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J. Imaging, Volume 6, Issue 8 (August 2020) – 9 articles

Cover Story (view full-size image): The research proposes a simple and cost-effective system for aerial 3D thermography of large complex structures as buildings using a single calibrated IR-VIS system. Starting from a datasets of infrared and visible images collected using unmanned aerial vehicles, it is possible to reconstruct 3D visible and thermal models. The joint use of 3D imaging and infrared thermography is a powerful tool in engineering and sciences, specifically of great interest in non-destructing testing and continuous monitoring. Applications range from structural and maintenance diagnostics of buildings (historical but also civil infrastuctures such as bridges), evaluation of energy efficiency, up to investigation of large archaeological sites. View this paper
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12 pages, 3014 KiB  
Article
On Computational Aspects of Krawtchouk Polynomials for High Orders
by Basheera M. Mahmmod, Alaa M. Abdul-Hadi, Sadiq H. Abdulhussain and Aseel Hussien
J. Imaging 2020, 6(8), 81; https://doi.org/10.3390/jimaging6080081 - 13 Aug 2020
Cited by 28 | Viewed by 2768
Abstract
Discrete Krawtchouk polynomials are widely utilized in different fields for their remarkable characteristics, specifically, the localization property. Discrete orthogonal moments are utilized as a feature descriptor for images and video frames in computer vision applications. In this paper, we present a new method [...] Read more.
Discrete Krawtchouk polynomials are widely utilized in different fields for their remarkable characteristics, specifically, the localization property. Discrete orthogonal moments are utilized as a feature descriptor for images and video frames in computer vision applications. In this paper, we present a new method for computing discrete Krawtchouk polynomial coefficients swiftly and efficiently. The presented method proposes a new initial value that does not tend to be zero as the polynomial size increases. In addition, a combination of the existing recurrence relations is presented which are in the n- and x-directions. The utilized recurrence relations are developed to reduce the computational cost. The proposed method computes approximately 12.5% of the polynomial coefficients, and then symmetry relations are employed to compute the rest of the polynomial coefficients. The proposed method is evaluated against existing methods in terms of computational cost and maximum size can be generated. In addition, a reconstruction error analysis for image is performed using the proposed method for large signal sizes. The evaluation shows that the proposed method outperforms other existing methods. Full article
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18 pages, 5040 KiB  
Article
Full 3D Microwave Breast Imaging Using a Deep-Learning Technique
by Vahab Khoshdel, Mohammad Asefi, Ahmed Ashraf and Joe LoVetri
J. Imaging 2020, 6(8), 80; https://doi.org/10.3390/jimaging6080080 - 11 Aug 2020
Cited by 39 | Viewed by 4297
Abstract
A deep learning technique to enhance 3D images of the complex-valued permittivity of the breast obtained via microwave imaging is investigated. The developed technique is an extension of one created to enhance 2D images. We employ a 3D Convolutional Neural Network, based on [...] Read more.
A deep learning technique to enhance 3D images of the complex-valued permittivity of the breast obtained via microwave imaging is investigated. The developed technique is an extension of one created to enhance 2D images. We employ a 3D Convolutional Neural Network, based on the U-Net architecture, that takes in 3D images obtained using the Contrast-Source Inversion (CSI) method and attempts to produce the true 3D image of the permittivity. The training set consists of 3D CSI images, along with the true numerical phantom images from which the microwave scattered field utilized to create the CSI reconstructions was synthetically generated. Each numerical phantom varies with respect to the size, number, and location of tumors within the fibroglandular region. The reconstructed permittivity images produced by the proposed 3D U-Net show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but it also enhances the detectability of the tumors. We test the trained U-Net with 3D images obtained from experimentally collected microwave data as well as with images obtained synthetically. Significantly, the results illustrate that although the network was trained using only images obtained from synthetic data, it performed well with images obtained from both synthetic and experimental data. Quantitative evaluations are reported using Receiver Operating Characteristics (ROC) curves for the tumor detectability and RMS error for the enhancement of the reconstructions. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis)
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16 pages, 37802 KiB  
Article
Practical Camera Sensor Spectral Response and Uncertainty Estimation
by Mikko E. Toivonen and Arto Klami
J. Imaging 2020, 6(8), 79; https://doi.org/10.3390/jimaging6080079 - 5 Aug 2020
Cited by 4 | Viewed by 4804
Abstract
Knowledge of the spectral response of a camera is important in many applications such as illumination estimation, spectrum estimation in multi-spectral camera systems, and color consistency correction for computer vision. We present a practical method for estimating the camera sensor spectral response and [...] Read more.
Knowledge of the spectral response of a camera is important in many applications such as illumination estimation, spectrum estimation in multi-spectral camera systems, and color consistency correction for computer vision. We present a practical method for estimating the camera sensor spectral response and uncertainty, consisting of an imaging method and an algorithm. We use only 15 images (four diffraction images and 11 images of color patches of known spectra to obtain high-resolution spectral response estimates) and obtain uncertainty estimates by training an ensemble of response estimation models. The algorithm does not assume any strict priors that would limit the possible spectral response estimates and is thus applicable to any camera sensor, at least in the visible range. The estimates have low errors for estimating color channel values from known spectra, and are consistent with previously reported spectral response estimates. Full article
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39 pages, 19974 KiB  
Review
A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles
by Dario Cazzato, Claudio Cimarelli, Jose Luis Sanchez-Lopez, Holger Voos and Marco Leo
J. Imaging 2020, 6(8), 78; https://doi.org/10.3390/jimaging6080078 - 4 Aug 2020
Cited by 64 | Viewed by 10341
Abstract
The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to [...] Read more.
The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed. Full article
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10 pages, 2142 KiB  
Article
Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications
by Oussama Zeglazi, Mohammed Rziza, Aouatif Amine and Cédric Demonceaux
J. Imaging 2020, 6(8), 77; https://doi.org/10.3390/jimaging6080077 - 3 Aug 2020
Cited by 3 | Viewed by 2309
Abstract
The human visual perception uses structural information to recognize stereo correspondences in natural scenes. Therefore, structural information is important to build an efficient stereo matching algorithm. In this paper, we demonstrate that incorporating the structural information similarity, extracted either from image intensity ( [...] Read more.
The human visual perception uses structural information to recognize stereo correspondences in natural scenes. Therefore, structural information is important to build an efficient stereo matching algorithm. In this paper, we demonstrate that incorporating the structural information similarity, extracted either from image intensity (SSIM) directly or from image gradients (GSSIM), between two patches can accurately describe the patch structures and, thus, provides more reliable initial cost values. We also address one of the major phenomenons faced in stereo matching for real world scenes, radiometric changes. The performance of the proposed cost functions was evaluated within two stages: the first one considers these costs without aggregation process while the second stage uses the fast adaptive aggregation technique. The experiments were conducted on the real road traffic scenes KITTI 2012 and KITTI 2015 benchmarks. The obtained results demonstrate the potential merits of the proposed stereo similarity measurements under radiometric changes. Full article
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13 pages, 3847 KiB  
Article
A Cost-Effective System for Aerial 3D Thermography of Buildings
by Claudia Daffara, Riccardo Muradore, Nicola Piccinelli, Nicola Gaburro, Tullio de Rubeis and Dario Ambrosini
J. Imaging 2020, 6(8), 76; https://doi.org/10.3390/jimaging6080076 - 2 Aug 2020
Cited by 22 | Viewed by 3915
Abstract
Three-dimensional (3D) imaging and infrared (IR) thermography are powerful tools in many areas in engineering and sciences. Their joint use is of great interest in the buildings sector, allowing inspection and non-destructive testing of elements as well as an evaluation of the energy [...] Read more.
Three-dimensional (3D) imaging and infrared (IR) thermography are powerful tools in many areas in engineering and sciences. Their joint use is of great interest in the buildings sector, allowing inspection and non-destructive testing of elements as well as an evaluation of the energy efficiency. When dealing with large and complex structures, as buildings (particularly historical) generally are, 3D thermography inspection is enhanced by Unmanned Aerial Vehicles (UAV—also known as drones). The aim of this paper is to propose a simple and cost-effective system for aerial 3D thermography of buildings. Special attention is thus payed to instrument and reconstruction software choice. After a very brief introduction to IR thermography for buildings and 3D thermography, the system is described. Some experimental results are given to validate the proposal. Full article
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19 pages, 3648 KiB  
Article
No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features
by Domonkos Varga
J. Imaging 2020, 6(8), 75; https://doi.org/10.3390/jimaging6080075 - 30 Jul 2020
Cited by 20 | Viewed by 5505
Abstract
The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector [...] Read more.
The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods. Full article
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16 pages, 7265 KiB  
Article
No-Reference Quality Assessment of In-Capture Distorted Videos
by Mirko Agarla, Luigi Celona and Raimondo Schettini
J. Imaging 2020, 6(8), 74; https://doi.org/10.3390/jimaging6080074 - 30 Jul 2020
Cited by 13 | Viewed by 3775
Abstract
We introduce a no-reference method for the assessment of the quality of videos affected by in-capture distortions due to camera hardware and processing software. The proposed method encodes both quality attributes and semantic content of each video frame by using two Convolutional Neural [...] Read more.
We introduce a no-reference method for the assessment of the quality of videos affected by in-capture distortions due to camera hardware and processing software. The proposed method encodes both quality attributes and semantic content of each video frame by using two Convolutional Neural Networks (CNNs) and then estimates the quality score of the whole video by using a Recurrent Neural Network (RNN), which models the temporal information. The extensive experiments conducted on four benchmark databases (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) containing in-capture distortions demonstrate the effectiveness of the proposed method and its ability to generalize in cross-database setup. Full article
(This article belongs to the Special Issue Deep Learning for Visual Contents Processing and Analysis)
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29 pages, 3836 KiB  
Review
Hand Gesture Recognition Based on Computer Vision: A Review of Techniques
by Munir Oudah, Ali Al-Naji and Javaan Chahl
J. Imaging 2020, 6(8), 73; https://doi.org/10.3390/jimaging6080073 - 23 Jul 2020
Cited by 293 | Viewed by 41763
Abstract
Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including [...] Read more.
Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications. Full article
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