Variable photorealistic image synthesis for training dataset generation

2020 ◽  
Author(s):  
Vadim Sanzharov ◽  
Vladimir Frolov ◽  
Alexey Voloboy
Keyword(s):  
Artificial Intelligence ◽  
Image Synthesis ◽  
3D Models ◽  
Training Dataset ◽  
Photorealistic Rendering ◽  
Texture Generation ◽  
Small Set ◽  
Photorealistic Images ◽  
New Applications ◽  
Digital Assets

Photorealistic rendering systems have recently found new applications in artificial intelligence, specifically in computer vision for the purpose of generation of image and video sequence datasets. The problem associated with this application is producing large number of photorealistic images with high variability of 3d models and their appearance. In this work, we propose an approach based on combining existing procedural texture generation techniques and domain randomization to generate large number of highly variative digital assets during the rendering process. This eliminates the need for a large pre-existing database of digital assets (only a small set of 3d models is required), and generates objects with unique appearance during rendering stage, reducing the needed post-processing of images and storage requirements. Our approach uses procedural texturing and material substitution to rapidly produce large number of variations of digital assets. The proposed solution can be used to produce training datasets for artificial intelligence applications and can be combined with most of state-of-the-art methods of scene generation.

Mesterséges intelligencia az egészség és a jólét területén: a gépi tanulás, a crowdsourcing és az ön-annotációban rejlő lehetőségek

2017 ◽  
Vol 15 (4) ◽  
pp. 54
Author(s):  
András Lőrincz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Visual Processing ◽  
Wearable Sensors ◽  
3D Models ◽  
Human Intelligence ◽  
Health And Wellbeing ◽  
Mining Methods ◽  
Human Computing ◽  
New Applications

Cikkemben érveket hozok fel amellett, hogy, hogy a technológiai fejlődés ma nagy lehetőségeket kínálnak az egészségügy és a jólét számára. Nézetem szerint (1) az „okos” eszközök (smart tools) és a különböző viselhető érzékelők, (2) az adatgyűjtés és az adatbányászati módszerek, (3) a három dimenziós (3D-s) képi rögzítési és képi feldolgozási eszközök, (4) a 3D-s, bonyolult fizikai motorral rendelkező, például grafikai modellek, valamint (5) a crowdsourcing-on (outsourcing: külső erőforrások igénybevétele, crowdsourcing: külső emberi erőforrások tömeges igénybevétele) alapuló emberalapú számítások (human-based computing), terén történő nagy és sikeres erőfeszítések hatalmas változásokat indítanak el. Nem állítom, bár tagadni sem tudom azt, hogy a mesterséges intelligencia eszközei néhány év múlva elérik az emberi intelligencia szintjét, mert ez lehetséges. Véleményem szerint, az egészségügy és a jólét területén gyors fejlődés lehetséges az egészségügyi és jóléti szakértők, és a motivált mérnökök közötti aktív együttműködés útján. --- Artificial Intelligence, Health and Wellbeing: prospects for machine learning, crowdsourcing and self-annotation We argue that recent technology developments – e.g. smart tools and wearable sensors of diverse kinds, data collection and data mining methods, 3D visual recording and visual processing methods, 3D models of the environment with robust physics engine – and new applications of human computing and crowdsourcing hold great promises for health and wellbeing. We are neither claiming nor excluding that human intelligence will be reached in some years from now, but make the above claim, which is both weaker and stronger. We believe that fast developments for health and wellbeing are the question of active collaboration between health and wellbeing experts and motivated engineers.

scholarly journals The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Daniele Giansanti
Keyword(s):  
Artificial Intelligence ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Mobile Health ◽  
Digital Pathology ◽  
Digital Radiology ◽  
Cellular Diagnostics ◽  
Information And Communication ◽  
Electronic Health ◽  
New Applications

Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...]

scholarly journals SYNTHETIC VISION SYSTEM CALIBRATION FOR CONFORM PROJECTION ON THE PILOT’S HEAD-UP DISPLAY

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 575-581
Author(s):  
S. Danilov ◽  
M. Kozyrev ◽  
M. Grechanichenko ◽  
L. Grodzitskiy ◽  
V. Mizginov ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Vision System ◽  
Digital Terrain Model ◽  
3D Models ◽  
Sensor Data ◽  
Training Dataset ◽  
Learning Approaches ◽  
Terrain Model ◽  
Front Window ◽  
Calibration Algorithm

Abstract. Situational awareness of the crew is critical for the safety of the air flight. Head-up display allows providing all required flight information in front of the pilot over the cockpit view visible through the cockpit’s front window. This device has been created for solving the problem of informational overload during piloting of an aircraft. While computer graphics such as scales and digital terrain model can be easily presented on the such display, errors in the Head-up display alignment for correct presenting of sensor data pose challenges. The main problem arises from the parallax between the pilot’s eyes and the position of the camera. This paper is focused on the development of an online calibration algorithm for conform projection of the 3D terrain and runway models on the pilot’s head-up display. The aim of our algorithm is to align the objects visible through the cockpit glass with their projections on the Head-up display. To improve the projection accuracy, we use an additional optical sensor installed on the aircraft. We combine classical photogrammetric techniques with modern deep learning approaches. Specifically, we use an object detection neural network model to find the runway area and align runway projection with its actual location. Secondly, we re-project the sensor’s image onto the 3D model of the terrain to eliminate errors caused by the parallax. We developed an environment simulator to evaluate our algorithm. Using the simulator we prepared a large training dataset. The dataset includes 2000 images of video sequences representing aircraft’s motion during takeoff, landing and taxi. The results of the evaluation are encouraging and demonstrate both qualitatively and quantitatively that the proposed algorithm is capable of precise alignment of the 3D models projected on a Head-up display.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

Role of Artificial Intelligence in Cognitive Radio Networks

2019 ◽  
pp. 258-279 ◽  
Author(s):  
Shikha Singhal ◽  
Shashank Gupta ◽  
Adwitiya Sinha
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Evolutionary Process ◽  
Radio Spectrum ◽  
Radio Networks ◽  
Training Dataset ◽  
Wireless Medium ◽  
Computing Machines

The role of artificial intelligence techniques and its impact in context of cognitive radio networks has become immeasurable. Artificial intelligence redefines and empowers the decision making and logical capability of computing machines through the evolutionary process of leaning, adapting, and upgrading its knowledge bank accordingly. Significant functionalities of artificial intelligence include sensing, collaborating, learning, evolving, training, dataset, and performing tasks. Cognitive radio enables learning and evolving through contextual data perceived from its immediate surrounding. Cognitive science aims at acquiring knowledge by observing and recording externalities of environment. It allows self-programming and self-learning with added intelligence and enhanced communicational capabilities over wireless medium. Equipped with cognitive technology, the vision of artificial intelligence gets broadened towards optimizing usage of radio spectrum by accessing spectrum availability, thereby reducing channel interferences while communication among licensed and non-licensed users.

scholarly journals Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China

2019 ◽  
Vol 10 (1) ◽  
pp. 29 ◽  
Author(s):  
Wei Chen ◽  
Limin Fan ◽  
Cheng Li ◽  
Binh Thai Pham
Keyword(s):  
Artificial Intelligence ◽  
Frequency Ratio ◽  
Spatial Prediction ◽  
Training Dataset ◽  
Slope Aspect ◽  
Hybrid Integration ◽  
Operating Characteristics ◽  
Regional Survey ◽  
Index Of Entropy ◽  
Bivariate Models

The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area.

scholarly journals An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 901
Author(s):  
Fucong Liu ◽  
Tongzhou Zhang ◽  
Caixia Zheng ◽  
Yuanyuan Cheng ◽  
Xiaoli Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Active Learning ◽  
Sample Selection ◽  
Training Dataset ◽  
Learning Method ◽  
Active Learning Method ◽  
Level Information ◽  
The Stability ◽  
Different Characteristics

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.

Artificial Intelligence (AI) Techniques for Maximizing Value of Power Generating Assets

2018 ◽  
Author(s):  
Komandur Sunder Raj
Keyword(s):  
Artificial Intelligence ◽  
Power Industry ◽  
The Past ◽  
Power Plant Equipment ◽  
Industrial Internet ◽  
Plant Operations ◽  
Plant Equipment ◽  
And Performance ◽  
Self Learning ◽  
Digital Assets

For over two decades, there has been considerable interest in and research devoted to the use of artificial intelligence (AI) for maximizing the value of power generating assets. AI may be thought of as application of intelligence in a systematic and rational manner to power plant equipment, components and processes for self-learning and solving complex problems. AI techniques are increasingly finding applications in the power industry in addressing issues related to performance, reliability, availability, maintenance, automation, cybersecurity, workforce, and others. In the past several years, pace has accelerated in AI techniques, largely stemming from increased speed and power in computing, advances in technology, and utilization of algorithms. The Industrial Internet of Things (IIoT) is rapidly gaining ground by leveraging AI, digital assets and data analytics in managing and optimizing plant operations and performance of power generating assets. This paper provides an overview of how AI techniques are being utilized to maximize the value of power generating assets and prognosis for future use of AI in the power industry.

Hey Siri, tell me a story: Digital storytelling and AI authorship

2020 ◽  
Vol 26 (4) ◽  
pp. 808-823
Author(s):  
Sarah Thorne
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
News Media ◽  
Future Directions ◽  
Daily Lives ◽  
Short Films ◽  
The Future ◽  
Commercial Applications ◽  
New Applications ◽  
Applications Of Ai

Surveying narrative applications of artificial intelligence in film, games and interactive fiction, this article imagines the future of artificial intelligence (AI) authorship and explores trends that seek to replace human authors with algorithmically generated narrative. While experimental works that draw on text generation and natural language processing have a rich history, this article focuses on commercial applications of AI narrative and looks to future applications of this technology. Video games have incorporated AI and procedural generation for many years, but more recently, new applications of this technology have emerged in other media. Director Oscar Sharp and artist Ross Goodwin, for example, generated significant media buzz about two short films that they produced which were written by their AI screenwriter. It’s No Game (2017), in particular, offers an apt commentary on the possibility of replacing striking screenwriters with AI authors. Increasingly, AI agents and virtual assistants like Siri, Cortana, Alexa and Google Assistant are incorporated into our daily lives. As concerns about their eavesdropping circulate in news media, it is clear that these companions are learning a lot about us, which raises concerns about how our data might be employed in the future. This article explores current applications of AI for storytelling and future directions of this technology to offer insight into issues that have and will continue to arise as AI storytelling advances.

scholarly journals A review on drug repurposing applicable to COVID-19

2020 ◽  
Author(s):  
Serena Dotolo ◽  
Anna Marabotti ◽  
Angelo Facchiano ◽  
Roberto Tagliaferri
Keyword(s):  
Artificial Intelligence ◽  
Drug Target ◽  
Lower Cost ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
The Moment ◽  
New Applications ◽  
Novel Drug ◽  
Small Chemical

Abstract Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

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