Coding of image data via correlation filters for invariant pattern recognition: Some practical results

1996 ◽  
pp. 269-277
Author(s):  
Jonny Gauvin ◽  
Michel Doucet ◽  
Denis Gingras ◽  
Paul Chevrette
Keyword(s):  
Pattern Recognition ◽  
Image Data ◽  
Correlation Filters

scholarly journals Real-Time Pattern Recognition with Adaptive Correlation Filters

10.5772/4977 ◽  
2007 ◽  
Author(s):  
Vitaly Kober ◽  
Victor H. ◽  
J. Angel ◽  
Josue Alvarez-Borrego
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Correlation Filters ◽  
Time Pattern

Nonconvex 3D array image data recovery and pattern recognition under tensor framework

2022 ◽  
Vol 122 ◽  
pp. 108311
Author(s):  
Ming Yang ◽  
Qilun Luo ◽  
Wen Li ◽  
Mingqing Xiao
Keyword(s):  
Pattern Recognition ◽  
Image Data ◽  
Data Recovery

Circular harmonic correlation filters for rotationally invariant color pattern recognition

1990 ◽  
Author(s):  
Juan C. Campos Rubio ◽  
Salvador Bosch ◽  
Maria S. Millan Garcia-Verela ◽  
Carlos Ferreira ◽  
Maria J. Yzuel
Keyword(s):  
Pattern Recognition ◽  
Color Pattern ◽  
Correlation Filters ◽  
Rotationally Invariant

Techniques for the Automated Segmentation of Lung in Thoracic Computed Tomography Scans

2013 ◽  
pp. 675-687
Author(s):  
William F. Sensakovic ◽  
Samuel G. Armato
Keyword(s):  
Computed Tomography ◽  
Image Data ◽  
Segmentation Method ◽  
Correlation Filters ◽  
Lung Segmentation ◽  
Thoracic Diseases ◽  
Thoracic Computed Tomography ◽  
Segmentation Accuracy ◽  
Computed Tomography Scans ◽  
Computer Aided

Computed Tomography (CT) is widely used to diagnose and assess thoracic diseases. The improved resolution of CT studies has resulted in a substantial increase of image data for analysis by radiologists. The time-consuming nature of this analysis motivates the application of Computer-Aided Diagnostic (CAD) methods to assist radiologists. Most CAD methods require identification of the lung within the patient images, a preprocessing step known as “lung segmentation.” This chapter describes an intensity-based lung segmentation method. The segmentation method begins with simple thresholding, and several image processing modules are included to improve segmentation accuracy and robustness. Common segmentation difficulties are discussed and motivate the inclusion of each module in the lung segmentation method. These modules will include brief explanations of common techniques (e.g., morphological operators) in addition to novel techniques developed specifically for lung segmentation (e.g., gradient correlation filters).

scholarly journals Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

2015 ◽  
Vol 338 ◽  
pp. 77-89 ◽  
Author(s):  
Victor H. Diaz-Ramirez ◽  
Andres Cuevas ◽  
Vitaly Kober ◽  
Leonardo Trujillo ◽  
Abdul Awwal
Keyword(s):  
Pattern Recognition ◽  
Combinatorial Optimization ◽  
Correlation Filters ◽  
Multi Objective
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