Weld Detect Identification Using Texture Features and Dynamic Time Warping

In this paper, a simple yet robust algorithm for texture identification using 1 Dimensional Discrete Fourier Transform (1-D DFT) and Dynamic Time Warping (DTW) is presented with illumination variations. In the first stage, several image processing techniques namely Fuzzy C means (FCM) clustering, edge detection, Otsu thresholding and inverse surface thresholding method are utilized to locate the region of interest (ROI) where defects might exist. Next, the image undergoes the feature extraction process using 1-D DFT and finally, the features are classified using DTW. Several defect images consist of 2 types of defect namely the porosity and crack are experimented and classified using the DTW.

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Analisis Speaker Recognition Menggunakan Metode Dynamic Time Warping (DTW) Berbasis Matlab

AVITEC ◽

10.28989/avitec.v1i1.492 ◽

2019 ◽

Vol 1 (1) ◽

Author(s):

Noor Fita Indri Prayoga

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Dynamic Time Warping ◽

Recognition Accuracy ◽

Recognition System ◽

Extraction Process ◽

Test Results ◽

Time Warping ◽

Dynamic Time ◽

The Voice

Voice is one of way to communicate and express yourself. Speaker recognition is a process carried out by a device to recognize the speaker through the voice. This study designed a speaker recognition system that was able to identify speakers based on what was said by using dynamic time warping (DTW) method based in matlab. To design a speaker recognition system begins with the process of reference data and test data. Both processes have the same process, which starts with sound recording, preprocessing, and feature extraction. In this system, the Fast Fourier Transform (FFT) method is used to extract the features. The results of the feature extraction process from the two data will be compared using the DTW method. Calculations using DTW that produce the smallest value will be determined as the output. The test results show that the system can identify the voice with the best level of recognition accuracy of 90%, and the average recognition accuracy of 80%. The results were obtained from 50 tests, carried out by 5 people consisting of 3 men and 2 women, each speaker said a predetermined word

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Detection of Cotton Leaf Disease Using Image Processing Techniques

Journal of Physics Conference Series ◽

10.1088/1742-6596/2062/1/012009 ◽

2021 ◽

Vol 2062 (1) ◽

pp. 012009

Author(s):

Sushreeta Tripathy

Keyword(s):

Image Processing ◽

Plant Disease ◽

Region Of Interest ◽

Texture Features ◽

Disease Diagnosis ◽

Cotton Leaf ◽

Diagnosis System ◽

Image Processing Techniques ◽

Plant Disease Diagnosis ◽

Processing Techniques

Abstract In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.

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Quantifying and correcting residual azimuthal anisotropic moveout in image gathers using dynamic time warping

Geophysics ◽

10.1190/geo2019-0324.1 ◽

2020 ◽

Vol 85 (5) ◽

pp. O71-O82

Author(s):

Luke Decker ◽

Qunshan Zhang

Keyword(s):

High Frequency ◽

Dynamic Time Warping ◽

Field Data ◽

Transverse Isotropy ◽

Relative Strength ◽

Time Warping ◽

Dynamic Time ◽

Processing Techniques ◽

Seismic Images ◽

Feature Resolution

We have developed a novel application of dynamic time warping (DTW) for correcting residual moveout in image gathers, enhancing seismic images, and determining azimuthal anisotropic orientation and relative intensity when moveout is caused by wave propagation through a medium possessing elliptical horizontally transverse isotropy (HTI). The method functions by first using DTW to determine the sequences of integer shifts that most closely match seismic traces within an image gather to its stack and then applying those shifts to flatten the gather. Flattening shifts are fitted to an ellipse to provide an approximation for the orientation and relative strength of elliptical HTI anisotropy. We evaluated the method on synthetic and 3D field data examples to show how it is able to (1) correct for residual azimuthal anisotropic moveout, (2) accurately recover high-frequency information and improve feature resolution in seismic images, and (3) determine the anisotropic orientation while providing a measure of relative strength of elliptic anisotropy. Although the method is not intended to replace anisotropic processing techniques for moveout correction, we find that it has the ability to inexpensively approximate the effects of such operations while providing a representation of the elliptic HTI anisotropy present within a volume.

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A Simple and Efficient Arrowhead Detection Technique in Biomedical Images

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416570020 ◽

2016 ◽

Vol 30 (05) ◽

pp. 1657002 ◽

Cited By ~ 12

Author(s):

K. C. Santosh ◽

Naved Alam ◽

Partha Pratim Roy ◽

Laurent Wendling ◽

Sameer Antani ◽

...

Keyword(s):

Template Matching ◽

Dynamic Time Warping ◽

State Of The Art ◽

Region Of Interest ◽

Similarity Score ◽

Test Results ◽

Connected Component ◽

Time Warping ◽

Biomedical Images ◽

Dynamic Time

In biomedical documents/publications, medical images tend to be complex by nature and often contain several regions that are annotated using arrows. In this context, an automated arrowhead detection is a critical precursor to region-of-interest (ROI) labeling and image content analysis. To detect arrowheads, in this paper, images are first binarized using fuzzy binarization technique to segment a set of candidates based on connected component (CC) principle. To select arrow candidates, we use convexity defect-based filtering, which is followed by template matching via dynamic time warping (DTW). The DTW similarity score confirms the presence of arrows in the image. Our test results on biomedical images from imageCLEF 2010 collection shows the interest of the technique, and can be compared with previously reported state-of-the-art results.

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