processing : An International Journal 12, no. 1 (2021): 39–52. http://dx.doi.org/10.5121/sipij.2021.12104.

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Image processing technologies may be employed for quicker and accurate diagnosis in analysis and feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements due to some problems in traditional approach such as lack of consistency and accuracy. The proposed approach utilizes global & local region information for fetal contour extractio

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Cheng, Peng, Jiang An Wang, Da Hui Qin, and Gui Yuan Mei. "The Underwater Bubbles Image’s Acquisition and Processing." Applied Mechanics and Materials 33 (October 2010): 152–56. http://dx.doi.org/10.4028/www.scientific.net/amm.33.152.

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Machine Vision was used to observer and measure the underwater bubbles. There are several ways been taken to solve many difficulties in the process of the acquisition to get the image. Such as the equipment selection, the light sources selection, the approach to images and the feature extraction of the image. The most important of them is the noise elimination and the extraction of the bubbles. The feature of the image can be very obviously after the Median filter and Sub-pixel edge detection. In this way, it provided an effective and feasible method to measure the underwater bubbles.

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Dena Nadir George, Haitham Salman Chyad, and Raniah Ali Mustafa. "Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques." Global Journal of Engineering and Technology Advances 6, no. 3 (2021): 056–62. http://dx.doi.org/10.30574/gjeta.2021.6.3.0031.

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Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and

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Dena, Nadir George, Salman Chyad Haitham, and Ali Mustafa Raniah. "Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques." Global Journal of Engineering and Technology Advances 6, no. 3 (2021): 056–62. https://doi.org/10.5281/zenodo.4643420.

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Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and

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Ren, Zhigang, Guoquan Ren, and Dinhai Wu. "Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR." Micromachines 13, no. 10 (2022): 1765. http://dx.doi.org/10.3390/mi13101765.

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Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing o

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MORE, MAHADEV A. "CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25835.

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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user appli

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Liu, Huan, and Chao Tao Liu. "Development of Cable Inspection System Based on Image Processing." Applied Mechanics and Materials 602-605 (August 2014): 2199–204. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2199.

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A stayed cable inspection system was developed which consists of robot, host computer, cameras and image acquisition system. The robot was driven with single motor and could climb cables of various and variable diameters. Pictures of the cables’ were taken by the robot, and the defects and mars were identified automatically with image recognition. The steps of image recognition includes image de-noising, image enhancement, image segmentation, feature extraction, and recognition with the features of the images’ histogram grayscale distributions and energy distributions.

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Chen, Mujun. "Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model." Computational Intelligence and Neuroscience 2022 (June 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/5156532.

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In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the current research frontiers of image processing and natural language processing, we find out the problem of the ineffectiveness of m-RNN for some image generation descriptions, starting from both the image feature extraction part and text sequence data processing. Unlike traditional image automatic processing algorithms, this algorithm does not need to

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Krishna, Nanditha, and K. Nagamani. "Understanding and Visualization of Different Feature Extraction Processes in Glaucoma Detection." Journal of Physics: Conference Series 2327, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2327/1/012023.

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Abstract In the recent years the usage of mobile phone is increased and it is the major reason for cause of vision loss in several people. The continuous usage increases pressure inside optic nerve head and it leads to glaucoma disease. Also, there are lot of other reasons which leads to the cause of glaucoma. The purpose of this paper is to determine the importance of feature extraction process in glaucoma detection and implementation of different techniques for extracting convenient features for training machine learning model using pre-processed OCT (Optical Coherence Tomography) images. Th

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Abdulhussain, Sadiq H., Basheera M. Mahmmod, Jan Flusser, Khaled A. AL-Utaibi, and Sadiq M. Sait. "Fast Overlapping Block Processing Algorithm for Feature Extraction." Symmetry 14, no. 4 (2022): 715. http://dx.doi.org/10.3390/sym14040715.

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In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. T

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Tangwannawit, Panana, and Sakchai Tangwannawit. "Feature extraction to predict quality of segregating sweet tamarind using image processing." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (2022): 339–46. https://doi.org/10.11591/ijeecs.v25.i1.pp339-346.

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In this modern age, several new methods have been developed, especially in image processing for agriculture business, which consists of technologies derived from artificial intelligence (AI) capabilities called machine learning. Classify is a widely used method to analyze patterns, trends, as well as the body of knowledge from the data visualization. Image classification application improves discrimination and prediction efficiency. The objective of this research was to feature extraction of sweet tamarind and compare the algorithm for classification. This research used images from golden swee

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P, Soumya Balan. "Survey on Feature Extraction Techniques in Image Processing." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (2018): 217–22. http://dx.doi.org/10.22214/ijraset.2018.3035.

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Zhang, Qian, Feng Yu, Xin Liu, and Jun Feng Zhang. "Tomato Disease Image Retrieval Based on Composite Features." Applied Mechanics and Materials 571-572 (June 2014): 777–80. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.777.

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A new method of tomato disease image retrieval was proposed, which based on the composite feature extraction of disease images. The feature was converted to a set of hash sequence. This retrieval method can reflect the image content in a better way for the comprehensive application of color, texture and shape features. The digital index of image through perception hash algorithm processing can retrieval images and return the result more rapidly.

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