Skew Detection of Fabric Images Based on Edge Detection and Projection Profile Analysis

2011 ◽  
pp. 483-488 ◽  
Author(s):  
Zhoufeng Liu ◽  
Jie Huang ◽  
Chunlei Li
Keyword(s):  
Edge Detection ◽  
Profile Analysis ◽  
Projection Profile ◽  
Skew Detection

Skew detection using wavelet decomposition and projection profile analysis

2007 ◽  
Vol 28 (5) ◽  
pp. 555-562 ◽  
Author(s):  
Shutao Li ◽  
Qinghua Shen ◽  
Jun Sun
Keyword(s):  
Wavelet Decomposition ◽  
Profile Analysis ◽  
Projection Profile ◽  
Skew Detection

Detection of Text in Videos Using Discrete Wavelet Transform and Gradient Difference

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Manoj Kumar Dixit
Keyword(s):  
Template Matching ◽  
Profile Analysis ◽  
Text Detection ◽  
Character Segmentation ◽  
Video Frame ◽  
Discrete Wavelet ◽  
Correlation Technique ◽  
Projection Profile ◽  
Comparison Results ◽  
Line Segmentation

Text detection in video frames provide highly condensed information about the content of the video and it is useful for video seeking, browsing, retrieval and understanding video text in large video databases. In this paper, we propose a hybrid method that it automatically detects segments and recognizes the text present in the video. Detection is done by using laplacian method based on wavelet and color features. Segmentation of detected text is divided into two modules Line segmentation and Character segmentation. Line segmentation is done by using mathematical statistical method based on projection profile analysis. In line segmentation, multiple lines of text in video frame obtained from text detection are segmented into single line. Character segmentation is done by using Connected Component. Analysis (CCA) and Vertical Projection Profile Analysis. The input for character segmentation is the line of text obtained from line segmentation, in which all the characters in the line are segmented separately for recognition. Optical character recognition is Processed by using template matching and correlation technique. Template matching is performed by comparing an input character with a set of templates, each comparison results in a similarity measure between the input characters with a set of templates. After all templates have been compared with the observed character image, the character’s identity is assigned with the most similar template based on correlation. Eventually, the text in video frame is detected, segmented, and processed to OCR for recognition.

Projection Profile Analysis for Identifying Different Views of Chest Radiographs

2006 ◽  
Vol 13 (4) ◽  
pp. 518-525 ◽  
Author(s):  
E.-Fong Kao ◽  
Chungnan Lee ◽  
Twei-Shiun Jaw ◽  
Jui-Sheng Hsu ◽  
Gin-Chung Liu
Keyword(s):  
Profile Analysis ◽  
Chest Radiographs ◽  
Projection Profile

Projection profile analysis for automated detection of abnormalities in chest radiographs

2005 ◽  
Vol 33 (1) ◽  
pp. 118-123 ◽  
Author(s):  
E-Fong Kao ◽  
Chungnan Lee ◽  
Jui-Sheng Hsu ◽  
Twei-Shiun Jaw ◽  
Gin-Chung Liu
Keyword(s):  
Profile Analysis ◽  
Automated Detection ◽  
Chest Radiographs ◽  
Projection Profile

An Edge-Based Algorithm for Text Extraction in Images and Video Frame

2011 ◽  
Vol 403-408 ◽  
pp. 900-907
Author(s):  
Anubhav Kumar ◽  
Awanish Kr Kaushik ◽  
R.L. Yadava ◽  
Divya Saxena
Keyword(s):  
Edge Detection ◽  
Text Detection ◽  
Detection Methods ◽  
Video Frame ◽  
Large Dataset ◽  
Projection Profile ◽  
The Past ◽  
Video Frames ◽  
Edge Based ◽  
Detection And Localization

In this paper, a proposal of a new and unusual framework to detect and extract the text from the images and video frames have been presented. In the past various methods have been presented for detection and localization of text in images and video frames. In this paper, a comparison has been made between several text detection methods and proposed method for text detection in images and video frames. The proposed method is carried out by edge detection, and the projection profile method is used to localize the text region better. Various experiments have been carried out to evaluate and compare the performance of the proposed algorithm. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical. Various parameters like average time, precision and recall rates and analyzed for both existing and proposed method to determine the success and limitation of our method.

Applications of SIMS to electronic materials and devices

1992 ◽  
Vol 50 (2) ◽  
pp. 1682-1683
Author(s):  
S.F. Corcoran
Keyword(s):  
Depth Distribution ◽  
Secondary Ion Mass Spectrometry ◽  
Semiconductor Device ◽  
Electronic Materials ◽  
Profile Analysis ◽  
Semiconductor Industry ◽  
Small Diameter ◽  
Depth Resolution ◽  
Detection Sensitivity

Over the past decade secondary ion mass spectrometry (SIMS) has played an increasingly important role in the characterization of electronic materials and devices. The ability of SIMS to provide part per million detection sensitivity for most elements while maintaining excellent depth resolution has made this technique indispensable in the semiconductor industry. Today SIMS is used extensively in the characterization of dopant profiles, thin film analysis, and trace analysis in bulk materials. The SIMS technique also lends itself to 2-D and 3-D imaging via either the use of stigmatic ion optics or small diameter primary beams.By far the most common application of SIMS is the determination of the depth distribution of dopants (B, As, P) intentionally introduced into semiconductor materials via ion implantation or epitaxial growth. Such measurements are critical since the dopant concentration and depth distribution can seriously affect the performance of a semiconductor device. In a typical depth profile analysis, keV ion sputtering is used to remove successive layers the sample.

Sign in / Sign up

Export Citation Format

Share Document