Automatic Extraction of Lip Based on Wavelet Edge Detection

2006 ◽  
Author(s):  
Ye-peng Guan
Keyword(s):  
Edge Detection ◽  
Automatic Extraction

Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade

2020 ◽  
Vol 65 (1) ◽  
pp. 506-517 ◽  
Author(s):  
Junlong Xu ◽  
Xingping Wen ◽  
Haonan Zhang ◽  
Dayou Luo ◽  
Jinbo Li ◽  
...  
Keyword(s):  
Edge Detection ◽  
Automatic Extraction

scholarly journals COASTLINE EXTRACTION FROM AERIAL IMAGES BASED ON EDGE DETECTION

2016 ◽  
Vol III-8 ◽  
pp. 153-158 ◽  
Author(s):  
V. Paravolidakis ◽  
K. Moirogiorgou ◽  
L. Ragia ◽  
M. Zervakis ◽  
C. Synolakis
Keyword(s):  
Edge Detection ◽  
Human Population ◽  
Active Contours ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Entire Region ◽  
Local Threshold ◽  
Step Algorithm ◽  
Fitting In ◽  
Processing Steps

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.

scholarly journals COASTLINE EXTRACTION FROM AERIAL IMAGES BASED ON EDGE DETECTION

2016 ◽  
Vol III-8 ◽  
pp. 153-158 ◽  
Author(s):  
V. Paravolidakis ◽  
K. Moirogiorgou ◽  
L. Ragia ◽  
M. Zervakis ◽  
C. Synolakis
Keyword(s):  
Edge Detection ◽  
Human Population ◽  
Active Contours ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Entire Region ◽  
Local Threshold ◽  
Step Algorithm ◽  
Fitting In ◽  
Processing Steps

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.

AUTOMATIC EXTRACTION OF LIP BASED ON LIP MAP

2008 ◽  
Vol 05 (01) ◽  
pp. 31-40
Author(s):  
YE-PENG GUAN
Keyword(s):  
Computer Vision ◽  
Edge Detection ◽  
Superior Performance ◽  
Automatic Extraction ◽  
Multi Scale ◽  
Automatic Location ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Lip Segmentation ◽  
Optimum Threshold

The effective automatic location and tracking of a person's lip has been proven to be very difficult in the field of computer vision. A lip segmentation approach is proposed based on wavelet multi-scale edge detection across a lip map. The developed algorithm exploits the spatial interactions between neighboring pixels through wavelet multi-scale edge detection across the lip map. The algorithm produces better segmentation automatically without the need to determine an optimum threshold for each lip image. It has indicated the developed algorithm with superior performance by comparing with some existing lip segmentation algorithms.

scholarly journals Automatic Coastline Extraction Using Edge Detection and Optimization Procedures

Geosciences ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 407 ◽  
Author(s):  
Vasilis Paravolidakis ◽  
Lemonia Ragia ◽  
Konstantia Moirogiorgou ◽  
Michalis Zervakis
Keyword(s):  
Climate Change ◽  
Edge Detection ◽  
Satellite Images ◽  
Active Contours ◽  
Coastal Development ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Spatial Objects ◽  
Safe Navigation ◽  
High Resolution Satellite Images

Coastal areas are quite fragile landscapes as they are among the most vulnerable to climate change and natural hazards. Coastline mapping and change detection are essential for safe navigation, resource management, environmental protection, and sustainable coastal development and planning. In this paper, we proposed a new methodology for the automatic extraction of coastline, using aerial images. This method is based on edge detection and active contours (snake method). Initially the noise of the image is reduced which is followed by an image segmentation. The output images are further processed to remove all small spatial objects and to concentrate on the spatial objects of interests. Then, the morphological operators are applied. We used aerial images taken from an aircraft and high-resolution satellite images from a coastal area in Crete, Greece, and we compared the results with geodetic measurements, to validate the methodology.

Difference mode EXELFS with parallel-detection EELS

1992 ◽  
Vol 50 (2) ◽  
pp. 1262-1263
Author(s):  
Michael K. Kundmann ◽  
Ondrej L. Krivanek
Keyword(s):  
Edge Detection ◽  
Important Distinction ◽  
Edge Structure ◽  
Parallel Detection ◽  
Gain Variation ◽  
Ideal Solutions ◽  
Detector Cell ◽  
The Difference ◽  
Elemental Detection ◽  
Partial Correction

Parallel detection has greatly improved the elemental detection sensitivities attainable with EELS. An important element of this advance has been the development of differencing techniques which circumvent limitations imposed by the channel-to-channel gain variation of parallel detectors. The gain variation problem is particularly severe for detection of the subtle post-threshold structure comprising the EXELFS signal. Although correction techniques such as gain averaging or normalization can yield useful EXELFS signals, these are not ideal solutions. The former is a partial throwback to serial detection and the latter can only achieve partial correction because of detector cell inhomogeneities. We consider here the feasibility of using the difference method to efficiently and accurately measure the EXELFS signal.An important distinction between the edge-detection and EXELFS cases lies in the energy-space periodicities which comprise the two signals. Edge detection involves the near-edge structure and its well-defined, shortperiod (5-10 eV) oscillations. On the other hand, EXELFS has continuously changing long-period oscillations (∼10-100 eV).

A Dividing Ratio Changeable Digital PLL Based on Phase State Memory and Double Clock-Edge Detection

2008 ◽  
Vol 128 (7) ◽  
pp. 1185-1190 ◽  
Author(s):  
Kuniaki Fujimoto ◽  
Hirofumi Sasaki ◽  
Mitsutoshi Yahara
Keyword(s):  
Edge Detection ◽  
Phase State
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