Research on computer vision enhancement in intelligent robot based on machine learning and deep learning

2021 ◽  
Author(s):  
Yuhan Ding ◽  
Lisha Hua ◽  
Shunlei Li
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Intelligent Robot

scholarly journals Implementation of conventional communication system in deep learning

2017 ◽  
Vol 7 (1.1) ◽  
pp. 696
Author(s):  
Satyanarayana P ◽  
Charishma Devi. V ◽  
Sowjanya P ◽  
Satish Babu ◽  
N Syam Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Communication Network ◽  
Deep Learning ◽  
Communication System ◽  
Communication Systems ◽  
Physical Layer ◽  
Learning Capacity ◽  
Constrained Learning ◽  
Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.  

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

scholarly journals Penerapan Computer Vision Menggunakan Metode Deep Learning pada Perspektif Generasi Ulul Albab

2021 ◽  
Vol 7 (2) ◽  
pp. 98-107
Author(s):  
Imamul Arifin ◽  
Reydiko Fakhran Haidi ◽  
Muhammad Dzalhaqi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Library Research

Machine learning merupakan salah satu penerapan kecerdasan buatan. Penggunaan machine learning pada computer vision erat berkaitan dengan deep learning yang mana para ilmuwan komputer mendapatkan inspirasi mengenai teknologi deep learning dari alam sekitar. Tujuan penelitian pada naskah ini adalah Mengetahui dan memahami teknologi deep learning beserta contoh sederhana dalam pemrosesan objek gambar dan Mengetahui dan memahami teknologi kecerdasan buatan dalam perspektif generasi ulul albab sehingga bisa memberikan manfaat secara menyeluruh bagi dunia. Penelitian yang dilakukan pada karya tulis ini merupakan jenis penelitian kualitatif dengan metode studi pustaka (library research) menggunakan berbagai buku dan literatur bacaan lainnya seperti jurnal dan website khusus sehingga menghasilkan informasi dari topik yang diteliti. Teknologi kecerdasan buatan akan selalu berkembang dan menuju arah yang semakin canggih, tetapi teknologi juga mempunyai dampak negatif. Generasi Ulul Albab harus bisa berjuang untuk memberikan dampak positif bagi masyarakat karena sejatinya generasi ulul albab adalah harapan kemajuan peradaban islam di berbagai sektor ilmu pengetahuan dan teknologi.

scholarly journals Development of the "smart library" application using the Intel Distribution of OpenVINO toolkit

2019 ◽  
Author(s):  
Евгений Васильев ◽  
Evgeniy Vasil'ev ◽  
Валентина Кустикова ◽  
Valentina Kustikova ◽  
Иван Вихрев ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Source Code ◽  
Face Features ◽  
Machine Learning Model ◽  
Library Users ◽  
The Face

We represent a case study of using deep learning and computer vision library - the Intel Distribution of OpenVINO toolkit. We develop the automated “smart library” using DL and computer vision methods implemented in OpenVINO toolkit. The application involves the registration of the reader (adding information and photos of the new user); updating the machine learning model that describes the face features of the library users; authorization of the reader through face recognition; receiving and returning books by comparing the cover image with the database of flat images available in the library of books. The source code of the application is free available on GitHub: https://github.com/itlab-vision/openvino-smart-library. The developed application is planned to be published as a sample of the OpenVINO toolkit.

scholarly journals PERANCANGAN APLIKASI MOBILE UNTUK KLASIFIKASI SAYURAN MENGGUNAKAN DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 300-306
Author(s):  
Muhamad Jaelani Akbar ◽  
Mochamad Wisuda Sardjono ◽  
Margi Cahyanti ◽  
Ericks Rachmat Swedia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Test Accuracy ◽  
Final Test ◽  
Testing Data

Sayuran merupakan sebutan bagi bahan pangan asal tumbuhan yang biasanya mengandung kadar air tinggi dan dikonsumsi dalam keadaan segar atau setelah diolah secara minimal. Keanekaragaman sayur yang terdapat di dunia menyebabkan keragaman pula dalam pengklasifikasian sayur. Oleh karena itu diperlukan adanya pendekatan digital agar dapat mengenali jenis sayuran dengan cepat dan mudah. Dalam penelitian ini jumlah jenis sayuran yang digunakan sebanyak 7 jenis diantara: brokoli, jagung, kacang panjang, pare, terung ungu, tomat dan kubis. Dataset yang digunakan berjumlah 941 gambar sayur dari 7 jenis sayur, ditambah 131 gambar sayur dari jenis yang tidak terdapat pada dataset, selain itu digunakan 291 gambar selain sayuran. Untuk melakukan klasifikasi jenis sayuran digunakan algoritme Convolutional Neural Network (CNN), yang merupakan salah satu bidang ilmu baru dalam Machine Learning dan berkembang dengan pesat. CNN merupakan salah satu algoritme yang terdapat pada metode Deep Learning dengan memiliki kemampuan yang baik dalam Computer Vision, salah satunya yaitu image classification atau klasifikasi objek citra. Uji coba dilakukan pada lima perangkat selular berbasiskan sistem operasi Android. Python digunakan sebagai bahasa pemrograman dalam merancang aplikasi mobile ini dengan menggunakan modul Tensor flow untuk melakukan training dan testing data. Metode yang dapat digunakan dalam melakukan klasifikasi citra ini yaitu Convolutional Neural Network (CNN). Hasil final test accuracy yang diperoleh yaitu didapat keakuratan mengenali jenis sayuran sebesar 98.1% dengan salah satu hasil pengujian yaitu klasifikasi sayur jagung dengan akurasi sebesar 99.98049%.

scholarly journals Traffic Sign Classification Using Convolutional Neural Networks and Computer Vision

2021 ◽  
Vol 12 (3) ◽  
pp. 4244-4250
Author(s):  
Anuraag Velamati Et.al
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Human Beings ◽  
Traffic Sign ◽  
The World ◽  
Traffic Sign Classification

The world is quickly and continuously advancing towards better technological advancements that will make life quite easier for us, human beings [22]. Humans are looking for more interactive and advanced ways to improve their learning. One such dream is making a machine think like a computer, which lead to innovations like AI and deep learning [25]. The world is running at a higher pace in the domain of AI, deep learning, robotics and machine learning Using this knowledge and technology, we could develop anything right now [36]. As a part of sub-domain, the introduction of Convolution Neural Networks made deep learning extensively strong in the domain of image classification and detection [1]..The research that we have conducted is one of its kind. Our research used Convolution Neural Network, TensorFlow and Keras.

scholarly journals A Study of incremental Learning model using deep neural network

2021 ◽  
Vol 10 (2) ◽  
pp. 658-663
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Supervised Learning ◽  
Incremental Learning ◽  
Deep Neural Network ◽  
Learning Model

Deep learning has arrived with a great number of advances in the research of machine learning and its models. Due to the advancements recently in the field of deep learning and its models especially in the fields like NLP and Computer Vision in supervised learning for which we have to pre-definably decide a dataset and train our model completely on it and make predictions but in case if we have any new samples of data on which we want our model to be predicted then we have to completely retrain the model, which is computationally costly therefore to avoid re-training the model, we add the new samples on the previously learnt features from the pre- trained model called Incremental Learning. In the paper we proposed the system to overcome the process of catastrophic forgetting we introduced the concept of building on pre-trained model.

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