Fast Iris Segmentation Algorithm for Visible Wavelength Images Based on Multi-color Space

2020 ◽  
pp. 239-250
Author(s):  
Shaaban Sahmoud ◽  
Hala N. Fathee
Keyword(s):  
Color Space ◽  
Segmentation Algorithm ◽  
Iris Segmentation ◽  
Visible Wavelength

An iris segmentation algorithm based on edge orientation for off-angle iris recognition

2013 ◽  
Author(s):  
Mahmut Karakaya ◽  
Del Barstow ◽  
Hector Santos-Villalobos ◽  
Christopher Boehnen
Keyword(s):  
Iris Recognition ◽  
Segmentation Algorithm ◽  
Iris Segmentation ◽  
Edge Orientation

scholarly journals A Neural-Network-Based Approach to White Blood Cell Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mu-Chun Su ◽  
Chun-Yen Cheng ◽  
Pa-Chun Wang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification System ◽  
Blood Cells ◽  
White Blood Cells ◽  
Recognition Rate ◽  
Color Space ◽  
Segmentation Algorithm ◽  
Cell Classification ◽  
Morphological Process

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

Extracting Golden Area from Image Based on Canny Operator

2015 ◽  
Vol 731 ◽  
pp. 201-204
Author(s):  
Ying Wu ◽  
Xiu Ping Zhao ◽  
Yang Jin ◽  
Xi Zhang
Keyword(s):  
Image Segmentation ◽  
Color Space ◽  
Threshold Value ◽  
Segmentation Algorithm ◽  
Canny Operator ◽  
Small Areas ◽  
Color Separation ◽  
Lab Color Space ◽  
Canny Algorithm ◽  
Image Segmentation Algorithm

This paper researched application of Canny algorithm on the color separation of golden image , to generate a separated golden image plate base on the extraction of golden area, so as to get the effect more closer to the real metallic. Canny algorithm is based on the gray-scale image segmentation algorithm. The image is mapped from RGB to Lab color space. According to the color attributes of b, the golden target regions are extracted using Canny algorithm. But it’s difficult to get the closed target boundary outlet by Canny algorithm, so this paper modified image segmentation algorithm. Firstly, the image is filtered by Canny operator; secondly, small areas on the Canny processed image are removed by using some pre-determined threshold value.; then processed the image through using smoothing and sharping method so to make inner area of image more smooth meanwhile improving boundary sharpness. The experimental results showed that the method based on Canny operator is very suitable for golden area extraction from a image. The golden target-regions can be closed boundary outlet, which makes the golden areas are more accurate and continuous.

scholarly journals A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Félix Fuentes-Hurtado ◽  
Valery Naranjo ◽  
Jose Antonio Diego-Mas ◽  
Mariano Alcañiz
Keyword(s):  
Hybrid Method ◽  
Iris Segmentation ◽  
Visible Wavelength

Fast and reliable iris segmentation algorithm

2013 ◽  
Vol 7 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Abduljalil Radman ◽  
Nasharuddin Zainal ◽  
Kasmiran Jumari
Keyword(s):  
Segmentation Algorithm ◽  
Iris Segmentation

scholarly journals Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1634 ◽  
Author(s):  
Sajad Sabzi ◽  
Yousef Abbaspour-Gilandeh ◽  
Ginés García-Mateos ◽  
Antonio Ruiz-Canales ◽  
José Miguel Molina-Martínez
Keyword(s):  
Precision Agriculture ◽  
Irrigation Management ◽  
Color Space ◽  
Texture Features ◽  
Apple Fruit ◽  
Segmentation Algorithm ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Local Standard ◽  
Image Pixels

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

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