scholarly journals A Review on Deep Learning Techniques for 3D Sensed Data Classification

2019 ◽  
Vol 11 (12) ◽  
pp. 1499 ◽  
Author(s):  
David Griffiths ◽  
Jan Boehm
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Point Clouds ◽  
Image Understanding ◽  
Future Research ◽  
National Scale ◽  
The Past ◽  
Current State ◽  
Learning Techniques ◽  
Learning Architectures

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches, including RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.

scholarly journals Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 37 ◽  
Author(s):  
Luca Cappelletti ◽  
Tommaso Fontana ◽  
Guido Walter Di Donato ◽  
Lorenzo Di Tucci ◽  
Elena Casiraghi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
State Of The Art ◽  
The State ◽  
Complex Data ◽  
Data Imputation ◽  
Genome Sequences ◽  
Missing Data Imputation ◽  
The Past ◽  
Learning Techniques

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences.

Environmental Peacebuilding

2021 ◽  
pp. 562-578
Author(s):  
Florian Krampe ◽  
Ashok Swain
Keyword(s):  
Natural Resources ◽  
Sustainable Management ◽  
State Of The Art ◽  
Future Research ◽  
Environmental Cooperation ◽  
The Past ◽  
Current State ◽  
Policy Arenas ◽  
Looking Back ◽  
Scholarly Attention

For international and domestic actors, postconflict situations constitute one of the most difficult policy arenas to understand and operate within. In this context, the sustainable management of natural resources to prevent conflict and build peace—before, during, or after conflict—has received increasing scholarly attention over the past three decades. Emphasizing the potential for environmental cooperation to support peace and stability, researchers have focused on the ecological foundations for a socially, economically, and politically resilient peace. This chapter takes stock of the current state of the art on environmental peacebuilding, providing a summary of the most common definitions before looking back at the development of environmental peacebuilding along the two most noticeable perspectives and the remaining challenges and pathways for future research.

scholarly journals Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3836
Author(s):  
Clément Dechesne ◽  
Pierre Lassalle ◽  
Sébastien Lefèvre
Keyword(s):  
Deep Learning ◽  
Satellite Images ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Earth Observation ◽  
Current State ◽  
Learning Techniques ◽  
Reference Databases

In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are still unable to provide results with the level of accuracy sought in real applications, i.e., in operational settings. Thus, it is mandatory to qualify these segmentation results and estimate the uncertainty brought about by a deep network. In this work, we address uncertainty estimations in semantic segmentation. To do this, we relied on a Bayesian deep learning method, based on Monte Carlo Dropout, which allows us to derive uncertainty metrics along with the semantic segmentation. Built on the most widespread U-Net architecture, our model achieves semantic segmentation with high accuracy on several state-of-the-art datasets. More importantly, uncertainty maps are also derived from our model. While they allow for the performance of a sounder qualitative evaluation of the segmentation results, they also include valuable information to improve the reference databases.

scholarly journals A REVIEW REGARDING DEEP LEARNING TECHNOLOGY IN MOBILE ROBOTS

1970 ◽  
Vol 5 (1.) ◽  
Author(s):  
Vlad Ovidiu Mihalca ◽  
Flaviu Birouaș ◽  
Florin Avram ◽  
Arnold Nilgesz
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Mobile Robots ◽  
State Of The Art ◽  
Future Research ◽  
Learning Technology ◽  
Learning Techniques ◽  
Industry Applications

Deep Learning usage is spread across many fields of application. This paper presents details from a selected variety of works published in recent years to illustrate the versatility of the Deep Learning techniques, their potential in current and future research and industry applications as well as their state-of-the-art status in vision tasks, where their efficiency is experimentally proven to near 100% accuracy. The presented applications range from navigation to localization, object recognition and more advanced interactions such as grasping.

scholarly journals A Controlled Benchmark of Video Violence Detection Techniques

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 321
Author(s):  
Nicola Convertini ◽  
Vincenzo Dentamaro ◽  
Donato Impedovo ◽  
Giuseppe Pirlo ◽  
Lucia Sarcinella
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Detection Techniques ◽  
Detection Systems ◽  
Current State ◽  
Violence Detection ◽  
Learning Techniques ◽  
The Future ◽  
Feature Based ◽  
Art Techniques

This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3.

scholarly journals Deep Learning Methods in Speaker Recognition: A Review

2021 ◽  
Author(s):  
Dávid Sztahó ◽  
György Szaszák ◽  
András Beke
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Speaker Recognition ◽  
State Of The Art ◽  
Speaker Verification ◽  
The Novel ◽  
Learning Methods ◽  
Speech Technology ◽  
The Past ◽  
Learning Techniques

This paper reviews the applied Deep Learning (DL) practices in the field of Speaker Recognition (SR), both in verification and identification. Speaker Recognition has been a widely used topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5–6 years. However, as Deep Learning techniques do advance in most machine learning fields, the former state-of-the-art methods are getting replaced by them in Speaker Recognition too. It seems that Deep Learning becomes the now state-of-the-art solution for both Speaker Verification (SV) and identification. The standard x-vectors, additional to i-vectors, are used as baseline in most of the novel works. The increasing amount of gathered data opens up the territory to Deep Learning, where they are the most effective.

scholarly journals Generating Bird’s Eye View from Egocentric RGB Videos

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Vanita Jain ◽  
Qiming Wu ◽  
Shivam Grover ◽  
Kshitij Sidana ◽  
Gopal Chaudhary ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Point Clouds ◽  
Two Dimensions ◽  
The Other ◽  
Future Research ◽  
3D Point Clouds ◽  
Image Translation ◽  
Rgb Images ◽  
Path Prediction

In this paper, we present a method for generating bird’s eye video from egocentric RGB videos. Working with egocentric views is tricky since such the view is highly warped and prone to occlusions. On the other hand, a bird’s eye view has a consistent scaling in at least the two dimensions it shows. Moreover, most of the state-of-the-art systems for tasks such as path prediction are built for bird’s eye views of the subjects. We present a deep learning-based approach that transfers the egocentric RGB images captured from a dashcam of a car to bird’s eye view. This is a task of view translation, and we perform two experiments. The first one uses an image-to-image translation method, and the other uses a video-to-video translation. We compare the results of our work with homographic transformation, and our SSIM values are better by a margin of 77% and 14.4%, and the RMSE errors are lower by 40% and 14.6% for image-to-image translation and video-to-video translation, respectively. We also visually show the efficacy and limitations of each method with helpful insights for future research. Compared to previous works that use homography and LIDAR for 3D point clouds, our work is more generalizable and does not require any expensive equipment.

scholarly journals An Input-Perceptual Reconstruction Adversarial Network for Paired Image-to-Image Conversion

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4161
Author(s):  
Aamir Khan ◽  
Weidong Jin ◽  
Muqeet Ahmad ◽  
Rizwan Ali Naqvi ◽  
Desheng Wang
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
State Of The Art ◽  
Basic Structure ◽  
Image Generation ◽  
Adversarial Network ◽  
Current State ◽  
Image Conversion ◽  
Learning Techniques ◽  
Generation Network

Image-to-image conversion based on deep learning techniques is a topic of interest in the fields of robotics and computer vision. A series of typical tasks, such as applying semantic labels to building photos, edges to photos, and raining to de-raining, can be seen as paired image-to-image conversion problems. In such problems, the image generation network learns from the information in the form of input images. The input images and the corresponding targeted images must share the same basic structure to perfectly generate target-oriented output images. However, the shared basic structure between paired images is not as ideal as assumed, which can significantly affect the output of the generating model. Therefore, we propose a novel Input-Perceptual and Reconstruction Adversarial Network (IP-RAN) as an all-purpose framework for imperfect paired image-to-image conversion problems. We demonstrate, through the experimental results, that our IP-RAN method significantly outperforms the current state-of-the-art techniques.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

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