Parallelization of the Scale-Invariant Keypoint Detection Algorithm for Cell Broadband Engine Architecture

2008 ◽  
Author(s):  
Bomjun Kwon ◽  
Taiho Choi ◽  
Heejin Chung ◽  
Geonho Kim
Keyword(s):  
Detection Algorithm ◽  
Scale Invariant ◽  
Keypoint Detection ◽  
Cell Broadband Engine

scholarly journals Natural Feature Detection on Mobile Phones with 3D FAST

2010 ◽  
Vol 9 (4) ◽  
pp. 29-34 ◽  
Author(s):  
Achim Weimert ◽  
Xueting Tan ◽  
Xubo Yang
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Hessian Matrix ◽  
Detection Algorithm ◽  
Scale Invariant ◽  
Natural Feature ◽  
Detection Approach ◽  
Scale Levels ◽  
Speed Up ◽  
Low Performance

In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)

scholarly journals CANNY ALGORITHM, COSMIC STRINGS AND THE COSMIC MICROWAVE BACKGROUND

2010 ◽  
Vol 19 (02) ◽  
pp. 183-217 ◽  
Author(s):  
REBECCA J. DANOS ◽  
ROBERT H. BRANDENBERGER
Keyword(s):  
Cosmic Strings ◽  
Simulated Data ◽  
String Tension ◽  
Detection Algorithm ◽  
Small Scale ◽  
Scale Invariant ◽  
South Pole Telescope ◽  
Scaling Solution ◽  
Order Of Magnitude ◽  
Canny Algorithm

We describe a new code to search for signatures of cosmic strings in cosmic microwave anisotropy maps. The code implements the Canny algorithm, an edge detection algorithm designed to search for the lines of large gradients in maps. Such a gradient signature which is coherent in position-space is produced by cosmic strings via the Kaiser–Stebbins effect. We test the power of our new code to set limits on the tension of the cosmic strings by analyzing simulated data, with and without cosmic strings. We compare maps with a pure Gaussian scale-invariant power spectrum with maps which have a contribution of a distribution of cosmic strings obeying a scaling solution. The maps have angular scale and angular resolution comparable to what current and future ground-based small-scale cosmic microwave anisotropy experiments will achieve. We present tests of the codes, indicate the limits on the string tension which could be set with the current code, and describe various ways to refine the analysis. Our results indicate that when applied to the data of ongoing cosmic microwave experiments such as the South Pole Telescope project, the sensitivity of our method to the presence of cosmic strings will be more than an order of magnitude better than the limits from existing analyses.

Rock Core Thin Section Image Stitching Based on SIFT Features

2014 ◽  
Vol 1049-1050 ◽  
pp. 398-401
Author(s):  
Juan Juan Yin ◽  
Guo Jian Cheng ◽  
Na Liu ◽  
Xin Jian Qiang ◽  
Ye Liu
Keyword(s):  
Thin Section ◽  
Detection Algorithm ◽  
Visual Area ◽  
Image Stitching ◽  
Microscopic Image ◽  
Tight Sandstone ◽  
Scale Invariant ◽  
Multi Scale ◽  
Rock Analysis ◽  
Scale Invariant Feature

Because of the inherent conflict between visual area and resolution in rock microscope structure, during the study of the RCTS (Rock Core Thin Section) microstructure, we cannot focus on the multi-scale structure characteristics of the particles, pores and throats, and it is fail to satisfy the demands of a more comprehensive study. In order to solve this problem, a microscopic image stitching method in RCTS is proposed by applying SIFT (Scale Invariant Feature Transform) detection algorithm. This method can successfully solve the conflict between the visual area and resolution, overcoming the problem of inclining and deformation in images acquisition under the microscope and finally, achieving the seamless stitching of RCTS microscopic image for big visual area. The experimental results show that this method can improve the accuracy of rock analysis in microstructure and has important practical and theoretical significance for the development of tight sandstone reservoir.

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 Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Haotian Li ◽  
Kezheng Lin ◽  
Jingxuan Bai ◽  
Ao Li ◽  
Jiali Yu
Keyword(s):  
Object Detection ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Single Shot ◽  
Small Object ◽  
Feature Maps ◽  
Scale Invariant ◽  
Feature Pyramid ◽  
Small Object Detection ◽  
Detection And Localization

In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection, a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed. Firstly, the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure; at the same time, the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel. Then, the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps, and the scale-invariant convolution is performed again on these layers. Finally, the obtained layer is used for detection and localization. The final location coordinates and confidence are output after nonmaximum suppression. Experimental results on the Pascal VOC 2007 and 2012 datasets confirm that there is a 8.2% improvement in mAP compared to the original SSD and some existing algorithms.

scholarly journals Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels

2017 ◽  
Vol 7 (5) ◽  
pp. 2565 ◽  
Author(s):  
Zaynab El khattabi ◽  
Youness Tabii ◽  
Abdelhamid Benkaddour
Keyword(s):  
Moving Average ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Scale Invariant Feature Transform ◽  
Shot Boundary Detection ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Shot Boundary ◽  
Feature Transform ◽  
Scale Invariant Feature

<p>Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.</p>

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