Computer Vision with Deep Learning Techniques for Neurodegenerative Diseases Analysis Using Neuroimaging: A Survey

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

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Underwater Gesture Recognition Using Classical Computer Vision and Deep Learning Techniques

Journal of Image and Graphics ◽

10.18178/joig.8.1.9-14 ◽

2020 ◽

Vol 8 (1) ◽

pp. 9-14

Author(s):

Mygel Andrei M. Martija ◽

Jakov Ivan S. Dumbrique ◽

Prospero C., Jr Naval

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Gesture Recognition ◽

Learning Techniques ◽

Classical Computer

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Segmentation for Complex Background Images using Deep Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1042.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 1746-1750

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Learning Algorithm ◽

Vision System ◽

Automated Segmentation ◽

Computer Vision System ◽

Important Stage ◽

Deep Learning Algorithm ◽

Complex Background ◽

Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

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Smart Doll: Emotion Recognition Using Embedded Deep Learning

Symmetry ◽

10.3390/sym10090387 ◽

2018 ◽

Vol 10 (9) ◽

pp. 387 ◽

Cited By ~ 3

Author(s):

Jose Espinosa-Aranda ◽

Noelia Vallez ◽

Jose Rico-Saavedra ◽

Javier Parra-Patino ◽

Gloria Bueno ◽

...

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Ethical Issues ◽

Real Life ◽

Graphic Processing Units ◽

Cloud Processing ◽

Embedded Computer Vision ◽

Embedded Computer ◽

Learning Techniques ◽

Learning Capabilities

Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll.

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Deep Learning for Computer Vision: A Brief Review

Computational Intelligence and Neuroscience ◽

10.1155/2018/7068349 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 326

Author(s):

Athanasios Voulodimos ◽

Nikolaos Doulamis ◽

Anastasios Doulamis ◽

Eftychios Protopapadakis

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Review Paper ◽

Machine Learning Techniques ◽

Future Directions ◽

Boltzmann Machines ◽

Learning Techniques ◽

Previous State ◽

Human Pose ◽

Learning Schemes

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

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Varietal quality control in the nursery plant industry using computer vision and deep learning techniques

Journal of Chemometrics ◽

10.1002/cem.3320 ◽

2020 ◽

Author(s):

Sergio Borraz‐Martínez ◽

Francesc Tarrés ◽

Ricard Boqué ◽

Mariàngela Mestre ◽

Joan Simó ◽

...

Keyword(s):

Computer Vision ◽

Quality Control ◽

Deep Learning ◽

Plant Industry ◽

Learning Techniques ◽

Nursery Plant

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Computer vision and deep learning techniques for pedestrian detection and tracking: A survey

Neurocomputing ◽

10.1016/j.neucom.2018.01.092 ◽

2018 ◽

Vol 300 ◽

pp. 17-33 ◽

Cited By ~ 94

Author(s):

Antonio Brunetti ◽

Domenico Buongiorno ◽

Gianpaolo Francesco Trotta ◽

Vitoantonio Bevilacqua

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Pedestrian Detection ◽

Detection And Tracking ◽

Learning Techniques

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Artificial Intelligence and Machine Learning Algorithms

Advances in Computer and Electrical Engineering - Challenges and Applications for Implementing Machine Learning in Computer Vision ◽

10.4018/978-1-7998-0182-5.ch008 ◽

2020 ◽

pp. 188-219

Author(s):

Amit Kumar Tyagi ◽

Poonam Chahal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computer Vision ◽

Big Data ◽

Deep Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Techniques

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.

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