scholarly journals An Effective Color Addition to Feature Detection and Description for Book Spine Image Matching

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Spencer G. Fowers ◽  
Dah-Jye Lee
Keyword(s):  
Radio Frequency Identification ◽  
Feature Detection ◽  
Feature Matching ◽  
Low Cost ◽  
Color Difference ◽  
Automation System ◽  
Feature Descriptor ◽  
Color Feature ◽  
Imaging Device ◽  
Feature Descriptors

The important task of library book inventory, or shelf-reading, requires humans to remove each book from a library shelf, open the front cover, scan a barcode, and reshelve the book. It is a labor-intensive and often error-prone process. Technologies such as 2D barcode scanning or radio frequency identification (RFID) tags have recently been proposed to improve this process. They both incur significant upfront costs and require a large investment of time to fit books with special tags before the system can be productive. A vision-based automation system is proposed to improve this process without those prohibitively high upfront costs. This low-cost shelf-reading system uses a hand-held imaging device such as a smartphone to capture book spine images and a server that processes feature descriptors in these images for book identification. Existing color feature descriptors for feature matching typically use grayscale feature detectors, which omit important color edges. Also, photometric-invariant color feature descriptors require unnecessary computations to provide color descriptor information. This paper presents the development of a simple color enhancement feature descriptor called Color Difference-of-Gaussians SIFT (CDSIFT). CDSIFT is well suited for library inventory process automation, and this paper introduces such a system for this unique application.

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 FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1632
Author(s):  
Chien-Hung Kuo ◽  
Erh-Hsu Huang ◽  
Chiang-Heng Chien ◽  
Chen-Chien Hsu
Keyword(s):  
Stereo Vision ◽  
Feature Detection ◽  
Feature Matching ◽  
Threshold Value ◽  
Frame Rate ◽  
Feature Descriptor ◽  
Fpga Design ◽  
Scale Invariant ◽  
Finite Area ◽  
Time Required

In this paper, we propose an FPGA-based enhanced-SIFT with feature matching for stereo vision. Gaussian blur and difference of Gaussian pyramids are realized in parallel to accelerate the processing time required for multiple convolutions. As for the feature descriptor, a simple triangular identification approach with a look-up table is proposed to efficiently determine the direction and gradient of the feature points. Thus, the dimension of the feature descriptor in this paper is reduced by half compared to conventional approaches. As far as feature detection is concerned, the condition for high-contrast detection is simplified by moderately changing a threshold value, which also benefits the reduction of the resulting hardware in realization. The proposed enhanced-SIFT not only accelerates the operational speed but also reduces the hardware cost. The experiment results show that the proposed enhanced-SIFT reaches a frame rate of 205 fps for 640 × 480 images. Integrated with two enhanced-SIFT, a finite-area parallel checking is also proposed without the aid of external memory to improve the efficiency of feature matching. The resulting frame rate by the proposed stereo vision matching can be as high as 181 fps with good matching accuracy as demonstrated in the experimental results.

A novel 3D feature detection and matching approach for autonomous planetary landing mission

2018 ◽  
Vol 233 (6) ◽  
pp. 2241-2249 ◽  
Author(s):  
Yang Tian ◽  
Meng Yu ◽  
Yingying Zhang
Keyword(s):  
Pose Estimation ◽  
Feature Detection ◽  
Feature Matching ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Feature Description ◽  
Natural Feature ◽  
Planetary Landing ◽  
Distance Set ◽  
Simulation Results

In an autonomous planetary/asteroid landing mission, landmark recognition is crucial to the success of the navigation system. The failure of feature detection or matching could lead to evident increase of bias in lander pose estimation. To this end, we propose a novel 3D feature detection and matching algorithm in this paper. The spherical harmonic coefficients are adopted to describe a 3D natural feature, and a relative distance set feature description approach is proposed as a supplement feature descriptor to enhance the distinctiveness of 3D feature. Simulation results demonstrate the effectiveness of our complete feature detection and matching algorithm in terms of feature detection rate and correct feature matching rate.

A Method of Moving Object Tracking Based on Background Subtraction and Color Feature

2013 ◽  
Vol 710 ◽  
pp. 747-750
Author(s):  
Chun Yang Liu ◽  
Dao Zheng Hou ◽  
Chang An Liu
Keyword(s):  
Target Tracking ◽  
Background Subtraction ◽  
Feature Matching ◽  
Search Algorithm ◽  
Color Difference ◽  
Subtraction Method ◽  
Color Feature ◽  
Multi Target Tracking ◽  
Weak Points ◽  
Feature Based

Background subtraction method is a tracking method only based on motion information. It cannot make a distinction between the foreground objects detected. The feature-based tracking methods need to select the appropriate prediction and search algorithm. But the algorithm complexity and amount of computation is large for the multi-target tracking. Therefore, this paper presents the method based on the combination of background subtraction and color feature. For improving the weak points of the traditional background subtraction method based on grayscale image, it is proposed to use the background subtraction based on RGB color difference. It improves the integrity of the foreground regions that makes better effect of the color feature matching. The experimental results show that in the multi-target tracking applications, the proposed method is simple and easy to implement. It can be used to track the targets with specific color feature and has high robustness.

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.

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.

scholarly journals On the application of the EM-imaging for chipless RFID tags

2015 ◽  
Vol 2 (2) ◽  
pp. 86-96 ◽  
Author(s):  
M. Zomorrodi ◽  
N.C. Karmakar
Keyword(s):  
Radio Frequency Identification ◽  
Low Cost ◽  
60 Ghz ◽  
Rfid Tag ◽  
High Data ◽  
Data Encoding ◽  
Bending Effect ◽  
Chipless Rfid ◽  
Frequency Identification ◽  
Cost Item

The electromagnetic (EM) imaging technique at mm-band 60 GHz is proposed for data encoding purpose in the chipless Radio Frequency Identification (RFID) systems. The fully printable chipless RFID tag comprises tiny conductive EM polarizers to create high cross-polar radar cross-section. Synthetic aperture radar approach is applied for formation of the tag's EM-image and revealing the tag's content. The achieved high data encoding capacity of 2 bits/cm2in this technique based on a fully printable tag is very convincing for many applications. The system immunity to multipath interference, bending effect, and printing inaccuracy suggests huge potentials for low-cost item tagging. Tags are also readable through a tick paper envelop; hence secure identification is provided by the proposed technique.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

scholarly journals FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei
Keyword(s):  
Feature Matching ◽  
Field Of View ◽  
Image Feature ◽  
Feature Points ◽  
Matching Process ◽  
Feature Descriptors ◽  
Coordinate Mapping ◽  
Matching Performance ◽  
Perspective Model ◽  
Attitude Matrix

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

A review of passive self-sensing tag

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Yawei Xu ◽  
Lihong Dong ◽  
Haidou Wang ◽  
Jiannong Jing ◽  
Yongxiang Lu
Keyword(s):  
Radio Frequency Identification ◽  
Large Scale ◽  
Low Cost ◽  
Integrated Sensor ◽  
Content Type ◽  
Passive Sensing ◽  
Static Information ◽  
Potential Applications ◽  
Frequency Identification ◽  
Identification Tags

Purpose Radio frequency identification tags for passive sensing have attracted wide attention in the area of Internet of Things (IoT). Among them, some tags can sense the property change of objects without an integrated sensor, which is a new trend of passive sensing based on tag. The purpose of this paper is to review recent research on passive self-sensing tags (PSSTs). Design/methodology/approach The PSSTs reported in the past decade are classified in terms of sensing mode, composition and the ways of power supply. This paper presents operation principles of PSSTs and analyzes the characteristics of them. Moreover, the paper focuses on summarizing the latest sensing parameters of PSSTs and their matching equipment. Finally, some potential applications and challenges faced by this emerging technique are discussed. Findings PSST is suitable for long-term and large-scale monitoring compared to conventional sensors because it gets rid of the limitation of battery and has relatively low cost. Also, the static information of objects stored in different PSSTs can be identified by a single reader without touch. Originality/value This paper provides a detailed and timely review of the rapidly growing research in PSST.

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