Image Similarity Comparison Using Dual-Tree Wavelet Transform

2006 ◽  
pp. 189-197 ◽  
Author(s):  
Mong-Shu Lee ◽  
Li-yu Liu ◽  
Fu-Sen Lin
Keyword(s):  
Wavelet Transform ◽  
Image Similarity ◽  
Similarity Comparison

Analysis of image similarity with CBIR concept using wavelet transform and threshold algorithm

2013 ◽  
Author(s):  
A Haris Rangkuti ◽  
Nashrul Hakiem ◽  
Rizal Broer Bahaweres ◽  
Agus Harjoko ◽  
Agfianto Eko Putro
Keyword(s):  
Wavelet Transform ◽  
Image Similarity ◽  
Threshold Algorithm

Image similarity based on Discrete Wavelet Transform for robots with low-computational resources

2010 ◽  
Vol 58 (7) ◽  
pp. 879-888 ◽  
Author(s):  
Alberto Pretto ◽  
Emanuele Menegatti ◽  
Yoshiaki Jitsukawa ◽  
Ryuichi Ueda ◽  
Tamio Arai
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Similarity ◽  
Discrete Wavelet ◽  
Computational Resources

The Research on Multiscale Image Fusion Based on the Dualtree Complex Wavelet

2013 ◽  
Vol 748 ◽  
pp. 600-604
Author(s):  
Yi Luo ◽  
Gui Ling Yao ◽  
Wei Fan Wang
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Fusion Rule ◽  
Input Image ◽  
Image Similarity ◽  
Source Image ◽  
Multi Scale ◽  
Rapid Extraction ◽  
Fusion Coefficient

In order to effectively ease and solve fusion effect and the contradiction of the algorithm complexity, this paper puts forward a fusion rule on rapid extraction of multi-scale fusion coefficient, this fusion rules first used in the source image multi-scale decomposition of the scale fusion is the extraction of coefficient based on the neighborhood the fusion of window way, the low frequency of the improved neighborhood entropy to extract matching measure (that is, between the input image similarity degree), high frequency with the cross scale neighborhood gradient to extract matching measure, and gives the fusion coefficient formula. Because of the wavelet transform has moved degeneration, this paper puts forward the application of double tree after wavelet transform to do image multi-scale decomposition.

scholarly journals Defect Inspection in Display Panel Using Concentrated Auto Encoder

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
DongHun Ku
Keyword(s):  
Critical Dimension ◽  
Image Similarity ◽  
Inspection Time ◽  
Performance Parameters ◽  
Defect Inspection ◽  
Alignment Position ◽  
Similarity Comparison ◽  
Comparison Methods ◽  
Inspection Performance

In this paper, concentrated auto encoder (CAE) is proposed for aligning photo spacer (PS) and for local inspection of PS. The CAE method has two characteristics. First, unaligned images can be moved to the same alignment position, which makes it possible to move the measured PS images to the same position in order to directly compare the images. Second, the characteristics of the abnormal PS are maintained even if the PS is aligned by the CAE method. The abnormal PS obtained through CAE has the same alignment as the reference PS and has its abnormal characteristics. The presence or absence of defects and the location of defects were identified without precisely measuring the height of the PS and critical dimension (CD). Also, alignment and defect inspection were performed simultaneously, which shortened the inspection time. Finally, inspection performance parameters and inspection time were analyzed to confirm the validity of the CAE method and were compared with the image similarity comparison methods used for defect inspection.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

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