Computer Vision Based Orange Grading Using SVM

Fruit grading is very important for promoting its additional value. We graded oranges based on its images. Four photos were taken from different view angles for each orange. Both RGB and HSI color model were utilized. We extracted a 28-dimensional feature which can describe the size and color of them. Then support vector machine was used to grade these oranges into four levels. Experimental result shows SVM has promising performance for orange grading.

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Basketball shooting technology based on acceleration sensor fusion motion capture technology

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00731-9 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Binbin Zhao ◽

Shihong Liu

Keyword(s):

Computer Vision ◽

Support Vector Machine ◽

Motion Capture ◽

Visual Recognition ◽

Gaussian Mixture ◽

Human Vision ◽

Support Vector ◽

Background Removal ◽

Graphics Processing ◽

Basketball Shooting

AbstractComputer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.

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Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

Emitor: Jurnal Teknik Elektro ◽

10.23917/emitor.v17i2.6232 ◽

2017 ◽

Vol 17 (2) ◽

pp. 29-38

Author(s):

Ratih Purwati ◽

Gunawan Ariyanto

Keyword(s):

Computer Vision ◽

Support Vector Machine ◽

Face Recognition ◽

Local Binary Pattern ◽

Cross Validation ◽

Support Vector ◽

Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

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Pedestrian Crowd Detection Based Unmanned Aerial Vehicle Infrared Imagery

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.873.347 ◽

2017 ◽

Vol 873 ◽

pp. 347-352

Author(s):

Yong Hao Xiao ◽

Hong Zhen

Keyword(s):

Computer Vision ◽

Support Vector Machine ◽

Human Detection ◽

Support Vector ◽

Infrared Images ◽

Histogram Of Oriented Gradient ◽

Detection Approach ◽

Aerial Vehicle ◽

Geometric Filtering ◽

Pedestrian Crowd

Human detection is a keyproblem in computer vision. Recently, some research has been focusing on the detection ofpedestrianusing infrared images. The infrared images have outstanding merit. It depends only on object's temperature, but not on color or texture. In this paper, the pedestrian crowd detection approach is proposed. The approach is compose of ROI blocks extraction and crowd block recognition. ROI blocks can be extracted with circle gradient operator and weighted geometric filtering. Crowd blocks are recognized by support vector machine, which combines histogram of oriented gradient and circle gradient. The experimental results show thatthe approach works effectively in different scenes.

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Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021824553 ◽

2021 ◽

Vol 8 (2) ◽

pp. 311

Author(s):

Mohammad Farid Naufal

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Cross Validation ◽

Nearest Neighbors ◽

Support Vector ◽

Classification Algorithms ◽

K Nearest Neighbors

Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. Computer vision adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan self autonomous machine agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara real time. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942. AbstractWeather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.

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An Image Detection Method Based on Parameter Optimization of Support Vector Machine

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2021.15.35 ◽

2021 ◽

Vol 15 ◽

pp. 306-314

Author(s):

Zhanshen Feng

Keyword(s):

Image Processing ◽

Computer Vision ◽

Support Vector Machine ◽

Parameter Optimization ◽

Visual Processing ◽

Image Data ◽

Image Features ◽

Classification Model ◽

Support Vector ◽

Image Detection

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

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Predicting sensory evaluation of spinach freshness using machine learning model and digital images

PLoS ONE ◽

10.1371/journal.pone.0248769 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248769

Author(s):

Kento Koyama ◽

Marin Tanaka ◽

Byeong-Hyo Cho ◽

Yusaku Yoshikawa ◽

Shige Koseki

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Sensory Evaluation ◽

Color Space ◽

Local Features ◽

Support Vector ◽

Nondestructive Technique ◽

Color Features ◽

Artificial Neural ◽

Four Levels

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.

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Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽

10.1371/journal.pone.0242899 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242899

Author(s):

Musatafa Abbas Abbood Albadr ◽

Sabrina Tiun ◽

Masri Ayob ◽

Fahad Taha AL-Dhief ◽

Khairuddin Omar ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Support Vector Machine ◽

Ct Scan ◽

Extreme Learning Machine ◽

Experimental Result ◽

Support Vector ◽

X Ray ◽

Chest X Ray ◽

Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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