Study on Shape Recognition Based on Contour

2014 ◽  
Vol 644-650 ◽  
pp. 4240-4243
Author(s):  
Wei Zhang
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Fourier Transformation ◽  
Recursive Algorithm ◽  
Shape Recognition ◽  
Experimental Results ◽  
Discrete Fourier Transformation ◽  
Object Contour ◽  
Important Character

Shape is the inherence characteristic of an object in the image, and it is the important character used for the object recognition. So it is significant for object recognition based on shape. This paper presents a contour-based method of feature extraction and shape recognition. First the object contour is translated into a 1-D contour curve. Secondly the curve is smoothed to restrain the noise. The number of peaks of the curve is achieved as well as the areas which contained between adjacent peak-valley, then the latter is followed by Discrete Fourier Transformation (DFT). Then two kinds of features ate extracted which are invariant to translation, scaling and rotation transformations. By using the features, a two-stake recursive algorithm for recognition is proposed. Experimental results show that this method is simple and efficient.

scholarly journals Wavelets assisted fuzzy edge refinement

2014 ◽  
Vol 14 (2) ◽  
pp. 5409-5418
Author(s):  
Kumud Saxena
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Edge Detection ◽  
Image Enhancement ◽  
Experimental Results ◽  
Edge Enhancement ◽  
Processing Step ◽  
Edge Detectors ◽  
Novel Algorithm

Image enhancement is a crucial pre-processing step to be performed for various applications where object recognition, identification, verification is required. Among various image enhancement methods, edge enhancement has taken its importance as it is widely used for understanding features in an image. Several types of edge detectors are available for certain types of edges. If edges are enhanced and clear, the reliability for feature extraction increases. The Quality of edge detection can be measured from several criteria objectively. In this paper, a novel algorithm for edge enhancement has been proposed for multiple types of images. The features can be extracted clearly by using this method. For comparison purpose Roberts, Sobel, Prewitt, Canny, and Log edge operators are used and their results are displayed. Experimental results demonstrate the effectiveness of the proposed approach.

scholarly journals Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Xiaolong Zheng ◽  
Zhanyong Tang ◽  
Tianzhang Xing ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mobile Sensing ◽  
Experimental Results ◽  
Smart Devices ◽  
Detection Accuracy ◽  
Phase Information ◽  
Current Sensing ◽  
Privacy Leakage ◽  
Sensing Systems ◽  
Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

Generation of Wave-Field Time Histories by the Modulated Discrete Fourier Transformation

2003 ◽  
Author(s):  
Yousun Li
Keyword(s):  
Wave Field ◽  
Fourier Transformation ◽  
Time Instant ◽  
Discrete Fourier Transformation ◽  
Fast Fourier Transformation ◽  
Structural Members ◽  
Random Waves ◽  
Offshore Structure ◽  
Time Histories ◽  
The Time Domain

In the time domain simulation of the response of an offshore structure under random waves, the time histories of the wave field should be generated as the input to the dynamic equations. Herein the wave field is the wave surface elevation, the water particle velocities and accelerations at structural members. The generated time histories should be able to match the given wave-field spectral descriptions, to trace the structural member motions if it is a compliant offshore structure, and be numerically efficient. Most frequently used generation methods are the direct summation of a limited number of cosine functions, the Fast Fourier Transformation, and the digital filtering model. However, none of them can really satisfy all the above requirements. A novel technique, called the Modulated Discrete Fourier Transformation, has been developed. Under this method, the wave time histories at each time instant is a summation of a few time-varying complex functions. The simulated time histories have continuous spectral density functions, and the motions of the structural members are well included. This method seems to be superior to all the conventional methods in terms of the above mentioned three requirements.

scholarly journals Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing

2018 ◽  
Vol 41 (8) ◽  
pp. 2338-2351 ◽  
Author(s):  
Anna Swider ◽  
Eilif Pedersen
Keyword(s):  
Signal Processing ◽  
Fourier Transformation ◽  
Filter Design ◽  
Real Life ◽  
Digital Filtering ◽  
Data Preprocessing ◽  
Sensor Data ◽  
Discrete Fourier Transformation ◽  
Filtering Algorithms ◽  
Multiple Sensors

In the phase of industry digitalization, data are collected from many sensors and signal processing techniques play a crucial role. Data preprocessing is a fundamental step in the analysis of measurements, and a first step before applying machine learning. To reduce the influence of distortions from signals, selective digital filtering is applied to minimize or remove unwanted components. Standard software and hardware digital filtering algorithms introduce a delay, which has to be compensated for to avoid destroying signal associations. The delay from filtering becomes more crucial when the analysis involves measurements from multiple sensors, therefore in this paper we provide an overview and comparison of existing digital filtering methods with an application based on real-life marine examples. In addition, the design of special-purpose filters is a complex process and for preprocessing data from many sources, the application of digital filtering in the time domain can have a high numerical cost. For this reason we describe discrete Fourier transformation digital filtering as a tool for efficient sensor data preprocessing, which does not introduce a time delay and has low numerical cost. The discrete Fourier transformation digital filtering has a simpler implementation and does not require expert-level filter design knowledge, which is beneficial for practitioners from various disciplines. Finally, we exemplify and show the application of the methods on real signals from marine systems.

scholarly journals Multiplication of Polynomials using Discrete Fourier Transformation

2006 ◽  
Vol 14 (4) ◽  
pp. 121-128
Author(s):  
Krzysztof Treyderowski ◽  
Christoph Schwarzweller
Keyword(s):  
Fourier Transformation ◽  
Discrete Fourier Transformation ◽  
Vandermonde Matrices ◽  
Univariate Polynomials

Multiplication of Polynomials using Discrete Fourier Transformation In this article we define the Discrete Fourier Transformation for univariate polynomials and show that multiplication of polynomials can be carried out by two Fourier Transformations with a vector multiplication in-between. Our proof follows the standard one found in the literature and uses Vandermonde matrices, see e.g. [27].

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