scholarly journals Distance transform-watershed segmentation and multi-layer perceptron algorithm to separate touching orange fruit in digital images

2021 ◽  
Vol 922 (1) ◽  
pp. 012047
Author(s):  
I S Nasution ◽  
C Keke
Keyword(s):  
National Standard ◽  
Distance Transform ◽  
Multi Layer Perceptron ◽  
Citrus Fruits ◽  
Watershed Segmentation ◽  
Corn Kernel ◽  
Perceptron Algorithm ◽  
Class A ◽  
Orange Fruit ◽  
Accuracy Rates

Abstract An algorithm to separate touching oranges using a distance transform-watershed segmentation is presented. In this study, there are four classes of oranges, such as class A, B, C, and D, respectively. The size of each class is based on the Indonesian National Standard (SNI), the sample used is 168 oranges of which 140 are for training and 28 oranges are for testing. The image of citrus fruits was captured using Kinect v2 camera with a camera resolution of 1920 × 1080 pixels, the distance from the camera to the background is 23 cm. The images were captured in PNG format. The watersheds were computed based on the distance transformed by orange regions. The corresponding basins were finally used to split the falsely connected corn kernel by intersecting the basins with the corn kernel regions. Experimental results show that the multi-layer perceptrons have classification accuracy rates of 92.85%. The algorithm appears to be robust enough to separate most of the multiple touching scenarios.

scholarly journals HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA

2018 ◽  
Vol 10 (2) ◽  
pp. 81-86
Author(s):  
Marlinda Vasty Overbeek
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Moving Objects ◽  
Image Data ◽  
Gradient Algorithm ◽  
Multi Layer Perceptron ◽  
Histogram Of Oriented Gradient ◽  
Perceptron Algorithm ◽  
Feature Extracting

This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.

scholarly journals Planning And Manufacture Of Sortir Fruit Orenge System

2018 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Muhamad Taufiq Tamam
Keyword(s):  
Citrus Fruit ◽  
High Accuracy ◽  
Laser Sensor ◽  
Test Results ◽  
Citrus Fruits ◽  
Photo Diode ◽  
Long Time ◽  
Small Test ◽  
Orange Fruit ◽  
Size Dimension

One application of electronics technology in the field of agriculture is to process the yield of citrus fruits, to choose oranges based on size / dimension. If using manpower (manual) requires high accuracy and takes a long time. By using electronics technology it can be eliminated. The aim of this research is to make prototype of sorting tool that can choose orange fruit based on dimension / size. By using laser sensor and photo diode (photo diode) function to know dimension / size of citrus fruit, citrus fruit will be separated according to dimension / size, big or small. Test results show that this tool can work well in accordance with the planned

Line Parameter based Word-Level Indic Script Identification System

2016 ◽  
Vol 6 (2) ◽  
pp. 18-41 ◽  
Author(s):  
Pawan Kumar Singh ◽  
Supratim Das ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Hough Transform ◽  
Identification Accuracy ◽  
Distance Transform ◽  
Multi Layer Perceptron ◽  
Script Identification ◽  
Spatial Features ◽  
Word Level ◽  
Line Parameter ◽  
Word Images ◽  
Mlp Classifier

In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Manipuri, Oriya, Urdu, and Roman. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentations are performed at word-level using multiple classifiers on a dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%. The performance of the present technique is also compared with those of other state-of-the-art script identification methods on the same database. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentation are performed at word-level on a total dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%.

Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
A.B.M. Wijaya ◽  
D.S. Ikawahyuni ◽  
Rospita Gea ◽  
Febe Maedjaja
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
The Other ◽  
Multi Layer Perceptron ◽  
Initial Weight ◽  
Restricted Boltzmann Machines ◽  
Regression Problem ◽  
Perceptron Algorithm ◽  
Other Hand ◽  
The Neural Network

Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm puts Restricted Boltzmann Machines (RBM) into a stacked process. The output of the first RBM will be the input for the next RBM. On the other hand, the NEAT algorithm works by investigating the neural network architecture and evaluating the architecture using a multi-layer perceptron algorithm. Collaboration with a Genetic Algorithm in NEAT is the key process in architecture development. The research results showed that DBN could be utilized as the initial weight for Backpropagation Neural Network at 22.61% on average. On the other hand, the NEAT algorithm could be used by collaborating with a multi-layer perceptron to solve this regression problem by providing 74.5% confidence. This work also reveals potential works in the future by combining the Backpropagation algorithm with NEAT as an evaluation function and by combining it with DBN algorithms to process the produced initial weight.

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