scholarly journals A New Scheme for Keypoint Detection and Description

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Lian Yang ◽  
Zhangping Lu
Keyword(s):  
Gaussian Function ◽  
Scale Space ◽  
Hilbert Curve ◽  
Scale Invariant ◽  
Order Difference ◽  
Keypoint Detection ◽  
Difference Of Gaussian ◽  
Pixel Value ◽  
Keypoints Matching ◽  
Critical Aspects

The keypoint detection and its description are two critical aspects of local keypoints matching which is vital in some computer vision and pattern recognition applications. This paper presents a new scale-invariant and rotation-invariant detector and descriptor, coined, respectively, DDoG and FBRK. At first the Hilbert curve scanning is applied to converting a two-dimensional (2D) digital image into a one-dimensional (1D) gray-level sequence. Then, based on the 1D image sequence, an approximation of DoG detector using second-order difference-of-Gaussian function is proposed. Finally, a new fast binary ratio-based keypoint descriptor is proposed. That is achieved by using the ratio-relationships of the keypoint pixel value with other pixel of values around the keypoint in scale space. Experimental results show that the proposed methods can be computed much faster and approximate or even outperform the existing methods with respect to performance.

scholarly journals Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

2021 ◽  
Author(s):  
Parastoo Soleimani ◽  
David W. Capson ◽  
Kin Fun Li
Keyword(s):  
Real Time ◽  
Memory Management ◽  
Scale Space ◽  
Image Resolution ◽  
Hardware Architecture ◽  
Frame Rate ◽  
Time Sharing ◽  
Scale Invariant ◽  
Nonlinear Scale Space ◽  
Nonlinear Scale

AbstractThe first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a $$1280 \times 768$$ 1280 × 768 image resolution is achieved which is favorably faster in comparison with other work.

Progressive Large Scale-Invariant Image Matching in Scale Space

2017 ◽  
Author(s):  
Lei Zhou ◽  
Siyu Zhu ◽  
Tianwei Shen ◽  
Jinglu Wang ◽  
Tian Fang ◽  
...  
Keyword(s):  
Image Matching ◽  
Large Scale ◽  
Scale Space ◽  
Scale Invariant

scholarly journals Keypoint Detection in RGBD Images Based on an Anisotropic Scale Space

2016 ◽  
Vol 18 (9) ◽  
pp. 1762-1771 ◽  
Author(s):  
Maxim Karpushin ◽  
Giuseppe Valenzise ◽  
Frederic Dufaux
Keyword(s):  
Scale Space ◽  
Keypoint Detection

scholarly journals Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT

2020 ◽  
Vol 7 (2) ◽  
pp. 310-320
Author(s):  
Siska Devella ◽  
Yohannes Yohannes ◽  
Firda Novia Rahmawati
Keyword(s):  
Random Forest ◽  
Scale Space ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Indonesia memiliki berbagai warisan budaya tak benda salah satunya adalah kain songket. Kain songket memiliki banyak ragam sesuai ciri khas dari setiap daerah, khususnya songket Palembang. Kain songket Palembang memiliki keistimewaan dibandingkan songket  dari daerah lain. Selain memiliki nilai sejarah, kain songket Palembang memiliki motif, mutu dan tingkat kerumitan yang tinggi dalam proses pembuatannya. Pada penelitian ini digunakan metode Random Forest untuk klasifikasi citra motif kain songket Palembang dengan mengunakan ekstraksi fitur Scale-Invariant Feature Transform (SIFT). Proses pembentukan fitur dengan metode SIFT melalui tahap scale space extrema detection, keypoint localization, orientation assignment, dan keypoint descriptor. Fitur yang dihasilkan digunakan untuk klasifikasi Random Forest. Citra motif songket yang digunakan pada penelitian ini sebanyak 115 citra dari setiap jenis motif, yaitu Bunga cina, Cantik Manis, dan Pulir. Pemilihan citra diambil dari 5 warna setiap motif songket Palembang. Data latih dan data uji yang digunakan masing-masing sebanyak 100 dan 15 untuk setiap motif Songket Palembang. Hasil pengujian menunjukkan bahwa metode SIFT dan Random Forest untuk klasifikasi citra motif kain Songket Palembang dapat memberikan akurasi yang cukup baik, dimana metode SIFT dan Random Forest mampu menghasilkan rata-rata overall accuracy  92,98%, per class accuracy 94,07%,  presision  92,98%, dan recall 89,74%.

scholarly journals Scale-Covariant and Scale-Invariant Gaussian Derivative Networks

2021 ◽  
Author(s):  
Tony Lindeberg
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Large Scale ◽  
Hybrid Approach ◽  
Multiple Scale ◽  
Scale Space ◽  
Training Data ◽  
Scale Invariant ◽  
Gaussian Derivative ◽  
Space Operations

AbstractThis paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade. By sharing the learnt parameters between multiple scale channels, and by using the transformation properties of the scale-space primitives under scaling transformations, the resulting network becomes provably scale covariant. By in addition performing max pooling over the multiple scale channels, or other permutation-invariant pooling over scales, a resulting network architecture for image classification also becomes provably scale invariant. We investigate the performance of such networks on the MNIST Large Scale dataset, which contains rescaled images from the original MNIST dataset over a factor of 4 concerning training data and over a factor of 16 concerning testing data. It is demonstrated that the resulting approach allows for scale generalization, enabling good performance for classifying patterns at scales not spanned by the training data.

A NOVEL ALGORITHM FOR SPEEDING UP KEYPOINT DETECTION AND MATCHING

2008 ◽  
Vol 08 (04) ◽  
pp. 643-661 ◽  
Author(s):  
JING LI ◽  
TAO YANG ◽  
QUAN PAN ◽  
YONG-MEI CHENG ◽  
JUN HOU
Keyword(s):  
Scale Space ◽  
Feature Detector ◽  
Keypoint Detection ◽  
Invariant Feature ◽  
Illumination Changes ◽  
Speed Up ◽  
Application Fields ◽  
Real Time Application ◽  
Keypoint Detector ◽  
Novel Algorithm

This work proposes a novel keypoint detector called QSIF (Quality and Spatial based Invariant Feature Detector). The primary contributions include: (1) a multilevel box filter is used to build the image scales efficiently and precisely, (2) by examining pixels in quality and spatial space simultaneously, QSIF can directly locate the keypoints without scale space extrema detection in the entire image spatial space, (3) QSIF can precisely control the number of output keypoints while maintaining almost the same repeatability of keypoint detection. This characteristic is essential in many real-time application fields. Extensive experimental results with images under scale, rotation, viewpoint and illumination changes demonstrate that the proposed QSIF has a stable and satisfied repeatability, and it can greatly speed up the keypoint detect and matching.

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