Relevant bibliographies by topics / Maximally Stable Extremal Regions (MSER) Feature Descriptor

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Academic literature 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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Journal articles on the topic "Maximally Stable Extremal Regions (MSER) Feature Descriptor"

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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Book chapters on the topic "Maximally Stable Extremal Regions (MSER) Feature Descriptor"

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Hore, Sirshendu, Sankhadeep Chatterjee, Shouvik Chakraborty, and Rahul Kumar Shaw. "Analysis of Different Feature Description Algorithm in object Recognition." In Advances in Multimedia and Interactive Technologies. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1025-3.ch004.

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Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SU
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Hore, Sirshendu, Sankhadeep Chatterjee, Shouvik Chakraborty, and Rahul Kumar Shaw. "Analysis of Different Feature Description Algorithm in object Recognition." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch023.

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Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SU
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Salahat, Ehab Najeh, and Murad Qasaimeh. "Recent Advances in Feature Extraction and Description Algorithms." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch002.

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Computer vision is one of the most active research fields in technology today. Giving machines the ability to see and comprehend the world at the speed of sight creates endless applications and opportunities. Feature detection and description algorithms are considered as the retina for machine vision. However, most of these algorithms are typically computationally intensive, which prevents them from achieving real-time performance. As such, embedded vision accelerators (FPGA, ASIC, etc.) can be targeted due to their inherent parallelizability. This chapter provides a comprehensive study on som
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Conference papers on the topic "Maximally Stable Extremal Regions (MSER) Feature Descriptor"

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Jamesh, Asma. "Drowsy Driver Detection using MSER Feature Detection and Binarization on MATLAB." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.31.

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Every year, 1.5 lakh people die in road mishaps in India. Among these accidents, 40% are due to ‘Drowsy or Sleep Driving’. According to several statistics, almost all commercial private drivers tend to drive continuously for 10 hours a day. Nearly all road accidents caused due to lack of sleep and drowsiness are highly hazardous and fatal. Drowsy Driver Detection Algorithm acquires real-time video and captures snapshots using an external Webcam. Using the Viola-Jones Algorithm, the face and the eyes of the driver are detected. The original RGB eye image is converted to a Gray image and then in
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