Color classification method of natural scene based on fuzzy inference

2020 ◽  
Author(s):  
Xiaofei Sun ◽  
连明 王
Keyword(s):  
Fuzzy Inference ◽  
Classification Method ◽  
Natural Scene ◽  
Color Classification

Identifikasi Beras Berdasarkan Warna Menggunakan Adaptive Neuro Fuzzy Inference System

2018 ◽  
Vol 5 (2) ◽  
pp. 51-59
Author(s):  
Muhammad Gilang Alfianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Fuzzy Inference ◽  
Primary Data ◽  
Observation Data ◽  
Fuzzy Inference Systems ◽  
Food Ingredient ◽  
Color Classification ◽  
Inference System ◽  
Neuro Fuzzy ◽  
The Government ◽  
Inference Systems

ABSTRACT   Rice is the main food ingredient for Indonesian people. Through the National Standardization Agency, The Government has established a general requirement of rice, that is good quality rice which has a white color of whitewashed and low-quality rice which has a yellowish color (SNI 6128: 2015). To determine the different color of good quality rice and the low-quality one it often happens of wrong identification caused by different perception on the color. This problem can be solved by creating the system to identify good quality rice of IR64 and the low-quality one. The data used are primary data, in the form of good quality rice with grain image of 50 and the low-quality one is 50. The observation data used for trial is La * b * and Sa * b * using Adaptive Neuro-Fuzzy Inference Systems ( ANFIS). The observation variable Sa * b * produce higher identification compared to La*b*, with accuracy value is  90%.   Keyword : rice, quality,color,classification   ABSTRAK   Beras merupakan bahan pangan utama masyarakat Indonesia. Pemerintah melalui badan Standarisasi Nasional telah menetapkan syarat umum beras, yaitu beras berkualitas baik yang mempunyai warna putih mengapur dan beras berkualitas  rusak yang mempunyai warna kekuningan (SNI 6128:2015). Untuk menentukan perbedaan warna beras berkualitas baik dan rusak seringkali terjadi kesalahan identifikasi yang dikarenakan perbedaan persepsi warna. Hal tersebut dapat diatasi dengan membuat sistem untuk mengidentifikasi butir beras IR 64 yang berkualitas baik dan rusak. Data yang digunakan adalah data primer, yang berupa gambar butir beras berkualitas baik sebanyak 50 dan butir beras beras berkualitas rusak sebanyak 50. Variabel penduga yang dicobakan adalah La*b* dan Sa*b* dengan menggunakan metode Adaptive Neuro Fuzzy Inference Systems (ANFIS). Variabel penduga Sa*b* menghasilkan identifikasi yang lebih tinggi dibandingakan La*b* dengan nilai akurasi sebesar 90%.   Kata kunci : beras, kualitas,warna,klasifikasi

scholarly journals Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Chuncheng Zhang ◽  
Shuang Qiu ◽  
Shengpei Wang ◽  
Huiguang He
Keyword(s):  
Target Detection ◽  
Rapid Serial Visual Presentation ◽  
High Speed ◽  
Neural Control ◽  
Visual Presentation ◽  
Event Related Potential ◽  
New Method ◽  
Classification Method ◽  
Computer Interface ◽  
Natural Scene

Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain–computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm.Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target.Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from others, and the separation significantly increased the ERP detection scores in the EEGNet classifier. Moreover, the visualization of the new method suggested the different underling of SVM and EEGNet classifiers in ERP detection of the RSVP experiment.Conclusion: In the RSVP experiment, the near-non-target samples contain separable ERP activity. The ERP detection scores can be increased using classifiers of the EEGNet model, by separating the non-target into near- and far-targets based on their delay against targets.

Mathematical Morphology Based Image Segmentation and Character String Extraction Using Fuzzy Inference

2015 ◽  
Vol 19 (4) ◽  
pp. 544-554
Author(s):  
Jianjun Chen ◽  
◽  
Noboru Takagi
Keyword(s):  
Visually Impaired ◽  
Fuzzy Inference ◽  
Natural Scene ◽  
Public Places ◽  
Character String ◽  
Visually Impaired People ◽  
Impaired People ◽  
Reading Text ◽  
String Extraction ◽  
Natural Scene Images

Signs are ubiquitous indoors and outdoors, and they are often used for finding public places and other locations. However, information on signs is inaccessible to many visually impaired people, unless represented non-visually such as with Braille, tactile graphics, or speech. Automatically reading text from signs in natural scene images is a vital application for assisting visually impaired people. However, finding text in scene images is a great challenge because it cannot be assumed that the acquired image contains only characters. Natural scene images usually contain diverse text in different sizes, styles, fonts, and colors, and complex backgrounds. Therefore, we turn to the development of a portable camera-based assistive system to aid visually impaired people reading text from natural scenery. In this paper, a new method for character string extraction from scene images is discussed. The algorithm is implemented and evaluated using a set of natural scene images. Accuracy, precision, and recall rates of the proposed method are calculated and analyzed to determine success and limitations. Recommendations for improvements are given based on the results.

scholarly journals A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2899
Author(s):  
Tingting Zhu ◽  
Kun Ding ◽  
Zhenye Li ◽  
Xianxu Zhan ◽  
Rong Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decisive Role ◽  
Solid Wood ◽  
Great Accuracy ◽  
Classification Method ◽  
Color Classification ◽  
Feature Extraction Method ◽  
Stochastic Sampling ◽  
Online Classification ◽  
Execution Speed

Solid wood floors are widely used as an interior decoration material, and the color of solid wood surfaces plays a decisive role in the final decoration effect. Therefore, the color classification of solid wood floors is the final and most important step before laying. However, research on floor classification usually focuses on recognizing complex and diverse features but ignores execution speed, which causes common methods to not meet the requirements of online classification in practical production. In this paper, a new online classification method of solid wood floors was proposed by combining probability theory and machine learning. Firstly, a probability-based feature extraction method (stochastic sampling feature extractor) was developed to obtain rapid key features regardless of the disturbance of wood grain. The stochastic features were determined by a genetic algorithm. Then, an extreme learning machine—as a fast classification neural network—was selected and trained with the selected stochastic features to classify solid wood floors. Several experiments were carried out to evaluate the performance of the proposed method, and the results showed that the proposed method achieved a classification accuracy of 97.78% and less than 1 ms for each solid wood floor. The proposed method has advantages including a high execution speed, great accuracy, and flexible adaptability. Overall, it is suitable for online industry production.

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

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