scholarly journals A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1337
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Performance Evaluation ◽  
Visual Tracking ◽  
Computational Cost ◽  
Performance Comparison ◽  
Feature Detector ◽  
Minimum Eigenvalue ◽  
Speeded Up Robust Features ◽  
A Performance

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), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.

scholarly journals Image Detection System Using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 5356-5362
Author(s):  
Mohini Gawande
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Estimation Method ◽  
Scale Invariant ◽  
Matrix Estimation ◽  
Speeded Up Robust Features ◽  
Invariant Features ◽  
Marketing Analysis ◽  
New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

Randomized Hough Transform

2011 ◽  
pp. 1343-1350 ◽  
Author(s):  
Lei Xu ◽  
Erkki Oja
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Hough Transform ◽  
Random Sampling ◽  
Computational Cost ◽  
Detection Accuracy ◽  
The Past ◽  
Randomized Hough Transform ◽  
Dynamic Storage ◽  
High Computational Cost

Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for decades, while the key idea remains more or less the same. Proposed in 1989 and further developed thereafter, the Randomized Hough Transform (RHT) manages to considerably overcome these shortcomings via innovations on the fundamental mechanisms, with random sampling in place of pixel scanning, converging mapping in place of diverging mapping, and dynamic storage in place of accumulation array. This article will provides an overview on advances and applications of RHT in the past one and half decades.

Performance Evaluation of Survivability Approaches in Optical Networks

2011 ◽  
pp. 134-157
Author(s):  
Abdelhamid Eshoul ◽  
Hussein T. Mouftah
Keyword(s):  
Performance Evaluation ◽  
Optical Networks ◽  
Mesh Networks ◽  
Performance Comparison ◽  
Dynamic Traffic ◽  
Performance Analyses ◽  
The Difference ◽  
A Performance

The chapter outlines the different survivability approaches for mesh networks under static and dynamic traffic environments. It describes the different solution options and their implementations. Also included are detailed performance analyses and evaluations for the difference survivability approaches under both traffic environments. Finally, we present a performance comparison between the different survivability approaches and end the chapter with some concluding remarks.

Image Processing for Surveillance and Security

2017 ◽  
pp. 52-81 ◽  
Author(s):  
Deepayan Bhowmik ◽  
Mehryar Emambakhsh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Digital Media ◽  
Visual Tracking ◽  
Daily Life ◽  
Fundamental Issue ◽  
Image Processing Techniques ◽  
The Media ◽  
Processing Techniques ◽  
Signal Processing Techniques

Security is a fundamental issue in today's world. In this chapter we discuss various aspects of security in daily life that can be solved using image processing techniques by grouping in three main categories: visual tracking, biometrics and digital media security. Visual tracking refers to computer vision techniques that analyses the scene to extract features representing objects (e.g., pedestrian) and track them to provide input to analyse any anomalous behaviour. Biometrics is the technology of detecting, extracting and analysing human's physical or behavioural features for identification purposes. Digital media security typically includes multimedia signal processing techniques that can protect copyright by embedding information within the media content using watermarking approaches. Individual topics are discussed referring recent literature.

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

A performance evaluation and modification of the UNICEF upward-flow water filter

Waterlines ◽  
1993 ◽  
Vol 12 (2) ◽  
pp. 29-31 ◽  
Author(s):  
Vinay Pratap Singh ◽  
Malay Chaudhuri
Keyword(s):  
Performance Evaluation ◽  
Upward Flow ◽  
Water Filter ◽  
A Performance
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