scholarly journals Measuring the Wave Height Based on Binocular Cameras

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1338 ◽  
Author(s):  
Yan Cang ◽  
Hengxiang He ◽  
Yulong Qiao
Keyword(s):  
Wave Height ◽  
Computation Time ◽  
Height Measurement ◽  
Scale Invariant ◽  
Epipolar Constraint ◽  
Marine Research ◽  
Key Points ◽  
System 2 ◽  
The Difference ◽  
Scale Invariant Feature

The wave is an important hydrological element in marine research. Accurately describing the characteristics of waves is therefore significant to the study of marine power. The contents of this article are as follows: (1) a wave height measurement system using binocular cameras is proposed, and the small tank experiments are conducted to prove the efficacy of the proposed system; (2) based on the scale invariant feature transition (SIFT) algorithm, sub-pixel Harris corners are calculated in the difference-of-Gaussian (DOG) space to locate key points more accurately; and (3) a bi-directional epipolar constraint is employed to decrease the mismatch rate and computation time.

scholarly journals Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor

2017 ◽  
Vol 14 (3) ◽  
pp. 651-661 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Nearest Neighbor ◽  
Daily Basis ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Suggested Approach ◽  
Approximate Nearest Neighbor ◽  
Invariant Feature ◽  
Key Points ◽  
Feature Transform ◽  
Scale Invariant Feature

There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that it operates on a big number of key-points, the only drawback it has is that it is rather time consuming. In the suggested approach, the system deploys SIFT to perform its basic tasks of matching and description is focused on minimizing the number of key-points which is performed via applying Fast Approximate Nearest Neighbor algorithm, which will reduce the redundancy of matching leading to speeding up the process. The proposed application has been evaluated in terms of two criteria which are time and accuracy, and has accomplished a percentage of accuracy of up to 100%, in addition to speeding up the processes of matching and description.

Logo Matching and Recognition Based on Context

2018 ◽  
pp. 164-176
Author(s):  
Tapan Kumar Das
Keyword(s):  
Real World ◽  
Scale Invariant Feature Transform ◽  
Gray Scale ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Key Points ◽  
Gray Scale Image ◽  
Feature Transform ◽  
Graphic Elements ◽  
Scale Invariant Feature

Logos are graphic productions that recall some real-world objects or emphasize a name, simply display some abstract signs that have strong perceptual appeal. Color may have some relevance to assess the logo identity. Different logos may have a similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or differ by the presence/absence of one or few traits. In this chapter, the author uses ensemble-based framework to choose the best combination of preprocessing methods and candidate extractors. The proposed system has reference logos and test logos which are verified depending on some features like regions, pre-processing, key points. These features are extracted by using gray scale image by scale-invariant feature transform (SIFT) and Affine-SIFT (ASIFT) descriptor method. Pre-processing phase employs four different filters. Key points extraction is carried by SIFT and ASIFT algorithm. Key points are matched to recognize fake logo.

scholarly journals Image Mosaic Method Based on SIFT Features of Line Segment

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Zhu ◽  
Mingwu Ren
Keyword(s):  
Line Segment ◽  
Corner Detection ◽  
Image Mosaic ◽  
Scale Invariant ◽  
Point Matching ◽  
Lighting Condition ◽  
Harris Corner ◽  
Key Points ◽  
Scale Invariant Feature ◽  
Harris Corner Detection

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

An Image Stitching Algorithm Based on Histogram Matching and SIFT Algorithm

2017 ◽  
Vol 31 (04) ◽  
pp. 1754006 ◽  
Author(s):  
Jing Zhang ◽  
Guangxue Chen ◽  
Zhaoyang Jia
Keyword(s):  
Weighted Average ◽  
Image Stitching ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Process ◽  
Histogram Matching ◽  
Key Points ◽  
Illumination Changes ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

Image stitching among images that have significant illumination changes will lead to unnatural mosaic image. An image stitching algorithm based on histogram matching and scale-invariant feature transform (SIFT) algorithm is brought out to solve the problem in this paper. First, histogram matching is used for image adjustment, so that the images to be stitched are at the same level of illumination, then the paper adopts SIFT algorithm to extract the key points of the images and performs the rough matching process, followed by RANSAC algorithm for fine matches, and finally calculates the appropriate mathematical mapping model between two images and according to the mapping relationship, a simple weighted average algorithm is used for image blending. The experimental results show that the algorithm is effective.

scholarly journals A Novel Approach Based Multi Biometric Finger Vein Template Recognition System using HGF

2021 ◽  
Vol 11 (1) ◽  
pp. 337-345
Author(s):  
Rahul Dev ◽  
Rohit Tripathi ◽  
Ruqaiya Khanam
Keyword(s):  
Research Work ◽  
Recognition System ◽  
Scale Invariant ◽  
Finger Vein ◽  
Novel Approach ◽  
Key Points ◽  
Vein Recognition ◽  
Scale Invariant Feature ◽  
Finger Vein Recognition ◽  
Recognition Technique

Abstract Finger vein(s) based biometrics is another way to deal with individual's distinguishing proof and has recently received much consideration. The strategy in light of low-level components, like the dark surface of finger vein is taken as standard. However, it is typically looked with numerous difficulties that involves affectability to noise and low neighbourhood consistency. Generally finger vein recognition in view of abnormal state highlights the portrayal that has ended up being a promising method to successfully defeat the above restrictions and enhance the framework execution. This research work proposes finger vein-based recognition technique making use of Hybrid BM3D Filter along with grouped sparse representation for image denoising and feature selection (Local Binary Pattern – LBP, Scale Invariant Feature Transform – SIFT) to evaluate features, key-points and perform recognition. The experimental results on two open databases of finger vein, i.e., HKPU and SDU show that the proposed method has enhanced the overall performance of finger vein pattern recognition system compared with other existing methods.

scholarly journals SCALE INVARIANT FEATURE TRANSFORM PLUS HUE FEATURE

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 27-32
Author(s):  
M. B.Daneshvar
Keyword(s):  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Keypoint Matching ◽  
Matching Process ◽  
Invariant Feature ◽  
Speed Up ◽  
Euclidean Distances ◽  
Feature Transform ◽  
The Difference ◽  
Scale Invariant Feature

This paper presents an enhanced method for extracting invariant features from images based on Scale Invariant Feature Transform (SIFT). Although SIFT features are invariant to image scale and rotation, additive noise, and changes in illumination but we think this algorithm suffers from excess keypoints. Besides, by adding the hue feature, which is extracted from combination of hue and illumination values in HSI colour space version of the target image, the proposed algorithm can speed up the matching phase. Therefore, we proposed the Scale Invariant Feature Transform plus Hue (SIFTH) that can remove the excess keypoints based on their Euclidean distances and adding hue to feature vector to speed up the matching process which is the aim of feature extraction. In this paper we use the difference of hue features and the Mean Square Error (MSE) of orientation histograms to find the most similar keypoint to the under processing keypoint. The keypoint matching method can identify correct keypoint among clutter and occlusion robustly while achieving real-time performance and it will result a similarity factor of two keypoints. Moreover removing excess keypoint by SIFTH algorithm helps the matching algorithm to achieve this goal.

scholarly journals A FAST APPROACH FOR STITCHING OF AERIAL IMAGES

2016 ◽  
Vol XLI-B3 ◽  
pp. 769-774 ◽  
Author(s):  
A. Moussa ◽  
N. El-Sheimy
Keyword(s):  
Computation Time ◽  
Image Data ◽  
Aerial Images ◽  
Aerial Image ◽  
Image Stitching ◽  
Scale Invariant ◽  
Main Challenge ◽  
Transformation Parameters ◽  
Automatic Image Stitching ◽  
Scale Invariant Feature

The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image’s coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

scholarly journals CO-REGISTRATION OF MULTI-TEMPORAL DEM BASED ON SIFT ALGORITHM FOR CHANGE DETECTION OF GLACIERS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 747-752
Author(s):  
Y. Huang ◽  
Q. Hu
Keyword(s):  
Change Detection ◽  
Average Rate ◽  
Rate Of Change ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Multi Temporal ◽  
Feature Information ◽  
The Difference ◽  
Scale Invariant Feature

To detect the change of geographic objects by using multi-temporal DEM, the data must be co-registered firstly. In this paper, the Scale-Invariant Feature Transform (SIFT) algorithm is used to co-register multi-temporal DEM data and glacier change detection. Firstly, the DEM is converted into image space and extracts feature information, calculate multiple sets of match point coordinates, and achieve swift and accurate DEM data co-registration using SIFT algorithm. Secondly, the difference between co-registered DEM datasets are analysed. Total area change and average rate of change are calculated. Finally, the change of multi-temporal DEM data of glaciers in Langkazi County, Tibet from 2004 to 2014 is detected using the method proposed in this paper. The results indicate that the proposed method is able to detect change of the glaciers and the overall accuracy is higher than 85 %.

The Study of Reduce the Mismatch Based on the SIFT Feature Matching

2012 ◽  
Vol 580 ◽  
pp. 378-382
Author(s):  
Xiao Yu Liu ◽  
Yan Piao ◽  
Lei Liu
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Space Rotation ◽  
Feature Transform ◽  
The Difference ◽  
Scale Invariant Feature ◽  
Keypoints Matching

The algorithm of SIFT (scale-invariant feature transform) feature matching is an international hotspot in the areas of the keypoints matching and target recognition at the present time. The algorithm is used in the image matching widely because of the good invariance of scale, illumination and space rotation .This paper proposes a new theory to reduce the mismatch—the theory to reduce the mismatch based on the main orientation of keypoints. This theory should firstly compute the grads of the main orientation of a couple of matched keypoints in the two images and the difference between them. Because the difference of the main orientation of matched keypoints should be much larger than the couples which are matched correctly, we can distinguish and reduce the mismatch through setting the proper threshold, and it can improve the accuracy of the SIFT algorithm greatly.

scholarly journals DETECTING FEATURES VIA MODIFIED HARRIS CORNERS AND MATCHING THEM THROUGH SIFT

2020 ◽  
Author(s):  
Jimut Bahan Pal
Keyword(s):  
Noise Intensity ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Harris Corner ◽  
Key Points ◽  
Daunting Task ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Robust To Noise

Interpreting images spatially is a daunting task which is achieved by detecting corners and features.The most important task of detecting features is achieved by Harris Corner Algorithm. The algorithmis not robust to different scale of the same image. The algorithm may detect corner but when theimage is zoomed in, the corner may appear as ridges. We use the corners detected from HarrisCorner algorithm and treat these as key points to pass into Scale Invariant Feature Transform (SIFT)algorithm. The SIFT algorithm extracts descriptor vector of dimension 128 X 1 from these cornersand can be used to find similarity between different images. This process is quite robust to noise,intensity, scale and occlusion and is used for matching images from a database of descriptors. Wehave investigated both the algorithms in this paper and made a modified version of Harris Corneralgorithm by performing different kind of thresholding, both of them gave a little different result.

Sign in / Sign up

Export Citation Format

Share Document