Modeling biological visual processes for improved contrast enhancement and edge detection of artificial visual systems

1996 ◽  
Author(s):  
David L. Enke ◽  
Cihan H. Dagli
Keyword(s):  
Edge Detection ◽  
Contrast Enhancement ◽  
Visual Processes ◽  
Visual Systems

Research on Carboform Nondestructive Examination Based on Infrared Thermal Wave

2011 ◽  
Vol 271-273 ◽  
pp. 177-180
Author(s):  
Hai Feng Chang
Keyword(s):  
Edge Detection ◽  
Contrast Enhancement ◽  
Thermal Performance ◽  
Thermal Wave ◽  
Thermal Imaging ◽  
Median Filter ◽  
Performance Difference ◽  
Infrared Thermal Imaging ◽  
Nondestructive Examination ◽  
Infrared Thermal Images

Due to special characteristics of Carboform material, there are many difficulties to exam such material with traditional methods. Infrared thermal imaging technology shoots carboform to obtain infrared thermal images. With variation of time and temperature, the change principle of thermal performance difference of carboform in different temperature can be compared and analyzed. Effective data and reasonable fitting time can be extracted to fit for data with power exponential function. Then, imaging functions were utilized to perform gray change, median filter, fuzzy contrast enhancement, edge detection so as to output images on fitted data. Defects of specimen can be found. Example of some carboform sample based on infrared thermal wave verified feasibility of the proposed method.

scholarly journals An edge detection mechanism using L*A*B color-based contrast enhancement for underwater images

2020 ◽  
Vol 18 (1) ◽  
pp. 41
Author(s):  
M Sudhakara ◽  
M Janaki Meena
Keyword(s):  
Edge Detection ◽  
Contrast Enhancement ◽  
Color Change ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Detection Algorithm ◽  
Input Image ◽  
Underwater Imaging ◽  
Detection Mechanism ◽  
Edge Detection Algorithm

In Ocean investigations, particularly those deployed by the Autonomous Underwater Vehicles, underwater object detection and recognition is an essential task. Edge detection places a key role and considered one of the pre-processing techniques for several deep learning applications. In an underwater environment, the illumination of light, turbulence in the water, suspended particles present in the seafloor are challenging issues to acquire the quality image. The two major problems in underwater imaging are light scattering and color change. In the former case, the vision sensors connected to the underwater vehicles or dive lights used by the divers themselves cause light dispersion and shadows in the seafloor. In the latter case, the occurrence of color distortion is mainly due to the attenuation of the light, hence the images are having dominant colors in the latter case. The conventional techniques are failed to detect the quality edges in the case of underwater images. Our mechanism focused, instead of applying the edge detection algorithm on the input image directly, it is better to apply edge detection algorithm after color correction and contrast enhancement using L*A*B model. Qualitative and quantitative test results demonstrate that the proposed mechanism is giving better results compared with state-of-the-art methods.

scholarly journals A Study of Contrast Enhancement by Image Fusion using Edge Detection Techniques

2015 ◽  
Vol 122 (13) ◽  
pp. 27-31
Author(s):  
Pallabi Ghosh ◽  
Debanjana Dasgupta ◽  
Debalina Ghosh
Keyword(s):  
Edge Detection ◽  
Image Fusion ◽  
Contrast Enhancement ◽  
Detection Techniques

Image Edge Detection Using Fractional Calculus with Feature and Contrast Enhancement

2018 ◽  
Vol 37 (9) ◽  
pp. 3946-3972 ◽  
Author(s):  
Amita Nandal ◽  
Hamurabi Gamboa-Rosales ◽  
Arvind Dhaka ◽  
Jose M. Celaya-Padilla ◽  
Jorge Issac Galvan-Tejada ◽  
...  
Keyword(s):  
Fractional Calculus ◽  
Edge Detection ◽  
Contrast Enhancement ◽  
Image Edge Detection ◽  
Image Edge

An Energy Analyser for Contrast Enhancement in STEM

1974 ◽  
Vol 32 ◽  
pp. 434-435
Author(s):  
H.T. Pearce-Percy
Keyword(s):  
Contrast Enhancement ◽  
Bending Angle ◽  
Magnetic Sector ◽  
Conventional Design ◽  
Bending Radius ◽  
Sector Type

Recently an energy analyser of the uniform magnetic sector type has been installd in a 100KV microscope. This microscope can be used in the STEM mode. The sector is of conventional design (Fig. 1). The bending angle was chosen to be 90° for ease of construction. The bending radius (ρ) is 20 cm. and the object and image distances are 42.5 cm. and 30.0 cm. respectively.

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