A pre-processing approach for efficient feature matching process in extreme illumination scenario

2012 ◽  
Author(s):  
Marizuana Mat Daud ◽  
Zulaikha Kadim ◽  
Shang Li Yuen ◽  
Hon Hock Woon ◽  
Ibrahima Faye ◽  
...  
Keyword(s):  
Feature Matching ◽  
Matching Process ◽  
Processing Approach

scholarly journals FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei
Keyword(s):  
Feature Matching ◽  
Field Of View ◽  
Image Feature ◽  
Feature Points ◽  
Matching Process ◽  
Feature Descriptors ◽  
Coordinate Mapping ◽  
Matching Performance ◽  
Perspective Model ◽  
Attitude Matrix

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Image Feature ◽  
Brute Force ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Average Precision ◽  
Detection Model ◽  
Matching Process

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

scholarly journals ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY

2016 ◽  
Vol XL-3/W4 ◽  
pp. 41-44
Author(s):  
S. Kerner ◽  
I. Kaufman ◽  
Y. Raizman
Keyword(s):  
Feature Extraction ◽  
Spatial Distribution ◽  
Aerial Photography ◽  
Image Matching ◽  
Feature Matching ◽  
Real Data ◽  
Matching Process ◽  
Feature Based ◽  
Extraction Stage

Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching. <br><br> In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage. <br><br> We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.

scholarly journals AN IMPROVED IMAGE MATCHING METHOD BASED ON SURF ALGORITHM

2018 ◽  
Vol XLII-3 ◽  
pp. 179-184 ◽  
Author(s):  
S. J. Chen ◽  
S. Z. Zheng ◽  
Z. G. Xu ◽  
C. C. Guo ◽  
X. L. Ma
Keyword(s):  
Remote Sensing ◽  
Information Entropy ◽  
Image Matching ◽  
Feature Matching ◽  
Operating Efficiency ◽  
Similarity Function ◽  
Entropy Theory ◽  
Matching Method ◽  
Matching Process ◽  
Invariant Transformation

Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

Graph-based visual odometry for VSLAM

2018 ◽  
Vol 45 (5) ◽  
pp. 679-687 ◽  
Author(s):  
Shaoyan Xu ◽  
Tao Wang ◽  
Congyan Lang ◽  
Songhe Feng ◽  
Yi Jin
Keyword(s):  
Design Methodology ◽  
Feature Matching ◽  
Graph Matching ◽  
Typical Feature ◽  
Mapping Algorithms ◽  
Content Type ◽  
Structure Information ◽  
Environmental Perturbations ◽  
Matching Process ◽  
Localization And Mapping

Purpose Typical feature-matching algorithms use only unary constraints on appearances to build correspondences where little structure information is used. Ignoring structure information makes them sensitive to various environmental perturbations. The purpose of this paper is to propose a novel graph-based method that aims to improve matching accuracy by fully exploiting the structure information. Design/methodology/approach Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner. Findings The authors compare it with several state-of-the-art visual simultaneous localization and mapping algorithms on three datasets. Experimental results reveal that the ORB-G algorithm provides more accurate and robust trajectories in general. Originality/value Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.

Using Mobile Technology for Communication in the Educational Setting

2015 ◽  
Vol 24 (2) ◽  
pp. 45-50 ◽  
Author(s):  
Meghan O'Brien
Keyword(s):  
Case Studies ◽  
Mobile Technology ◽  
Feature Matching ◽  
School Setting ◽  
Educational Setting ◽  
Complex Communication Needs ◽  
Matching Process ◽  
Communication Needs ◽  
Technology Systems

This article is intended to emphasize the importance of using a feature-matching process when identifying mobile technology systems for students with complex communication needs. The reader will be able to apply features of the needs and abilities of the student to the features of a mobile tech system (commercially available system with application) to determine if it is a match for the student. Case studies are used to illustrate the feature matching process in the school setting.

AAC Assessment and Intervention in Pediatric ICU/Acute Care: From Referral Through Continuum of Care

2013 ◽  
Vol 22 (2) ◽  
pp. 102-111 ◽  
Author(s):  
Rachel Santiago ◽  
John M. Costello
Keyword(s):  
Service Delivery ◽  
Acute Care ◽  
Feature Matching ◽  
Continuum Of Care ◽  
Patient Needs ◽  
Matching Process ◽  
Pediatric Icu ◽  
Patient Presentation ◽  
The Continuum

Abstract AAC supports in the ICU/Acute Care vary by patient presentation. Our 20+ years of bedside service delivery reveals trends in patient needs for AAC across the continuum of care. We detail three phases of AAC assessment and intervention and the role of SLP in the assessment/feature matching process.

scholarly journals Visual search for a target changing in synchrony with an auditory signal

2005 ◽  
Vol 273 (1588) ◽  
pp. 865-874 ◽  
Author(s):  
Waka Fujisaki ◽  
Ansgar Koene ◽  
Derek Arnold ◽  
Alan Johnston ◽  
Shin'ya Nishida
Keyword(s):  
Visual Search ◽  
Feature Matching ◽  
Visual Target ◽  
Reaction Times ◽  
Auditory Signal ◽  
Temporal Synchrony ◽  
Matching Process ◽  
Serial Process ◽  
Target Visibility ◽  
Parallel Processing Model

We examined whether the detection of audio–visual temporal synchrony is determined by a pre-attentive parallel process, or by an attentive serial process using a visual search paradigm. We found that detection of a visual target that changed in synchrony with an auditory stimulus was gradually impaired as the number of unsynchronized visual distractors increased (experiment 1), whereas synchrony discrimination of an attended target in a pre-cued location was unaffected by the presence of distractors (experiment 2). The effect of distractors cannot be ascribed to reduced target visibility nor can the increase in false alarm rates be predicted by a noisy parallel processing model. Reaction times for target detection increased linearly with number of distractors, with the slope being about twice as steep for target-absent trials as for target-present trials (experiment 3). Similar results were obtained regardless of whether the audio–visual stimulus consisted of visual flashes synchronized with amplitude-modulated pips, or of visual rotations synchronized with frequency-modulated up–down sweeps. All of the results indicate that audio–visual perceptual synchrony is judged by a serial process and are consistent with the suggestion that audio–visual temporal synchrony is detected by a ‘mid-level’ feature matching process.

Research on the Recognition Technology of Letter String on Label

2012 ◽  
Vol 476-478 ◽  
pp. 876-880
Author(s):  
Yu Chan Xie ◽  
Wei Jin
Keyword(s):  
Projection Method ◽  
Feature Matching ◽  
Small Region ◽  
Letter Recognition ◽  
Geometric Feature ◽  
Theory Analysis ◽  
Recognition Method ◽  
Matching Process ◽  
Straight Line ◽  
Single Character

A letters recognition method based on a small area of the feature matching is proposed, in which the unqualified letter in the string were recognized based on its geometric feature. In this method, firstly confirm label’s position by the extracted straight line characters. Then, rotate and zoom image according to the prior knowledge of label. Acquire the position of the string on the label and cut them out. And then single letters are segmented out using the projection method in the area of the string. Finally, compare geometrical characteristics of the extracted letters with its template to recognize and eliminate unqualified labels. The matching process proceeded only in the small region with letters and there is no need to search the irrelevant information. For this algorithm, so much time has been saved compared with traditional letter recognition method and projection method which is used for single character segmentation is very quick also. Theory analysis and experiment showed that the method can segment unqualified characters robustly and quickly.

Neighborhood Rough Set Approach with Biometric Application

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Rough Set ◽  
Experimental Analysis ◽  
Feature Matching ◽  
Human Identification ◽  
High Accuracy ◽  
Experimental Result ◽  
New Approach ◽  
Ear Recognition ◽  
Matching Process ◽  
Neighborhood Rough Set

This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds for different classes of features. The feature vectors are obtained from ear image and ear matching process is performed. Actually, matching is a process of ear identification. The extracted features are matched with classes of ear images enrolled in the database. NRS algorithm is developed in this work for feature matching. A set of 20 persons are used for experimental analysis and each person is having six images. The experimental result illustrates the high accuracy of NRS approach when compared to other existing techniques.

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