New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques

2018 ◽  
Vol 30 (2) ◽  
pp. 571-589 ◽  
Author(s):  
Nashat M. Hussain Hassan ◽  
Ahmed A. Nashat
Keyword(s):  
Image Processing ◽  
Automatic Detection ◽  
Image Processing Techniques ◽  
Processing Techniques

Automatic Detection and Classification of Brain Tumor using Image Processing Techniques

2017 ◽  
Vol 10 (11) ◽  
pp. 3692
Author(s):  
T. Sudhakar ◽  
J Bethanney Janney ◽  
D Haritha ◽  
M Juliet Sahaya ◽  
V Parvathy
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Automatic Detection ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Computational Intelligence ◽  
Asphalt Pavement ◽  
Classification Model ◽  
Laplacian Pyramid ◽  
Pavement Condition ◽  
Crack Patterns ◽  
Image Processing Techniques ◽  
Processing Techniques

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

scholarly journals Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

2018 ◽  
pp. 57-61
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas
Keyword(s):  
Image Processing ◽  
Fuzzy System ◽  
Absolute Error ◽  
Data Set ◽  
Wheat Grains ◽  
Image Processing Techniques ◽  
Neuro Fuzzy ◽  
Length Width ◽  
Processing Techniques

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

The Classification of Skateboarding Trick Manoeuvres Through the Integration of Image Processing Techniques and Machine Learning

2020 ◽  
pp. 347-356
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Determination and Classification of Blood Types using Image Processing Techniques

2017 ◽  
Vol 157 (1) ◽  
pp. 12-16 ◽  
Author(s):  
G. Ravindran ◽  
T. Joby ◽  
M. Pravin ◽  
P. Pandiyan
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Blood Types ◽  
Processing Techniques
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