Explaining feature detection Mechanisms: A Survey

2021 ◽  
pp. 51-64
Author(s):  
Ahmed A. Elngar ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Image Features ◽  
Mathematical Concepts ◽  
Detection Algorithms ◽  
Feature Detectors ◽  
Essential Components ◽  
Basic Notation ◽  
Computer Vision Applications ◽  
Detection Mechanisms

Feature detection, description and matching are essential components of various computer vision applications; thus, they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection algorithms.

scholarly journals A PERFORMANCE COMPARISON OF FEATURE DETECTORS FOR PLANETARY ROVER MAPPING AND LOCALIZATION

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 149-154 ◽  
Author(s):  
W. Wan ◽  
M. Peng ◽  
Y. Xing ◽  
Y. Wang ◽  
Z. Liu ◽  
...  
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Comprehensive Evaluation ◽  
Evaluation Criteria ◽  
Performance Comparison ◽  
Stereo Images ◽  
Feature Detectors ◽  
Comparison Results ◽  
Feature Based ◽  
Key Techniques

Feature detection and matching are key techniques in computer vision and robotics, and have been successfully implemented in many fields. So far there is no performance comparison of feature detectors and matching methods for planetary mapping and rover localization using rover stereo images. In this research, we present a comprehensive evaluation and comparison of six feature detectors, including Moravec, Förstner, Harris, FAST, SIFT and SURF, aiming for optimal implementation of feature-based matching in planetary surface environment. To facilitate quantitative analysis, a series of evaluation criteria, including distribution evenness of matched points, coverage of detected points, and feature matching accuracy, are developed in the research. In order to perform exhaustive evaluation, stereo images, simulated under different baseline, pitch angle, and interval of adjacent rover locations, are taken as experimental data source. The comparison results show that SIFT offers the best overall performance, especially it is less sensitive to changes of image taken at adjacent locations.

scholarly journals Hardware Friendly Robust Synthetic Basis Feature Descriptor

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 847 ◽  
Author(s):  
Dong Zhang ◽  
Lindsey Ann Raven ◽  
Dah-Jye Lee ◽  
Meng Yu ◽  
Alok Desai
Keyword(s):  
Computer Vision ◽  
Feature Matching ◽  
Good Alternative ◽  
Image Features ◽  
Feature Descriptor ◽  
Binary Feature ◽  
Computer Vision Applications ◽  
Computational Simplicity ◽  
Discrete Manner ◽  
Predetermined Number

Finding corresponding image features between two images is often the first step for many computer vision algorithms. This paper introduces an improved synthetic basis feature descriptor algorithm that describes and compares image features in an efficient and discrete manner with rotation and scale invariance. It works by performing a number of similarity tests between the feature region surrounding the feature point and a predetermined number of synthetic basis images to generate a feature descriptor that uniquely describes the feature region. Features in two images are matched by comparing their descriptors. By only storing the similarity of the feature region to each synthetic basis image, the overall storage size is greatly reduced. In short, this new binary feature descriptor is designed to provide high feature matching accuracy with computational simplicity, relatively low resource usage, and a hardware friendly design for real-time vision applications. Experimental results show that our algorithm produces higher precision rates and larger number of correct matches than the original version and other mainstream algorithms and is a good alternative for common computer vision applications. Two applications that often have to cope with scaling and rotation variations are included in this work to demonstrate its performance.

scholarly journals Optimization and Implementation of Synthetic Basis Feature Descriptor on FPGA

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 391
Author(s):  
Dah-Jye Lee ◽  
Samuel G. Fuller ◽  
Alexander S. McCown
Keyword(s):  
High Speed ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Feature Matching ◽  
Image Features ◽  
Image Feature ◽  
Superior Performance ◽  
Feature Descriptor ◽  
Binary Descriptors ◽  
Field Programmable

Feature detection, description, and matching are crucial steps for many computer vision algorithms. These steps rely on feature descriptors to match image features across sets of images. Previous work has shown that our SYnthetic BAsis (SYBA) feature descriptor can offer superior performance to other binary descriptors. This paper focused on various optimizations and hardware implementation of the newer and optimized version. The hardware implementation on a field-programmable gate array (FPGA) is a high-throughput low-latency solution which is critical for applications such as high-speed object detection and tracking, stereo vision, visual odometry, structure from motion, and optical flow. We compared our solution to other hardware designs of binary descriptors. We demonstrated that our implementation of SYBA as a feature descriptor in hardware offered superior image feature matching performance and used fewer resources than most binary feature descriptor implementations.

Performance Analysis of Robust Image Features Detection Algorithms

2014 ◽  
Vol 11 (3) ◽  
pp. 2-15 ◽  
Author(s):  
I. Nikolova
Keyword(s):  
Performance Analysis ◽  
Feature Detection ◽  
Image Features ◽  
Detection Algorithms ◽  
Digital Still Camera ◽  
Robust Image

Abstract This paper deals with the challenging task of acquiring stable image features in a sequence of images of the same scene taken under different viewing positions by a digital still camera. Two popular contemporary algorithms for discrete feature detection: SIFT and SURF are regarded. The results of the timing performance analysis of their sequential implementations are presented and discussed. The performance speedup analysis and scalability tests with multi-threading and GPU-based implementations are analyzed

scholarly journals Feature Detection Techniques for Preprocessing Proteomic Data

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Kimberly F. Sellers ◽  
Jeffrey C. Miecznikowski
Keyword(s):  
Feature Detection ◽  
Image Features ◽  
Two Dimensional Gel Electrophoresis ◽  
Detection Techniques ◽  
Low Level ◽  
Detection Algorithms ◽  
Proteomic Data ◽  
Flight Mass Spectrometry ◽  
Level Data ◽  
Large Level

Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.

scholarly journals An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing

2019 ◽  
Vol 5 (1) ◽  
pp. 273-276
Author(s):  
Konstantin Sieler ◽  
Ady Naber ◽  
Werner Nahm
Keyword(s):  
Image Processing ◽  
Feature Detection ◽  
Image Features ◽  
Image Feature ◽  
Spatial Density ◽  
Brain Surgery ◽  
Feature Detector ◽  
Spatial Allocation ◽  
Feature Detectors ◽  
Optical Image Processing

AbstractOptical image processing is part of many applications used for brain surgeries. Microscope camera, or patient movement, like brain-movement through the pulse or a change in the liquor, can cause the image processing to fail. One option to compensate movement is feature detection and spatial allocation. This allocation is based on image features. The frame wise matched features are used to calculate the transformation matrix. The goal of this project was to evaluate different feature detectors based on spatial density and temporal robustness to reveal the most appropriate feature. The feature detectors included corner-, and blob-detectors and were applied on nine videos. These videos were taken during brain surgery with surgical microscopes and include the RGB channels. The evaluation showed that each detector detected up to 10 features for nine frames. The feature detector KAZE resulted in being the best feature detector in both density and robustness.

Performance Enhancement of Image Stitching Process Under Bound Energy Aided Feature Matching and Varying Noise Environments

2020 ◽  
Vol 17 (9) ◽  
pp. 4419-4424
Author(s):  
Venkat P. Patil ◽  
C. Ram Singla
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
Feature Matching ◽  
Performance Enhancement ◽  
Computation Time ◽  
Automatic Target Recognition ◽  
Image Features ◽  
Image Stitching ◽  
Scale Invariant ◽  
Image Mosaicing

Image mosaicing is a method that combines several images or pictures of the superposing field of view to create a panoramic high-resolution picture. In the field of medical imagery, satellite data, computer vision, military automatic target recognition can be seen the importance of image mosaicing. The present domains of studies in computer vision, computer graphics and photo graphics are image stitching and video stitching. The registration of images includes five primary phases: feature detection and description; matching feature; rejection of outliers; transformation function derivation; image replication. Stitching images from specific scenes is a difficult job when images can be picked up under different noise. In this paper, we examine an algorithm for seamless stitching of images in order to resolve all such problems by employing dehazing methods to the collected images, and before defining image features and bound energy characteristics that match image-based features of the SIFT-Scale Invariant Feature Transform. The proposed method experimentation is compared with the conventional methods of stitching of image using squared distance to match the feature. The proposed seamless stitching technique is assessed on the basis of the metrics, HSGV and VSGV. The analysis of this stitching algorithm aims to minimize the amount of computation time and discrepancies in the final stitched results obtained.

scholarly journals ROBUST FEATURE MATCHING IN TERRESTRIAL IMAGE SEQUENCES

2018 ◽  
Vol XLII-3 ◽  
pp. 3-7
Author(s):  
A. Abbas ◽  
S. Ghuffar
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Image Sequences ◽  
Image Stitching ◽  
Urban Scenes ◽  
Structure Estimation ◽  
Scene Structure ◽  
Computer Vision Applications ◽  
Matching Techniques ◽  
Repeating Patterns

From the last decade, the feature detection, description and matching techniques are most commonly exploited in various photogrammetric and computer vision applications, which includes: 3D reconstruction of scenes, image stitching for panoramic creation, image classification, or object recognition etc. However, in terrestrial imagery of urban scenes contains various issues, which include duplicate and identical structures (i.e. repeated windows and doors) that cause the problem in feature matching phase and ultimately lead to failure of results specially in case of camera pose and scene structure estimation. In this paper, we will address the issue related to ambiguous feature matching in urban environment due to repeating patterns.

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

Perception

2018 ◽  
Author(s):  
Joel Z. Leibo ◽  
Tomaso Poggio
Keyword(s):  
Computational Neuroscience ◽  
Feature Detection ◽  
Pedestrian Detection ◽  
Detection Systems ◽  
Catching Up ◽  
Feature Detectors ◽  
Recognition Systems ◽  
Engineered Systems ◽  
Perceptual Systems ◽  
The Brain

This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.

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