Image Processing-Based Supervised Learning to Predict Robot Intention for Multimodal Interactions Between a Virtual Human and a Social Robot

2021 ◽  
pp. 533-539
Author(s):  
S. M. Mizanoor Rahman
Keyword(s):  
Image Processing ◽  
Supervised Learning ◽  
Social Robot ◽  
Virtual Human ◽  
Multimodal Interactions

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

Detecting Edge Using Support Vector Machine

2012 ◽  
Vol 588-589 ◽  
pp. 974-977 ◽  
Author(s):  
Jih Pin Yeh
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Edge Detection ◽  
Supervised Learning ◽  
Classical Method ◽  
Support Vector ◽  
Learning Method ◽  
Edge Detector

The edge detection is used in many applications in image processing. It is currently crucial technique of image processing. There are various methods for promoting edge detection. Here, it is presented that edge detection can be achieved using Support Vector Machine (SVM). Supervised learning method is applied. Laplacian edge detector is an instructor of Support Vector Machine. In this research, it is presented that any classical method can be applied for training of SVM as edge detector.

scholarly journals Skin Cancer Recognition Using SVM Image Processing Technique

2020 ◽  
pp. 11-15
Author(s):  
Rahul Chand Thakur ◽  
◽  
Vaibhav Panwar ◽  
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Supervised Learning ◽  
Detection System ◽  
Early Stage ◽  
Image Features ◽  
The Body ◽  
Image Processing Technique ◽  
Support Vector ◽  
Skin Cells

Skin cancer is considered as commonest cause of death among humans in today's world. This type of cancer shows non uniform or patchy growth of skin cells that most commonly occurs on of the certain parts of body which are more likely exposed to the light, but it can occur anywhere on the body. The majority of skin cancers can be treated if detected early. As a result, finding skin cancer early and easily will save a patient's life. Early detection of skin cancer at an early stage is now possible thanks to modern technologies. Biopsy procedure [1] is a systematic method for diagnosis skin cancer. It is achieved by extracting skin cells, after which the sample is sent to different laboratories for examination. It's a very long (in terms of time) and painful process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on svm. It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called Support Vector Machine (SVM) are used in the identification process. Epiluminescence microscopy is taken using an image and particular to several preprocessing techniques which are used in the reduction of sound artifacts and improvise quality of images. Segmentation is done by using certain thresholding techniques like OTSU. The GLCM technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The Supervised learning model called (SVM) is used to distinguish data sets. It determines whether a picture is cancerous or not.

Image processing sayatan tipis batuan gamping dengan jaringan syaraf tiruan (jst) menggunakan matlab dan linux (studi kasus : rajamandala-padalarang

2015 ◽  
Vol 12 (1) ◽  
Author(s):  
John Adler
Keyword(s):  
Image Processing ◽  
Supervised Learning ◽  
Thin Section ◽  
Rock Physics ◽  
Feed Forward ◽  
Levenberg Marquardt

Batu gamping dikenal sebagai batuan karbonat adalah salah satu kelas batuan sedimen yang mineral pembentuknya (sebesar 95% atau lebih) adalah calcite (CaCO3, kalsium karbonat), dolomite (CaMg(CO3)2) dan aragonite. Batuan kar-bonat ini menjadi sangat penting karena lebih dari 50% reservoar minyak dan gas di dunia adalah reservoar karbonat. Namun tantangannya adalah ketidak-teraturan dan kompleksitas struktur geometri pori karbonat dan frame (rangka) yang bisa teralterasi (berubahnya komposisi mineral batuan dan struktur ki-mianya).Pada penelitian ini, batuan akan dikarakterisasi menggunakan thin section (sayatan tipis) untuk mendapatkan gambar (image) berukuran 1000 mikrometer dengan bantuan mikroskop electron. Dengan metode baru, akan dikaji fenomena Seismic Rock Physics pada thin section yaitu menghitung kecepatan gelombang elastic pada suatu pori-pori batuan menggunakan Jaringan Syaraf Tiruan Backpropagation, merupakan salah satu jenis Jaringan Syaraf Tiruan (JST) feed forward dengan proses belajar dibimbing (supervised learning), ber-fungsi memilah-milah citra warna yang terdapat dalam image (metoda RGB, Red-Green-Blue). Metode pembelajaran yang dipakai adalah metode Levenberg-Marquardt.

Supervised Neural Networks for the Automatic Classification of Leukocytes in Blood Microscope Images

2013 ◽  
Vol 479-480 ◽  
pp. 491-495 ◽  
Author(s):  
Sheng Fuu Lin ◽  
Chien Hao Tseng ◽  
Chung I Huang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Supervised Learning ◽  
Automatic Classification ◽  
Learning System ◽  
Automated Segmentation ◽  
Classification Learning ◽  
Microscope Images ◽  
Traditional Pattern

In this paper, the application of the supervised learning system to automatic classification of leukocytes processing for the microscopic images analysis is presented. The traditional pattern classification in cellular images is typically made by experienced operators. Such procedures may present a non-standard and unstable accuracy when it depends on the operator’s capabilities and tiredness. In this study, we propose the supervised learning system to achieve an automated segmentation and classification of leukocytes based on supervised neural networks and image processing methods. The experimental results show that the proposed automatic classification learning system can effectively classify the five types of the leukocytes in microscopic cell images, as well as to compare the classification results to those obtained by the medical experts.

Image processing based on supervised learning in neural networks

1991 ◽  
Author(s):  
Akira Hasegawa ◽  
Wei Zhang ◽  
Kazuyoshi Itoh ◽  
Yoshiki Ichioka
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Supervised Learning
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