scholarly journals Artistic Style Meets Artificial Intelligence

2021 ◽  
Author(s):  
Suk Kyoung Choi ◽  
Steve DiPaola ◽  
Hannu Töyrylä
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Lived Experience ◽  
Creative Expression ◽  
Visual Artists ◽  
Image Transformation ◽  
Artistic Style ◽  
Latent Space ◽  
Recent Developments

Recent developments in neural network image processing motivate the question, how these technologies might better serve visual artists. Research goals to date have largely focused on either pastiche interpretations of what is framed as artistic “style” or seek to divulge heretofore unimaginable dimensions of algorithmic “latent space,” but have failed to address the process an artist might actually pursue, when engaged in the reflective act of developing an image from imagination and lived experience. The tools, in other words, are constituted in research demonstrations rather than as tools of creative expression. In this article, the authors explore the phenomenology of the creative environment afforded by artificially intelligent image transformation and generation, drawn from autoethnographic reviews of the authors’ individual approaches to artificial intelligence (AI) art. They offer a post-phenomenology of “neural media” such that visual artists may begin to work with AI technologies in ways that support naturalistic processes of thinking about and interacting with computationally mediated interactive creation.

scholarly journals Artistic Style Meets Artificial Intelligence

2021 ◽  
Author(s):  
Suk Kyoung Choi ◽  
Steve DiPaola ◽  
Hannu Töyrylä
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Lived Experience ◽  
Creative Expression ◽  
Visual Artists ◽  
Image Transformation ◽  
Artistic Style ◽  
Latent Space ◽  
Recent Developments

Recent developments in neural network image processing motivate the question, how these technologies might better serve visual artists. Research goals to date have largely focused on either pastiche interpretations of what is framed as artistic “style” or seek to divulge heretofore unimaginable dimensions of algorithmic “latent space,” but have failed to address the process an artist might actually pursue, when engaged in the reflective act of developing an image from imagination and lived experience. The tools, in other words, are constituted in research demonstrations rather than as tools of creative expression. In this article, the authors explore the phenomenology of the creative environment afforded by artificially intelligent image transformation and generation, drawn from autoethnographic reviews of the authors’ individual approaches to artificial intelligence (AI) art. They offer a post-phenomenology of “neural media” such that visual artists may begin to work with AI technologies in ways that support naturalistic processes of thinking about and interacting with computationally mediated interactive creation.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

Automated Grading of Tomatoes Using Artificial Intelligence

2020 ◽  
pp. 216-239
Author(s):  
Tawanda Mushiri ◽  
Liberty Tende
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Tomato Fruit ◽  
Grading System ◽  
Past Century ◽  
Manual Sorting ◽  
Fruit Processing ◽  
Automated Grading ◽  
Fruit Grading

The rate of production of horticultural produce had been seen increasing from the past century owing to the increase of population. Manual sorting and grading of tomatoes had become a challenge in market places and fruit processing firms since the demand of the fruit had increased. Considering grading of tomatoes, color is of major importance when it comes to the maturity of the tomatoes. Hence, there is a need to accurately classify them according to color. This process is very complicated, tiresome, and laborious when it is done manually by a human being. Apart from being labor-demanding, human sorting, and grading results in inaccuracy in classifying of tomatoes which is a loss to both the farmer and customer. This chapter had been prepared focusing on the automatic and effective tomato fruit grading system using artificial intelligence particularly using artificial neural network in Matlab. The system makes use of the image processing toolbox and the ANN toolbox to process and classify the tomatoes images according to color and size.

scholarly journals Convolution Based Neural Network Model to Prevent Diseases Classification and Prediction Using Deep Learning

2021 ◽  
Vol 10 (8) ◽  
pp. 46-50
Author(s):  
D J Samatha Naidu ◽  
M.Gurivi Reddy
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Internet ◽  
Learning Models ◽  
Data Set ◽  
Proposed Model ◽  
Recent Developments ◽  
Automatic Image Recognition ◽  
Recognition Systems

The farmer is a backbone to nation, but majority of the cultivated crops in india affecting by various diseases at various stages of its cultivation. Recent research works shows that diseases are not providing accurate results and few identifying but not providing optimized solutions to the system. In proposed work, the recent developments of Artificial intelligence through Deep Learning show that AIR (Automatic Image Recognition systems) using CNN algorithm models can be very beneficial in such scenarios. The Rice leaf diseases images related dataset is not easily available to automate , so that we have created our own trained data set which is small in size hence we have used transfer learning to develop our Proposed model which supports deep learning models. The Proposed CNN architecture illustrated based on VGG-16 model and it is trained, tested on given dataset collected from rice fields and the internet. The accuracy of the proposed model is moderately accurate with 92.46%.

scholarly journals Discovery of Novel Chemical Reactions by Deep Generative Recurrent Neural Network

2020 ◽  
Author(s):  
William Bort ◽  
Igor I. Baskin ◽  
Pavel Sidorov ◽  
Gilles Marcou ◽  
Dragos Horvath ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Chemical Reactions ◽  
Recurrent Neural Network ◽  
Coupling Reactions ◽  
Two Dimensional ◽  
Topographic Map ◽  
Latent Space ◽  
The Given ◽  
Condensed Graph Of Reaction

Here, we report an application of Artificial Intelligence techniques to generate novel chemical reactions of the given type. A sequence-to-sequence autoencoder was trained on the USPTO reaction database. Each reaction was converted into a single Condensed Graph of Reaction (CGR), followed by their translation into on-purpose developed SMILES/GGR text strings. The autoencoder latent space was visualized on the two-dimensional generative topographic map, from which some zones populated by Suzuki coupling reactions were targeted. These served for the generation of novel reactions by sampling the latent space points and decoding them to SMILES/CGR.<br>

Exploring the operation of a neural network using the example of handwritten digits recognition in an advanced informatics course using the python 3.8 programming language

2021 ◽  
pp. 21-29
Author(s):  
N. N. Samylkina ◽  
I. A. Kalinin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Processing ◽  
General Education ◽  
Programming Language ◽  
Loss Function ◽  
Optimization Method ◽  
Classification Problem ◽  
Hands On ◽  
Software Distribution

The article discusses the possibility of including artificial intelligence topics in the informatics course at the level of secondary general education on the demonstration example of handwritten digits recognition using the Python 3.8 programming language and the TensorFlow package. A hands-on example of a classification problem using the classic MNIST (Modified National Institute of Standards and Technology) training example set is dealt with step-by-step.Most of the tools required for the work (language interpreter, basic libraries, and shell) are downloaded in the form of a single software distribution kit of Anaconda. The network is trained using different methods. When the model is trained, the optimization method, the loss function, and the metric for estimation are specified. Image processing by convolutional nets containing special layers, which "convolve" the image the way a bitmap filter does, is also demonstrated

scholarly journals Prediksi Jarak Bola pada Citra Kamera Katadioptrik menggunakan metode Artifical Neural Network

2021 ◽  
Vol 9 (2) ◽  
pp. 279
Author(s):  
ZENDI ZAKARIA RAGA PERMANA ◽  
SUSIJANTO TRI RASMANA ◽  
IRA PUSPASARI
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Mobile Robot ◽  
Prediction Accuracy ◽  
Euclidean Distance ◽  
Morphological Process ◽  
Artifical Neural Network ◽  
Distance Data ◽  
Catadioptric Camera

ABSTRAKSaat ini, kecerdasan buatan memungkinkan untuk dikembangkan dalam dunia robotika, khususnya untuk pengaturan gerakan robot berdasarkan pengolahan citra. Penelitian ini mengembangkan sebuah mobile robot yang dilengkapi dengan kamera katadioptrik dengan sudut pandang 3600. Citra yang didapatkan, dikonversi dari RGB menjadi HSV. Selanjutnya disesuaikan dengan proses morfologi. Nilai jarak yang terbaca oleh kamera (piksel) dengan jarak sebenarnya (cm) dihitung menggunakan Euclidean Distance. Nilai ini sebagai ekstraksi ciri data jarak yang dilatihkan pada sistem. Sistem yang dibuat pada penelitian ini memiliki iterasi sebanyak 1.000.000, dengan tingkat kelinieran R2=0.9982 dan keakuratan prediksi sebesar 99,03%.Kata kunci: Robot, HSV, Euclidean Distance, Kamera katadioptrik, Artifical Neural NetworkABSTRACTRecently, artificial intelligence is possible to be developed in robotic, specifically for robot movements control based on image processing. This research develops a mobile robot with a 3600 perspective catadioptric camera is equipped. The camera captured images were converting from RGB to HSV. Furthermore, it adapted to the morphological process. The distance value read by the camera (pixels) to the actual distance (cm) is measured using Euclidean Distance. This value is a feature extraction of distance data that has training on the system. The system built in this study has 1,000,000 iterations, with a linearity level of R2 = 0.9982 and prediction accuracy of 99.03%.Keywords: Robot, HSV, Euclidean Distance, Catadioptric Camera, Artifical Neural Network

scholarly journals Fashion Style Generator

2017 ◽  
Author(s):  
Shuhui Jiang ◽  
Yun Fu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Global Optimization ◽  
Real Time ◽  
Synthetic Image ◽  
Feed Forward Neural Network ◽  
Style Transfer ◽  
Artistic Style ◽  
The Arts ◽  
Optimization Stage

In this paper, we focus on a new problem: applying artificial intelligence to automatically generate fashion style images. Given a basic clothing image and a fashion style image (e.g., leopard print), we generate a clothing image with the certain style in real time with a neural fashion style generator. Fashion style generation is related to recent artistic style transfer works, but has its own challenges. The synthetic image should preserve the similar design as the basic clothing, and meanwhile blend the new style pattern on the clothing. Neither existing global nor patch based neural style transfer methods could well solve these challenges. In this paper, we propose an end-to-end feed-forward neural network which consists of a fashion style generator and a discriminator. The global and patch based style and content losses calculated by the discriminator alternatively back-propagate the generator network and optimize it. The global optimization stage preserves the clothing form and design and the local optimization stage preserves the detailed style pattern. Extensive experiments show that our method outperforms the state-of-the-arts.

scholarly journals Aplikasi MATLAB untuk Mengenali Karakter Tulisan Tangan

MATICS ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 18
Author(s):  
Ali Mahmudi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Handwriting Recognition ◽  
Postal Code ◽  
Matlab Toolbox ◽  
Bank Deposits ◽  
Neural Network Toolbox

<p>Handwriting recognition is one of the very interesting research object in the field of image processing, artificial intelligence and computer vision. This is due to the handwritten characters is varied in every individual. The style, size and orientation of handwriting characters has made every body’s is different, hence handwriting recognition is a very interesting research object. Handwriting recognition application has been used in quite many applications, such as reading the bank deposits, reading the postal code in letters, and helping peolple in managing documents.</p><p>This paper presents a handwriting recognition application using Matlab. Matlab toolbox that is used in this research are Image Processing and Neural Network Toolbox.</p><p> </p>

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