scholarly journals Perbandingan DTCWT dan NMF pada Face Recognition menggunakan Euclidean Distance

2014 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
David David ◽  
Ferdinand Ariandy Luwinda
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Image Features ◽  
Face Image ◽  
Experimental Result ◽  
High Dimensional ◽  
Discrete Wavelet ◽  
Directional Selectivity ◽  
Complex Wavelet Transform ◽  
Non Negative Matrix Factorization

Dual tree complex wavelet transform (DTCWT) is widely used for representation of face image features. DTCWT is more frequently used than Gabor or Discrete Wavelet Transform (DWT) because it provides good directional selectivity in six different directions. Meanwhile, non-negative matrix factorization (NMF) is also frequently used since it can reduce high dimensional feature into smaller one without losing important features. This research focused on comparison between DTCWT and NMF as feature extraction and Euclidean Distance for classification. This research used ORL Faces database. Experimental result showed that NMF provided better results than DTCWT did. NMF reached 92% of accuracy and DTCWT reached 78% of accuracy. 

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

Real-Time Vehicle License Plate Recognition Based on Scanning and 2D Haar Discrete Wavelet Transform

2013 ◽  
Vol 284-287 ◽  
pp. 2402-2406 ◽  
Author(s):  
Rong Choi Lee ◽  
King Chu Hung ◽  
Huan Sheng Wang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Recognition Rate ◽  
Low Frequency ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Image Pixels

This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.

scholarly journals Performance Analysis of Iris Recognition using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

2019 ◽  
Vol 8 (6) ◽  
pp. 1-10
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Iris Recognition ◽  
Image Features ◽  
Discrete Wavelet ◽  
Multi Stage ◽  
Human Iris ◽  
The Individual ◽  
Matching Score ◽  
Bicubic Interpolation

Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual's. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5x5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).

scholarly journals Computer Graphic and Photographic Image Classification using Local Image Descriptors

2017 ◽  
Vol 67 (6) ◽  
pp. 654 ◽  
Author(s):  
Gajanan K Birajdar ◽  
Vijay H Mankar
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Computer Graphic ◽  
Image Features ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Natural Image ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Graphic Images

<p class="p1">With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy.</p>

scholarly journals Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine

2018 ◽  
Vol 7 (4.10) ◽  
pp. 935
Author(s):  
Vasudha Harlalka ◽  
Viraj Pradip Puntambekar ◽  
Kalugotla Raviteja ◽  
P. Mahalakshmi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Shannon Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Energy Entropy ◽  
Electroencephalogram Eeg ◽  
Logarithmic Energy

Epilepsy is a prevalent condition, mainly affecting the nervous system of the human body. Electroencephalogram (EEG) is used to evaluate and examine the seizures caused due to epilepsy. The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT). Here, Logarithmic energy entropy (LogEn) and Shannon entropy (ShanEn) are taken as input features. These features are fed to Linear Support Vector Machine     (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved. While ShanEn combinations give accuracy of 96.67% for AB-E and 95.5% for ABC-E. These results showcase that our methodology is suitable for overcoming the problem and can become an alternate option for clinical diagnosis.  

BIOLOGICAL SIGNAL ANALYSIS BY INDEPENDENT COMPONENT ANALYSIS USING COMPLEX WAVELET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 595-608 ◽  
Author(s):  
SATOSHI HORIHATA ◽  
ZHONG ZHANG ◽  
TAKASHI IMAMURA ◽  
TETSUO MIYAKE ◽  
HIROSHI TODA ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Signal Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
New Method ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Emg Signal ◽  
Electrocardiogram Ecg

Independent component analysis (ICA) is a useful method for blind source separation of two or more signals. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT), in which voice and noise signals were separated using a new method. At that time, we used a simulated signal. In this study, we analyze measured biological signals by using a new method, and discuss its effectiveness. As an experiment, we try to separate an electromyogram (EMG) signal from an electrocardiogram (ECG) signal.

Computational Paradigm and Quantitative Optimization to Parallel Processing Performance of Still Image Compression

2017 ◽  
Vol 2 (4) ◽  
pp. 11-17
Author(s):  
P. S. Jagadeesh Kumar ◽  
Tracy Lin Huan ◽  
Yang Yung
Keyword(s):  
Wavelet Transform ◽  
Parallel Processing ◽  
Image Compression ◽  
Set Partitioning ◽  
Discrete Wavelet ◽  
Processing Unit ◽  
Digital Information ◽  
Complex Wavelet Transform ◽  
Still Image ◽  
Central Processing

Fashionable and staggering evolution in inferring the parallel processing routine coupled with the necessity to amass and distribute huge magnitude of digital records especially still images has fetched an amount of confronts for researchers and other stakeholders. These disputes exorbitantly outlay and maneuvers the digital information among others, subsists the spotlight of the research civilization in topical days and encompasses the lead to the exploration of image compression methods that can accomplish exceptional outcomes. One of those practices is the parallel processing of a diversity of compression techniques, which facilitates split, an image into ingredients of reverse occurrences and has the benefit of great compression. This manuscript scrutinizes the computational intricacy and the quantitative optimization of diverse still image compression tactics and additional accede to the recital of parallel processing. The computational efficacy is analyzed and estimated with respect to the Central Processing Unit (CPU) as well as Graphical Processing Unit (GPU). The PSNR (Peak Signal to Noise Ratio) is exercised to guesstimate image re-enactment and eminence in harmonization. The moments are obtained and conferred with support on different still image compression algorithms such as Block Truncation Coding (BTC), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Dual Tree Complex Wavelet Transform (DTCWT), Set Partitioning in Hierarchical Trees (SPIHT), Embedded Zero-tree Wavelet (EZW). The evaluation is conceded in provisos of coding efficacy, memory constraints, image quantity and quality.

Data Hiding Scheme for Video Watermarking Using Horizontal and Vertical Coefficients of Single Level Discrete Wavelet Transform Method

2021 ◽  
pp. 2250044
Author(s):  
J. Jerisha Liby ◽  
T. Jaya
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Similarity Index ◽  
Video Watermarking ◽  
Data Matrix ◽  
Experimental Result ◽  
Video Frame ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Single Level

This paper proposes a new watermarking algorithm based on a single-level discrete wavelet transform (DWT). This method initially chooses ‘[Formula: see text]’ number of carrier frames to hide the data. After estimating the carrier frames, each frame is separated into RGB frames. Each R, G, and B frames are decomposed using a single-level DWT. The horizontal and vertical coefficients are selected to embed the watermark information since small changes in the horizontal and vertical coefficients do not highly affect the quality of the video frame. The watermark image pixels are shuffled using a predetermined key before embedding. The shuffled pixels are converted to binary, and they are grouped into three data matrices. Each data matrix is embedded in horizontal and vertical coefficients of the R, G and B frames of the video frame. After embedding the data, the watermarked video is reconstructed using the original approximation coefficients, the embed coefficients, and the original diagonal coefficients. During the extraction process, the watermark is extracted from the horizontal and vertical coefficients of the watermarked video. Experimental result reveals that the proposed method outperforms other related methods in terms of video quality and structural similarity index measurement.

Daubechies Complex Wavelet-Based Computer Vision Applications

2013 ◽  
pp. 138-155 ◽  
Author(s):  
Manish Khare ◽  
Rajneesh Kumar Srivastava ◽  
Ashish Khare
Keyword(s):  
Computer Vision ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Daubechies Complex Wavelet Transform ◽  
Computer Vision Applications ◽  
Complex Wavelet ◽  
Almost All ◽  
Moving Shadow Detection

Many methods for computer vision applications have been developed using wavelet theory. Almost all of them are based on real-valued discrete wavelet transform. This chapter introduces two computer vision applications, namely moving object segmentation and moving shadow detection and removal, using Daubechies complex wavelet transform. Daubechies complex wavelet transform has advantages over discrete wavelet transform as it is approximately shift-invariant, has a better edge detection, and provides true phase information. Results after applying Daubechies complex wavelet transform on these two applications demonstrate that Daubechies complex wavelet transform-based methods provide better results than other real-valued wavelet transform-based methods, and it also demonstrates that Daubechies complex wavelet transform has the potential to be applied to other computer vision applications.

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