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.

scholarly journals Image Mosaic Research and Realization Based on LoFTR Algorithm

2021 ◽  
Author(s):  
Aikui Tian ◽  
Kangtao Wang ◽  
liye zhang ◽  
Bingcai Wei
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
The Self ◽  
Image Feature ◽  
Feature Descriptor ◽  
Image Mosaic ◽  
Feature Points ◽  
Matching Method ◽  
Fusion Algorithm ◽  
The Matrix

Abstract Aiming at the problem of inaccurate extraction of feature points by the traditional image matching method, low robustness, and problems such as diffculty in inentifying feature points in area with poor texture. This paper proposes a new local image feature matching method, which replaces the traditional sequential image feature detection, description and matching steps. First, extract the coarse features with a resolution of 1/8 from the original image, then tile to a one-dimensional vector plus the positional encoding, feed them to the self-attention layer and cross-attention layer in the Transformer module, and finally get through the Differentiable Matching Layer and confidence matrix, after setting the threshold and the mutual closest standard, a Coarse-Level matching prediction is obtained. Secondly the fine matching is refined at the Fine-level match, after the Fine-level match is established, the image overlapped area is aligned by transforming the matrix to a unified coordinate, and finally the image is fused by the weighted fusion algorithm to realize the unification of seamless mosaic of images. This paper uses the self-attention layer and cross-attention layer in Transformers to obtain the feature descriptor of the image. Finally, experiments show that in terms of feature point extraction, LoFTR algorithm is more accurate than the traditional SIFT algorithm in both low-texture regions and regions with rich textures. At the same time, the image mosaic effect obtained by this method is more accurate than that of the traditional classic algorithms, the experimental effect is more ideal.

scholarly journals Performance Evaluation of Gradient Domain and Pyramid Blending Used in Image Stitching Process with ORB Binary Descriptor

2019 ◽  
Vol 9 (1) ◽  
pp. 1849-1854
Keyword(s):  
Feature Detection ◽  
Computation Time ◽  
Automatic Target Recognition ◽  
Image Features ◽  
Image Feature ◽  
Image Stitching ◽  
Imaging Data ◽  
Multiple Images ◽  
Binary Descriptor ◽  
Blending Method

Panorama development is the basically method of integrating multiple images captured of the same scene under consideration to get high resolution image. This process is useful for combining multiple images which are overlapped to obtain larger image. Usefulness of Image stitching is found in the field related to medical imaging, data from satellites, computer vision and automatic target recognition in military applications. The goal objective of this research paper is basically for developing an high improved resolution and its quality panorama having with high accuracy and minimum computation time. Initially we compared different image feature detectors and tested SIFT, SURF, ORB to find out the rate of detection of the corrected available key points along with processing time. Later on, testing is done with some common techniques of image blending or fusion for improving the mosaicing quality process. In this experimental results, it has been found out that ORB image feature detection and description algorithm is more accurate, fastest which gives a higher performance and Pyramid blending method gives the better stitching quality. Lastly panorama is developed based on combination of ORB binary descriptor method for finding out image features and pyramid blending method.

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 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 Local Deep Descriptor for Remote Sensing Image Feature Matching

2019 ◽  
Vol 11 (4) ◽  
pp. 430 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Lanfa Liu ◽  
Guojin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computer Vision ◽  
Deep Learning ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Training Dataset ◽  
Feature Descriptor ◽  
Remote Sensing Images

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

scholarly journals Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6235
Author(s):  
Chengyi Xu ◽  
Ying Liu ◽  
Fenglong Ding ◽  
Zilong Zhuang
Keyword(s):  
Feature Matching ◽  
Recognition Rate ◽  
Image Features ◽  
Geometric Feature ◽  
Feature Descriptor ◽  
Image Patch ◽  
Feature Description ◽  
Point Pair ◽  
Image Patches ◽  
Local Image

Considering the difficult problem of robot recognition and grasping in the scenario of disorderly stacked wooden planks, a recognition and positioning method based on local image features and point pair geometric features is proposed here and we define a local patch point pair feature. First, we used self-developed scanning equipment to collect images of wood boards and a robot to drive a RGB-D camera to collect images of disorderly stacked wooden planks. The image patches cut from these images were input to a convolutional autoencoder to train and obtain a local texture feature descriptor that is robust to changes in perspective. Then, the small image patches around the point pairs of the plank model are extracted, and input into the trained encoder to obtain the feature vector of the image patch, combining the point pair geometric feature information to form a feature description code expressing the characteristics of the plank. After that, the robot drives the RGB-D camera to collect the local image patches of the point pairs in the area to be grasped in the scene of the stacked wooden planks, also obtaining the feature description code of the wooden planks to be grasped. Finally, through the process of point pair feature matching, pose voting and clustering, the pose of the plank to be grasped is determined. The robot grasping experiment here shows that both the recognition rate and grasping success rate of planks are high, reaching 95.3% and 93.8%, respectively. Compared with the traditional point pair feature method (PPF) and other methods, the method present here has obvious advantages and can be applied to stacked wood plank grasping environments.

Hardware Implementation of AES Algorithm with Logic S-box

2017 ◽  
Vol 26 (09) ◽  
pp. 1750141 ◽  
Author(s):  
Soufiane Oukili ◽  
Seddik Bri
Keyword(s):  
Data Security ◽  
High Speed ◽  
Hardware Implementation ◽  
Network Applications ◽  
Aes Algorithm ◽  
Communication Techniques ◽  
High Speed Network ◽  
Cryptographic Algorithm ◽  
Hardware Implementations ◽  
Field Programmable

Cryptography has an important role in data security against known attacks and decreases or limits the risks of hacking information, especially with rapid growth in communication techniques. In the recent years, we have noticed an increasing requirement to implement cryptographic algorithms in fast rising high-speed network applications. In this paper, we present high throughput efficient hardware implementations of Advanced Encryption Standard (AES) cryptographic algorithm. We have adopted pipeline technique in order to increase the speed and the maximum operating frequency. Therefore, registers are inserted in optimal placements. Furthermore, we have proposed 5-stage pipeline S-box design using combinational logic to reach further speed. In addition, efficient key expansion architecture suitable for our proposed design is also presented. In order to secure the hardware implementation against side-channel attacks, masked S-box is introduced. The implementations had been successfully done by virtex-6 (xc6vlx240t) Field-Programmable Gate Array (FPGA) device using Xilinx ISE 14.7. Our proposed unmasked and masked architectures are very fast, they achieve a throughput of 93.73 Gbps and 58.57 Gbps, respectively. The obtained results are competitive in comparison with the implementations reported in the literature.

scholarly journals FPGA Implementation of DTCWT and PCA Based Watermarking Technique

2018 ◽  
Vol 7 (2) ◽  
pp. 82
Author(s):  
M. S. Sudha ◽  
T. C. Thanuja
Keyword(s):  
Wavelet Transform ◽  
Field Programmable Gate Array ◽  
High Speed ◽  
Hardware Implementation ◽  
Image Watermarking ◽  
Software Implementation ◽  
Low Power Consumption ◽  
Complex Wavelet Transform ◽  
System Generator ◽  
Field Programmable

The hardware implementation of the image watermarking algorithm offers numerous distinct advantages over the software implementation in terms of low power consumption, less area usage and reliability. The advantages of Dual Tree Complex Wavelet Transform (DTCWT) and Principle Component Analysis (PCA) techniques are extracted to improve the robustness and perceptibility. The hardware watermarking solution is more economical, because adding the component only takes up a small dedicated area of silicon. The algorithm is developed and simulated using Matlab, Simulink and system generator. The implementation is carried out using Spartan 6 Diligent Atlys Field Programmable Gate array (FPGA). The architecture uses 256 slice registers, 257 slice Look Up Tables (LUT’s) and 47 I/O pins. It also meets the requirement of high speed architecture with a delay of 1.328ns and an operating frequency of 549.451MHz.

A deep neural network-based vehicle re-identification method for bridge load monitoring

2021 ◽  
pp. 136943322110339
Author(s):  
Yufeng Zhang ◽  
Junxin Xie ◽  
Jiayi Peng ◽  
Hui Li ◽  
Yong Huang
Keyword(s):  
Neural Network ◽  
Feature Detection ◽  
Image Data ◽  
Image Features ◽  
Superior Performance ◽  
Multiple Sources ◽  
Identification Method ◽  
Load Monitoring ◽  
Bridge Structures ◽  
Point Feature

The accurate tracking of vehicle loads is essential for the condition assessment of bridge structures. In recent years, a computer vision method that is based on multiple sources of data from monitoring cameras and weight-in-motion (WIM) systems has become a promising strategy in bridge vehicle load identification for structural health monitoring (SHM) and has attracted increasing attention. The implementation of vehicle re-identification, namely, the identification of the same vehicle from images that were captured at different locations or time instants, is the key topic of this study. In this study, a vehicle re-identification method that is based on HardNet, a deep convolutional neural network (CNN) specialized in picking up local image features, is proposed. First, we obtain the vehicle point feature positions in the image through feature detection. Then, the HardNet is employed to encode the point feature image patches into deep learning feature descriptors. Re-identification of the target vehicle is achieved by matching the encoded descriptors between two images, which are robust toward scaling, rotation, and other types of noises. A comparison study of the proposed method with three published vehicle re-identification methods is performed using vehicle image data from a real bridge, and the superior performance of our proposed method is demonstrated.

NONLINEAR FM INDEX APPLICATION FOR ALIGNMENT OF SHORT DNA SEQUENCES USING RE-PARAMETRIZATION OF ALGORITHMS

Fractals ◽  
2018 ◽  
Vol 26 (03) ◽  
pp. 1850023 ◽  
Author(s):  
D. PACHECO BAUTISTA ◽  
R. CARREÑO AGUILERA ◽  
E. CORTÉS PÉREZ ◽  
M. GONZÁLEZ PÉREZ ◽  
J. J. MEDEL ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Processing Speed ◽  
High Speed ◽  
Hardware Implementation ◽  
Massively Parallel Sequencing ◽  
Search Algorithm ◽  
Massively Parallel ◽  
Field Programmable ◽  
Very High ◽  
Computation Speed

An innovative reconfiguration application is proposed to re-calculate the parameters of the Ferragina and Manzini exact search algorithm (or FM indexes), using a modular and efficient hardware implementation to accelerate alignment programs of short DNA sequence reads. Although these programs use multi-core execution strategies or multiple computers, they have become slow considering the very high speed at which the new massively parallel sequencing machines produce the reads to be aligned. Consequently, a search for different ways to accelerate the alignment is crucial. The proposed design runs with software functions in a hybrid system, and has the ability to align millions of reads to reference as large as the human genome. Tests on the M505k325t card show that a single alignment core can accelerate the computation by a factor close to [Formula: see text] in relation to BWA. Due to the minor consumption of area and power, multiple alignment cores can fill the Field Programmable Gate Array (FPGA) by multiplying the computation speed. With a multiple-core implementation, the processing speed of the design outperforms applications that are accelerated by GPUs and competes with similar FPGA proposals whose cost is much higher.

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