scholarly journals Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4343
Author(s):  
Franco Hidalgo ◽  
Thomas Bräunl
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Processing Time ◽  
Common Interest ◽  
Interest Point ◽  
Visual Slam ◽  
Interest Points ◽  
Interest Point Detectors ◽  
Feature Detectors ◽  
Video Frames

Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, but not extensively for underwater scenarios. In this paper (I) we characterize underwater images where light and suspended particles alter considerably the images captured, (II) evaluate the performance of common interest points detectors and descriptors in a variety of underwater scenes and conditions towards vSLAM in terms of the number of features matched in subsequent video frames, the precision of the descriptors and the processing time. This research justifies the usage of feature detectors in vSLAM for underwater scenarios and present its challenges and limitations.

Automated Evaluation of Interest Point Detectors

2014 ◽  
Vol 2 (1) ◽  
pp. 86-105 ◽  
Author(s):  
Simon R. Lang ◽  
Martin H. Luerssen ◽  
David M. W. Powers
Keyword(s):  
Computer Vision ◽  
Interest Point ◽  
Testing Methods ◽  
Interest Points ◽  
Complex Scene ◽  
Interest Point Detectors ◽  
Automated Evaluation ◽  
Image Set ◽  
Computer Vision Systems ◽  
The Right

Interest point detectors are important components in a variety of computer vision systems. This paper demonstrates an automated virtual 3D environment for controlling and measuring detected interest points on 2D images in an accurate and rapid manner. Real-time affine transform tools enable easy implementation and full automation of complex scene evaluations without the time-cost of a manual setup. Nine detectors are tested and compared using evaluation and testing methods based on Schmid, Mohr, and Bauckhage (2000). Each detector is tested on the BSDS500 image set and 34 3D scanned, and manmade models using rotation in the X, Y, and Z axis as well as scale in the X,Y axis. Varying degrees of noise on the models are also tested. Results demonstrate the differing performance and behaviour of each detector across the evaluated transformations, which may assist computer vision practitioners in choosing the right detector for their application.

scholarly journals A comparative evaluation of interest point detectors and local descriptors for visual SLAM

2009 ◽  
Vol 21 (6) ◽  
pp. 905-920 ◽  
Author(s):  
Arturo Gil ◽  
Oscar Martinez Mozos ◽  
Monica Ballesta ◽  
Oscar Reinoso
Keyword(s):  
Comparative Evaluation ◽  
Interest Point ◽  
Visual Slam ◽  
Local Descriptors ◽  
Interest Point Detectors

scholarly journals Parameters configuration for interest points in images obtained by Drone

2018 ◽  
Author(s):  
Rodrigo A. Rebouças ◽  
Elcio H. Shiguemori ◽  
Lamartine N. F. Guimarães
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
3D Reconstruction ◽  
Optimization Method ◽  
Input Image ◽  
Interest Points ◽  
Image Navigation ◽  
Mosaic Generation ◽  
Best Parameters

Drone use has grown with the use of image processing and computer vision techniques, such as autonomous image navigation, mosaic generation, elevation modeling, 3D reconstruction, and object recognition. In all techniques, an important step is an extraction of features, such as methods of interest points. This work addresses the modes of application of interest points, such as BRISK, ORB, FREAK, AKAZE and LATCH with the parameters configured automatically using the optimization method for images with different textures. This process is one of the pieces of final software that selects the use of a meta heuristic the best parameters automatically according to an input image.

Quantum image interest point extraction

2021 ◽  
pp. 2150063
Author(s):  
Nan Jiang ◽  
Zhuoxiao Ji ◽  
Hong Li ◽  
Jian Wang
Keyword(s):  
Image Processing ◽  
Time Complexity ◽  
Luminance Contrast ◽  
Target Object ◽  
Interest Point ◽  
Interest Points ◽  
Quantum Image ◽  
Absolute Value ◽  
Classical Image ◽  
Better Than

With the development of quantum computing, the application of it to image processing has lots of advantages compared to classical image processing. In this paper, we propose a scheme to extract the interest point in quantum images. Interest point is a kind of feature point which can help to identify the target object in the image. Our scheme is based on the idea of Luminance Contrast (LC) algorithm. The scheme computes the absolute value of gray level differences between a pixel and the others, and then adds all these differences together. The sum is defined as a saliency. After computing the saliency of every pixel, we label the pixels with the maximal saliency as the interest points. The algorithm has pretty good performance and its time complexity is much better than the classical algorithm in same conditions, which provides a new idea for the extraction of image interest point.

Interest Point Detection Based on Monogenic Signal Theory

2014 ◽  
Vol 926-930 ◽  
pp. 3451-3454
Author(s):  
Li Juan Wang ◽  
Chang Sheng Zhang
Keyword(s):  
Computer Vision ◽  
Signal Theory ◽  
Good Precision ◽  
Interest Point ◽  
Phase Congruency ◽  
Interest Points ◽  
Monogenic Signal ◽  
Modern Computer ◽  
Interest Point Detection ◽  
Point Detection

A new algorithm is proposed for interest point detection based on monogenic signal theory in this paper. The detection of stable and informative image points is one of the most important problems in modern computer vision. Phase congruency is a dimensionless measure that remains invariant to changes in image illumination and contrast. A monogenic phase congruency function is constructed using the characteristics to detect interest points in image. The experiment results indicate that different kinds of interest points can be detected and located with good precision, thus the proposed method can be applied over wide classes of images.

scholarly journals Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

2013 ◽  
Vol 13 (6) ◽  
pp. 329-338 ◽  
Author(s):  
Martin Zukal ◽  
Radek Beneš ◽  
Petr Číka ◽  
Kamil Říha
Keyword(s):  
Speckle Noise ◽  
Ultrasound Images ◽  
Interest Point ◽  
Interest Points ◽  
Objective Criterion ◽  
Interest Point Detectors ◽  
Objective Quality ◽  
Interest Point Detector ◽  
Medical Examinations ◽  
Point Detector

Abstract This paper focuses on the comparison of different interest point detectors and their utilization for measurements in ultrasound (US) images. Certain medical examinations are based on speckle tracking which strongly relies on features that can be reliably tracked frame to frame. Only significant features (interest points) resistant to noise and brightness changes within US images are suitable for accurate long-lasting tracking. We compare three interest point detectors - Harris-Laplace, Difference of Gaussian (DoG) and Fast Hessian - and identify the most suitable one for use in US images on the basis of an objective criterion. Repeatability rate is assumed to be an objective quality measure for comparison. We have measured repeatability in images corrupted by different types of noise (speckle noise, Gaussian noise) and for changes in brightness. The Harris-Laplace detector outperformed its competitors and seems to be a sound option when choosing a suitable interest point detector for US images. However, it has to be noted that Fast Hessian and DoG detectors achieved better results in terms of processing speed.

scholarly journals Interest Point Detectors for Visual SLAM

2007 ◽  
pp. 170-179 ◽  
Author(s):  
Óscar Martínez Mozos ◽  
Arturo Gil ◽  
Monica Ballesta ◽  
Oscar Reinoso
Keyword(s):  
Interest Point ◽  
Visual Slam ◽  
Interest Point Detectors

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

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