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Journal articles on the topic 'Maximally Stable Extremal Regions (MSER) Feature Descriptor'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Fan, Xiao Hu, Wei Ting Lin, Juan Cao, Ben Ling Li, and Yan Si. "A Description Method for MSER with SIFT Descriptor." Applied Mechanics and Materials 127 (October 2011): 115–20. http://dx.doi.org/10.4028/www.scientific.net/amm.127.115.

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Maximally Stable Extremal Regions are robust to complex affine distortion and illumination changes between reference image and real-time image. On the basis of deeply research on the SIFT descriptor, this paper propose a description algorithm for MSER using SIFT descriptor. The Second central moment is used in the algorithm to make ellipse adjustment for each irregular MSER. Then a self-adaptable rectangle area, whose side is proportional to the minor axis of the ellipse, is constructed encircling each ellipse centre. Finally, a SIFT feature vector is formed to express the MSER, after processi

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Ajayi, Oluibukun Gbenga, Ifeanyi Jonathan Nwadialor, Ifeanyi Chukwudi Onuigbo, and Olurotimi Adebowale Kemiki. "PRELIMINARY INVESTIGATION OF THE ROBUSTNESS OF MAXIMALLY STABLE EXTREMAL REGIONS (MSER) MODEL FOR THE AUTOMATIC REGISTRATION OF OVERLAPPING IMAGES." Geoplanning: Journal of Geomatics and Planning 5, no. 1 (2018): 63. http://dx.doi.org/10.14710/geoplanning.5.1.63-74.

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Various researchers in Digital Image processing have developed keen interest in the automation of object detection, description and extraction process used for various applications and this has led to the development of series of Feature detection and extraction models one of which is the Maximally Stable Extremal Regions Feature Algorithm (MSER). This paper investigates the robustness of MSER algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. The robustnes

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Kok, Kai Yit, and Parvathy Rajendran. "A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking." Symmetry 13, no. 8 (2021): 1337. http://dx.doi.org/10.3390/sym13081337.

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Despite years of work, a robust, widely applicable generic “symmetry detector” that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a “edge” or “corner” detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimu

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Awad, Ali Ismail, and M. Hassaballah. "Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images." Applied Sciences 9, no. 22 (2019): 4914. http://dx.doi.org/10.3390/app9224914.

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Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust f

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Tayyab, Muhammad, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, et al. "A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier." Sensors 25, no. 2 (2025): 441. https://doi.org/10.3390/s25020441.

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This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were use

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Ikhlas, Ahmad Lone*1 &. Manmeen Kaur2. "IMPROVED CONVOLUTIONAL NEURAL NETWORK BASED SEGMENTATION AND DETECTIONOF SKIN CANCER FROM DERMOSCOPY IMAGES USING MSER DESCRIPTOR." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 9, no. 2 (2020): 80–90. https://doi.org/10.5281/zenodo.3692902.

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Segmentation of skin lesion from a dermoscopic images is a predominant footstep in computerized analysis approaches. Inaccurate skin lesion region segmentation could unfavorably impact the successive processing phases of anautomated skin cancer diagnosis system based on computer-aided because in these days, skin cancer is the most predominant forms of cancer diseases for descendant and light-skinned people.The most malignant type of skin cancer is “Basal Cell Carcinoma (BCC)”and in medical science, classification of BCC in earlier stage is a biggest issue for researchers. In the wa

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Alhalalmeh, Zainab R., Yasser M. Fouda, Muhammad A. Rushdi, and Moawwad El-Mikkawy. "Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching." Sustainability 15, no. 19 (2023): 14234. http://dx.doi.org/10.3390/su151914234.

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This research addressed the need to enhance template-matching performance in e-learning and automated assessments within Egypt’s evolving educational landscape, marked by the importance of e-learning during the COVID-19 pandemic. Despite the widespread adoption of e-learning, robust template-matching feedback mechanisms should still be developed for personalization, engagement, and learning outcomes. This study augmented the conventional best-buddies similarity (BBS) approach with four feature descriptors, Harris, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and

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Masiero, A., A. Guarnieri, A. Vettore, and F. Pirotti. "On the use of INS to improve Feature Matching." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 227–32. http://dx.doi.org/10.5194/isprsarchives-xl-1-227-2014.

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The continuous technological improvement of mobile devices opens the frontiers of Mobile Mapping systems to very compact systems, i.e. a smartphone or a tablet. This motivates the development of efficient 3D reconstruction techniques based on the sensors typically embedded in such devices, i.e. imaging sensors, GPS and Inertial Navigation System (INS). Such methods usually exploits photogrammetry techniques (structure from motion) to provide an estimation of the geometry of the scene. <br><br> Actually, 3D reconstruction techniques (e.g. structure from motion) rely on use of featur

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Cai, Huiwen, Xiaoyan Wang, Ming Xia, and Yangsheng Wang. "Entropy-Based Maximally Stable Extremal Regions for Robust Feature Detection." Mathematical Problems in Engineering 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/857210.

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Maximally stable extremal regions (MSER) is a state-of-the-art method in local feature detection. However, this method is sensitive to blurring because, in blurred images, the intensity values in region boundary will vary more slowly, and this will undermine the stability criterion that the MSER relies on. In this paper, we propose a method to improve MSER, making it more robust to image blurring. To find back the regions missed by MSER in the blurred image, we utilize the fact that the entropy of probability distribution function of intensity values increases rapidly when the local region exp

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Jiang, Xiao Cun, Xiao Liu, Tao Tang, Xiao Hu Fan, and Xiao Cui. "A Comparison of Two Typical Local Feature Matching Algorithm: SIFT and MSER." Applied Mechanics and Materials 687-691 (November 2014): 4119–22. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4119.

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Scale invariant feature transform matching algorithm and Maximally Stable Extremal Regions matching algorithm have been widely used because of their good performance. The two local feature matching algorithms were compared through numbers of experiments in this paper. The experiment results showed that SIFT is good at dealing with the image distortion from shooting distance difference and small shooting viewpoint deviation; MSER is good at handling the complicated affine distortion from big shooting viewpoint deviation. From the aspect of scene types, the performance of SIFT is good both to st

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Kumari, S. Sandhya, and K. Sandhya Rani. "Big Data Classification of Ultrasound Doppler Scan Images Using a Decision Tree Classifier Based on Maximally Stable Region Feature Points." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 8 (2022): 76–87. http://dx.doi.org/10.17762/ijritcc.v10i8.5679.

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The classification of ultrasound scan images is important in monitoring the development of prenatal and maternal structures. This paper proposes a big data classification system for ultrasound Doppler scan images that combines the residual of maximally stable extreme regions and speeded up robust features (SURF) with a decision tree classifier. The algorithm first preprocesses the ultrasound scan images before detecting the maximally stable extremal regions (MSER). A few essential regions are chosen from the MSER regions, along with the residual region that provides the best Region of Interest

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Kuang, Xianyan, Wenbin Fu, and Liu Yang. "Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests." International Journal of Online Engineering (iJOE) 14, no. 03 (2018): 34. http://dx.doi.org/10.3991/ijoe.v14i03.7925.

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Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. Ac

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Jeyapal, Akilandeswari, Jothi Ganesan, Sabeenian Royappan Savarimuthu, et al. "A Comparative Study of Feature Detection Techniques for Navigation of Visually Impaired Person in an Indoor Environment." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 21–26. http://dx.doi.org/10.1166/jctn.2020.8623.

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A development of automatic location identification and tracking system for visually impaired/ challenged person is a very challenging task in an indoor environment. In this paper, the comprehensive study of different feature detection and matching techniques namely, Minimum Eigenvalue (MinEigen) algorithm, Harris–Stephens (Harris) algorithm, Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Binary Robust Invariant Scalable Keypoints (BRISK) and Maximally Stable Extremal Regions (MSER) is presented. These algorithms are employed to detect and match the features o

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Anugrahita, Dwi, Rini Mayasari, and Susilawati Susilawati. "Analisis Kinerja Deteksi Algoritma FAST dan Algoritma MSER pada Citra Digital Berbasis Marker." JRST (Jurnal Riset Sains dan Teknologi) 5, no. 2 (2022): 59. http://dx.doi.org/10.30595/jrst.v5i2.7796.

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Aungmented Reality (AR) membutuhkan algoritma yang baik dan tahan terhadap gangguan-gangguan yang dapat terjadi saat proses deteksi marker seperti perubahan pencahayaan, perubahan rotasi marker, dan blur pada kamera. Algoritma Feature from Accelerated Segment Test (FAST) dan algoritma Maximally Stable Extremal Regions (MSER) merupakan algoritma yang kerap dipakai sebagai metode pendeteksi marker pada AR. Tujuan penelitian ini yaitu untuk menganalisis kinerja algoritma FAST dan algoritma MSER terhadap kemampuan dan kecepatannya untuk mendeteksi dan mengekstraksi fitur-fitur pada citra serta ket

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Petrovic, Vladimir, and Jelena Popovic-Bozovic. "A method for real-time memory efficient implementation of blob detection in large images." Serbian Journal of Electrical Engineering 14, no. 1 (2017): 67–84. http://dx.doi.org/10.2298/sjee1701067p.

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In this paper we propose a method for real-time blob detection in large images with low memory cost. The method is suitable for implementation on the specialized parallel hardware such as multi-core platforms, FPGA and ASIC. It uses parallelism to speed-up the blob detection. The input image is divided into blocks of equal sizes to which the maximally stable extremal regions (MSER) blob detector is applied in parallel. We propose the usage of multiresolution analysis for detection of large blobs which are not detected by processing the small blocks. This method can find its place in many appli

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Ajayi, O. G. "PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 559–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-559-2020.

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Abstract. Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (1

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Ahmed, Muhammad Waqas, Touseef Sadiq, Hameedur Rahman, et al. "MAPE-ViT: multimodal scene understanding with novel wavelet-augmented Vision Transformer." PeerJ Computer Science 11 (May 23, 2025): e2796. https://doi.org/10.7717/peerj-cs.2796.

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This article introduces Multimodal Adaptive Patch Embedding with Vision Transformer (MAPE-ViT), a novel approach for RGB-D scene classification that effectively addresses fundamental challenges of sensor misalignment, depth noise, and object boundary preservation. Our framework integrates maximally stable extremal regions (MSER) with wavelet coefficients to create comprehensive patch embedding that capture both local and global image features. These MSER-guided patches, incorporating original pixels and multi-scale wavelet information, serve as input to a Vision Transformer, which leverages it

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Sambyal, Nitigya, and Pawanesh Abrol. "Feature based Text Extraction System using Connected Component Method." International Journal of Synthetic Emotions 7, no. 1 (2016): 41–57. http://dx.doi.org/10.4018/ijse.2016010104.

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Text detection and segmentation system serves as important method for document analysis as it helps in many content based image analysis tasks. This research paper proposes a connected component technique for text extraction and character segmentation using maximally stable extremal regions (MSERs) for text line formation followed by connected components to determined separate characters. The system uses a cluster size of five which is selected by experimental evaluation for identifying characters. Sobel edge detector is used as it reduces the execution time but at the same time maintains qual

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Jeong, Heon, Goo-Rak Kwon, and Sang-Woong Lee. "Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing." Energies 13, no. 11 (2020): 2856. http://dx.doi.org/10.3390/en13112856.

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Several factors cause the output degradation of the photovoltaic (PV) module. The main affecting elements are the higher PV module temperature, the shaded cell, the shortened or conducting bypass diodes, and the soiled and degraded PV array. In this paper, we introduce an image processing technique that automatically identifies the module generating the hot spots in the solar module. In order to extract feature points, we used the maximally stable extremal regions (MSER) method, which derives the area of interest by using the inrange function, using the blue color of the PV module. We propose

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Pandey, Digvijay, and Subodh Wairya. "A Novel Algorithm to Detect and Transmit Human-Directed Signboard Image Text to Vehicle Using 5G-Enabled Wireless Networks." International Journal of Distributed Artificial Intelligence 14, no. 1 (2022): 1–11. http://dx.doi.org/10.4018/ijdai.291084.

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The emerging 5G telecommunication technology uses novel aspects to fulfill the challenges of high data rate, ultra-low latency, broad bandwidth with the best user experience for text detection in sign board and thereafter transmission of identified information to the vehicles. This is performed on the images which are amorphous in nature or containing scenarios which are random or that cannot be determined. Detecting and transmission of textsover 5G wireless network from the unstructured images aids in many of the additional applications like Optical Character Recognition (OCR) and 5G technolo

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Patil, Shivanand Μ., V. S. Malemath, Suman Muddapur, and Praveen M. Dhulavvagol. "Enhanced Text Detection in Natural Scenes using Advanced Machine Learning Techniques." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 22114–18. https://doi.org/10.48084/etasr.10029.

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Text detection in natural scenes remains a fundamental challenge in computer vision, impacting applications from mobile navigation to document digitization. Traditional methods struggle with varying text orientations, complex backgrounds, and inconsistent lighting, while recent deep-learning approaches face computational efficiency challenges. This paper presents a novel hybrid machine-learning framework that combines traditional computer vision with advanced machine learning to achieve robust text detection. The framework integrates optimized preprocessing techniques, feature extraction metho

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Akcay, O., and E. O. Avsar. "THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 575–78. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-575-2017.

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A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods t

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Li, Yang, Dongyan Huang, Jiangtao Qi, et al. "Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera." Sensors 20, no. 13 (2020): 3799. http://dx.doi.org/10.3390/s20133799.

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In this study, an image registration algorithm was applied to calculate the rotation angle of objects when matching images. Some commonly used image feature detection algorithms such as features from accelerated segment test (FAST), speeded up robust features (SURF) and maximally stable extremal regions (MSER) algorithms were chosen as feature extraction components. Comparing the running time and accuracy, the image registration algorithm based on SURF has better performance than the other algorithms. Accurately obtaining the roll angle is one of the key technologies to improve the positioning

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Fouad, Haidy S., and Hend A. Elsayed. "Evaluation of GAM Classifier Performance for Airplane Remote Sensing Images Based on SIFT Features." Journal of Image and Graphics 12, no. 2 (2024): 168–67. http://dx.doi.org/10.18178/joig.12.2.158-167.

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Remote sensing technology and its applications have attracted the attention of researchers. Background variation and the small objects in remote sensing images make the classification process a challenging task. In several domains, Generalized Additive Models (GAMs) have demonstrated their ability to capture nonlinear interactions between explanatory variables and a response variable. This research evaluates the GAM with Scale Invariant Feature Transform (SIFT) for airplane remote sensing image classification. SIFT is a widely used local feature detection algorithm that performs best under sca

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Baso, Budiman, and Nanik Suciati. "Temu Kembali Citra Tenun Nusa Tenggara Timur menggunakan Esktraksi Fitur yang Robust terhadap Perubahan Skala, Rotasi, dan Pencahayaan." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 2 (2020): 349. http://dx.doi.org/10.25126/jtiik.2020722002.

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<p class="Abstrak">Ragam motif pada tenun Nusa Tenggara Timur (NTT) seperti flora, fauna dan geometris menjadi suatu keunikan yang dapat membedakan daerah asal dan jenis dari tenun tersebut. Pada penelitian ini, sistem temu kembali citra berbasis isi atau <em>Content-Based Image Retrieval</em> (CBIR) diimplementasikan pada citra tenun NTT sehingga user dapat mencari citra tenun pada <em>database</em> menggunakan citra <em>query </em>berdasarkan fitur visual yang terkandung dalam citra. Seringkali citra <em>query</em> yang diinputkan <em&

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Shrivastav, Supriya, Vikas Jindal, and Rajesh Eswarawaka. "A Fusion Method for Detection and Classification of Diseases in Tomato Plants Using Swarm-based Deep Learning." International Journal of Experimental Research and Review 45, Spl Vol (2024): 135–52. https://doi.org/10.52756/ijerr.2024.v45spl.011.

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Precise identification and detection of ailments in tomato plants are essential for preserving crop vitality and optimizing agricultural productivity. This promotes the use of agricultural methods that can be maintained over time and decreases financial losses caused by plant diseases. Detecting and classifying diseases in tomato plants is critical for ensuring crop health and maximizing agricultural productivity. Utilizing advanced computer vision techniques for this purpose enhances precision in monitoring plant health, ultimately leading to more efficient and targeted agricultural intervent

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Berrached, Saliha, and Nasr-Eddine Berrached. "Fast eye centre localization using combined unsupervised technics." Serbian Journal of Electrical Engineering 21, no. 1 (2024): 91–111. http://dx.doi.org/10.2298/sjee2401091b.

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Eye movements offer precious information about persons? state. Video surveillance, marketing, driver fatigue as well as medical diagnosis assistance applications manage eye behavior. We propose a new method for efficiently detecting eye movement. In this paper, we combine circle eye model with eye feature method to improve the accuracy. A set of detectors estimate the eyes centers to increase the localization rate. As a pre-processing stage, the mean of the edges yields the center of the two eye regions. Image treatment operations reduce the ROI. A Circle Hough Transform (CHT) algorithm is ado

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Moradi, Saed, Denis Laurendeau, and Clement Gosselin. "Multiple Cylinder Extraction from Organized Point Clouds." Sensors 21, no. 22 (2021): 7630. http://dx.doi.org/10.3390/s21227630.

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Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample s

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Sukumaran, Asha, and Thomas Brindha. "Nature-inspired hybrid deep learning for race detection by face shape features." International Journal of Intelligent Computing and Cybernetics 13, no. 3 (2020): 365–88. http://dx.doi.org/10.1108/ijicc-03-2020-0020.

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PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection mo

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Liu, Xiaomin, Runqi Zhao, Jun-Bao Li, Jeng-Shyang Pan, and Huaqi Zhao. "A Point–Set–Domain Image Object Matching Method for Airborne Object Localization." Journal of Internet Technology 26, no. 3 (2025): 303–14. https://doi.org/10.70003/160792642025052603003.

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Image object localization is an important research direction in the development of intelligent autonomous control systems for unmanned aerial vehicles (UAVs). Major challenges remain, such as cross-view images, large-scale deformation, and multitemporal variation. We propose a point–set–domain matching method to locate objects. First, the property constraints of a point, including sparsity, repeatability, and distinguishability,are combined into a keypoint response used to optimize convolutional neural networks, creating keypoint detector and feature descriptor models. With these models, we ca

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Finn, Anthony, Stefan Peters, Pankaj Kumar, and Jim O’Hehir. "Automated Georectification, Mosaicking and 3D Point Cloud Generation Using UAV-Based Hyperspectral Imagery Observed by Line Scanner Imaging Sensors." Remote Sensing 15, no. 18 (2023): 4624. http://dx.doi.org/10.3390/rs15184624.

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Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectr

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Ngeljaratan, Luna, Elif Ecem Bas, and Mohamed A. Moustafa. "Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures." Sensors 24, no. 5 (2024): 1450. http://dx.doi.org/10.3390/s24051450.

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Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infra

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Tehsin, Samabia, and Sumaira Kausar. "A Text Tracking Method using Maximally Stable Extremal Regions and Speeded up Robust Features." Journal of Information Communication Technologies and Robotic Applications, December 30, 2021, 39–47. http://dx.doi.org/10.51239/jictra.v0i0.271.

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Content-based image retrieval is an active research area because of vast applications of image and video collections. Text embedded in video can provide significant contribution in identifying the contents of the multimedia data and facilitate the process of video indexing, retrieval and analysis. Text tracking is a vital part of text extraction process. It can speed up the text extraction process and also improves the text localization accuracy for videos. This paper proposes a novel approach for text tracking in videos. This method experimentally proposes Maximally Stable Extremal Regions (M

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Kumar, Deepak, and Ramandeep SIngh. "Intellectual Curve Scene Text Detection from natural images using MSER descriptor based Region Segmentation approach." International Journal of Sensors, Wireless Communications and Control 09 (August 7, 2019). http://dx.doi.org/10.2174/2210327909666190807092724.

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A novel method to detect the text region from the natural image using the discriminative deep feature of text regions is presented with deep learning concept in this manuscript. Curve text detection (CTD) from the natural image is generally based on two different tasks: learning of text data and text region detection. In the learning of text data, the goal is to train the system with a sample of letters and natural images, while, in text region detection, the aim is to confirm the detected regions are text region or not. The emphasis of this research is on the development of deep learning algo

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Yu, Dongxian, Jiatao Kang, Zaihui Cao, and Neha Jain. "The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter." Recent Advances in Computer Science and Communications 13 (August 23, 2019). http://dx.doi.org/10.2174/2213275912666190823112357.

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In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Ext

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"An Optimal MSER Descriptor Based Facial Expression Recognition System using Artificial Intelligence Method." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (2020): 3571–74. http://dx.doi.org/10.35940/ijitee.b7522.019320.

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In this Artificial intelligence based Facial emotion recognition system (AI_FERS) model, emotions of facial expressions through performing some predefined steps such as face acquisition, pre-processing of images, face detection, feature extraction & classification have recognized. In the pre-processing of the image phase include the approaches used for face detection is: Knowledge-based, Feature-based, Template-based, and Appearance-based approach. Binary image computation, Skin-color segmentation and morphological filtering, which includes the dilation of Binary images and Gray Images are

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Manni, Francesca, Marco Mamprin, Ronald Holthuizen, et al. "Multi-view 3D skin feature recognition and localization for patient tracking in spinal surgery applications." BioMedical Engineering OnLine 20, no. 1 (2021). http://dx.doi.org/10.1186/s12938-020-00843-7.

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Abstract Background Minimally invasive spine surgery is dependent on accurate navigation. Computer-assisted navigation is increasingly used in minimally invasive surgery (MIS), but current solutions require the use of reference markers in the surgical field for both patient and instruments tracking. Purpose To improve reliability and facilitate clinical workflow, this study proposes a new marker-free tracking framework based on skin feature recognition. Methods Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Feature (SURF) algorithms are applied for skin feature detection. The p

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ÇİLTAŞ, Yıldız, and Funda AKAR. "SURF ve MSER Kombinasyonu ile Kopya Taşı Sahteciliği Algılama." Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, June 17, 2022. http://dx.doi.org/10.28948/ngumuh.1075784.

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Because digital images may contain a variety of data, they are regarded as an important source for information sharing. Also, images are widely used as evidence in a variety of real-life cases. The rapid rise in popularity of digital photographs is due to the improvement of technologies. Several software programs have been developed in recent years to modify digital images, such as Photoshop and Corel Photo, however these programs are now being used extensively for forgery. Because of technological advancements, it is difficult for people to recognize faked images with their naked eyes Therefo

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Abbas, Mohd Azwan, Halim Setan, Zulkepli Majid, et al. "ADAPTION OF INVARIANT FEATURES IN IMAGE FOR POINT CLOUDS REGISTRATION." Jurnal Teknologi 75, no. 10 (2015). http://dx.doi.org/10.11113/jt.v75.5279.

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Currently, coarse registration methods for scanner are required heavy operator intervention either before or after scanning process. There also have an automatic registration method but only applicable to a limited class of objects (e.g. straight lines and flat surfaces). This study is devoted to a search of a computationally feasible automatic coarse registration method with a broad range of applicability. Nowadays, most laser scanner systems are supplied with a camera, such that the scanned data can also be photographed. The proposed approach will exploit the invariant features detected from

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"Automatic Speed-Limit Sign Detection and Recognition for Advanced Driver Assistance Systems." International Journal of Innovative Technology and Exploring Engineering 8, no. 9S2 (2019): 1–5. http://dx.doi.org/10.35940/ijitee.i1001.0789s219.

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In recent years, traffic accidents have become the major cause to injuries, deaths and property damages. One of the main reasons to such accidents is due to high speed of vehicles. In order to maintain proper speed limit and thus provide significant contribution to improve safety, we propose Speed Limit sign detection and recognition method which is one of the features of Advanced Driver Assistance System (ADAS). In this paper we propose two approaches, i.e., histogram oriented gradient feature with SVM classifier namely HOG-SVM and CNN based approach. In these approaches we first pre-process

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Lin, Xiaoyu, Shanxiong Chen, Fujia Zhao, and Xiaogang Qiu. "Radical-based extract and recognition networks for Oracle character recognition." International Journal on Document Analysis and Recognition (IJDAR), April 13, 2022. http://dx.doi.org/10.1007/s10032-021-00392-2.

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AbstractThe recognition of Oracle bone inscription (OBI) is one of the most fundamental aspect of OBI study. However, the complex glyph structure and many variants of OBI, which hinder the advancement of automatic recognition research. In order to solve these problems, this paper designs an Oracle radical extract and recognition framework(ORERF) based on deep learning. First, combining the maximally stable extremal regions(MSER) algorithm and self-defined post-processing algorithm to generate Oracle single radical data annotation; then, the generated Oracle radical-level annotation data set is

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Palaiahnakote, Shivakumara, Chandrahas Pavan Kumar, Pranjal Aggarwal, et al. "A New Genetic Algorithm‐Based Network for Text Localization in Degraded Social Media Images." IET Image Processing 19, no. 1 (2025). https://doi.org/10.1049/ipr2.70030.

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ABSTRACTThis paper presents a novel model for understanding social image content through text localization. For text localization, we explore maximally stable extremal regions (MSER) for detecting components that work by clustering pixels with similar properties. The output of component detection includes several non‐text components due to the degradations of social media images. To select the best components among many, we explore the genetic algorithm by convolving different kernels with components, which results in a feature matrix that is further fed to EfficientNet for choosing actual tex

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Waqas Ahmed, Muhammad, Abdulwahab Alazeb, Naif Al Mudawi, et al. "Perception of Natural Scenes: Objects Detection and Segmentations using Saliency Map with AlexNet." International Arab Journal of Information Technology 22, no. 3 (2025). https://doi.org/10.34028/iajit/22/3/4.

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Object detection and classification play a crucial role in accurately tracking objects in complex environments. In recent years, there has been a significant increase in interest among researchers towards object analysis, fueled by the necessity to address challenges and explore opportunities across diverse technological domains. This study introduces a methodologically novel method for image classification through a custom-designed architecture inspired by AlexNet, tailored to process feature vectors for improved pattern recognition. The methodology incorporates Density-Based Spatial Clusteri

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Umamageswari, A., S. Deepa, A. Bhagyalakshmi, A. Sangari, and K. Raja. "EmotionFusion: A unified ensemble R-CNN approach for advanced facial emotion analysis." Journal of Intelligent & Fuzzy Systems, September 13, 2023, 1–15. http://dx.doi.org/10.3233/jifs-233842.

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To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms’ ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthc

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