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Advances in Infrared Imaging: Sensing, Exploitation and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 June 2018) | Viewed by 71346

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Prof. Dr. Angel D. Sappa

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Guest Editor

1. Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, CIDIS, Campus Gustavo Galindo, Km 30.5 vía Perimetral, Guayaquil 09-01-5863, Ecuador
2. Computer Vision Center, Edifici O, Campus UAB, Bellaterra, 08193 Barcelona, Spain
Interests: multispectral imaging; infrared image processing; cross-spectral image registration and fusion; 3D modeling

Dr. Riad I. Hammoud

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Guest Editor

BAE Systems (Massachusetts, USA)
Interests: video and image understanding, exploitation and analytics (object detection, tracking, feature extraction, recognition, geo/registration, multi-camera activity pattern modeling and recognition, multi-sensory data fusion, text mining and fusion with video data; automatic target recognition, etc.); ground/aerial/underwater robotics (assisted perception, supervised tele-autonomy, autonomous navigation), geo-localization and big data analytics (content/feature extraction, topic modeling, indexing, matching); machine learning (classification and clustering, pattern learning, and deep learning)

Special Issue Information

Dear Colleagues,

The number of infrared image applications has been growing considerably over the last few decades; this evolution is mainly motivated by the appearance of new devices, as well as the reduction in price of technology. Nowadays, infrared-based devices can be found in domains that range from medical applications to computer games or industrial solutions. Infrared images are divided into different categories, including Near InfraRed (NIR), Short-Wave InfraRed (SWIR), Mid-Wave InfraRed (MWIR) and Long-Wave InfraRed (LWIR). Images from each one of these sub-categories have their own characteristics that make them attractive to specific applications. It should be noticed that these characteristics are, not only used to develop new applications, but are also used to improve the appearance of the corresponding VS image in case it is available; for instance, noise filtering, image enhance, and dehazing to mention a few applications of infrared on visible spectrum images. In other words, infrared imaging has found its way to become a solid source of additional information for a wide range of applications.

This Special Issue is intended to review the state-of-the-art on infrared imaging, from image acquisition to the processing, and understanding of all the infrared spectra. The following infrared imaging topics will be covered, but this Special Issue is not limited to only these topics:

Imaging systems (NIR/SWIR/MWIR/LWIR)
Processing (filtering, coloring, feature extraction and matching)
Representation (fusion, thermal mapping)
Understanding (machine learning, pattern recognition)
Deep/Transfer learning, Domain adaptation
Vision-aided navigation
Sensing for agriculture and food safety
Autonomous driving
Thermal image applications
Remote sensing

This Special Issue will receive extended version of papers on infrared imaging published at the workshop on “Perception Beyond the Visual Spectrum” (workshop series in conjunction with CVPR conference) http://vcipl-okstate.org/pbvs/17/index.html

Dr. Angel D. Sappa
Dr. Riad I. Hammoud
Guest Editors

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Published Papers (14 papers)

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Research

12 pages, 1283 KiB

Open AccessArticle

Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools

by Berta Baca-Bocanegra, Julio Nogales-Bueno, Francisco José Heredia and José Miguel Hernández-Hierro

Sensors 2018, 18(8), 2426; https://doi.org/10.3390/s18082426 - 26 Jul 2018

Cited by 7 | Viewed by 3326

Abstract

Near infrared hyperspectral data were collected for 200 Syrah and Tempranillo grape seed samples. Next, a sample selection was carried out and the phenolic content of these samples was determined. Then, quantitative (modified partial least square regressions) and qualitative (K-means and lineal discriminant analyses) chemometric tools were applied to obtain the best models for predicting the reference parameters. Quantitative models developed for the prediction of total phenolic and flavanolic contents have been successfully developed with standard errors of prediction (SEP) in external validation similar to those previously reported. For these parameters, SEPs were respectively, 11.23 mg g⁻¹ of grape seed, expressed as gallic acid equivalents and 4.85 mg g⁻¹ of grape seed, expressed as catechin equivalents. The application of these models to the whole sample set (selected and non-selected samples) has allowed knowing the distributions of total phenolic and flavanolic contents in this set. Moreover, a discriminant function has been calculated and applied to know the phenolic extractability level of the samples. On average, this discrimination function has allowed a 76.92% of samples correctly classified according their extractability level. In this way, the bases for the control of grape seeds phenolic state from their near infrared spectra have been stablished. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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25 pages, 4537 KiB

Open AccessArticle

Thermal Response of Jointed Rock Masses Inferred from Infrared Thermographic Surveying (Acuto Test-Site, Italy)

by Matteo Fiorucci, Gian Marco Marmoni, Salvatore Martino and Paolo Mazzanti

Sensors 2018, 18(7), 2221; https://doi.org/10.3390/s18072221 - 10 Jul 2018

Cited by 40 | Viewed by 4373

Abstract

The Mediterranean region is affected by considerable daily and seasonal temperature variations due to intense solar radiation. In mid-seasons, thermal excursions can exceed tens of degrees thus influencing the long-term behaviour of jointed rock masses acting as a preparatory factor for rock slope instabilities. In order to evaluate the thermal response of a densely jointed rock-block, monitoring has been in operation since 2016 by direct and remote sensing techniques in an abandoned quarry in Acuto (central Italy). Monthly InfraRed Thermographic (IRT) surveys were carried out on its exposed faces and along sections of interest across monitored main joints. The results highlight the daily and seasonal cyclical behaviour, constraining amplitudes and rates of heating and cooling phases. The temperature time-series revealed the effect of sun radiation and exposure on thermal response of the rock-block, which mainly depends on the seasonal conditions. The influence of opened joints in the heat propagation is revealed by the differential heating experienced across it, which was verified under 1D and 2D analysis. IRT has proved to be a valid monitoring technique in supporting traditional approaches, for the definition of the surficial temperature distribution on rock masses or stone building materials. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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15 pages, 3094 KiB

Open AccessArticle

Thermal Imaging Metrology with a Smartphone Sensor

by Leigh Russell Stanger, Thomas Charles Wilkes, Nicholas Andrew Boone, Andrew John Samuel McGonigle and Jon Raffe Willmott

Sensors 2018, 18(7), 2169; https://doi.org/10.3390/s18072169 - 6 Jul 2018

Cited by 9 | Viewed by 6047

Abstract

Thermal imaging cameras are expensive, particularly those designed for measuring high temperature objects with low measurement uncertainty. A wide range of research and industrial applications would benefit from lower cost temperature imaging sensors with improved metrology. To address this problem, we present the first ever quantification methodology for the temperature measurement performance of an ultra-low cost thermal imaging system based on a smartphone sensor. The camera was formed from a back illuminated silicon Complementary Metal Oxide Semiconductor (CMOS) sensor, developed for the smartphone camera market. It was packaged for use with a Raspberry Pi computer. We designed and fitted a custom-made triplet lens assembly. The system performance was characterised with a range of state-of-the-art techniques and metrics: establishing a temperature resolution of below 10 °C in the range 600–1000 °C. Furthermore, the scene dependent aspects of combined uncertainty were considered. The minimum angular subtense for which an accurate thermal measurement could be made was determined to be 1.35°, which corresponds to a 23 mm bar at a distance of 1 m, or 45:1 field-of-view in radiation thermometer nomenclature. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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17 pages, 4051 KiB

Open AccessArticle

Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images

by Xavier Soria, Angel D. Sappa and Riad I. Hammoud

Sensors 2018, 18(7), 2059; https://doi.org/10.3390/s18072059 - 27 Jun 2018

Cited by 16 | Viewed by 7294

Abstract

Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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12 pages, 1581 KiB

Open AccessArticle

Estimating Respiratory Rate in Post-Anesthesia Care Unit Patients Using Infrared Thermography: An Observational Study

by Nadine Hochhausen, Carina Barbosa Pereira, Steffen Leonhardt, Rolf Rossaint and Michael Czaplik

Sensors 2018, 18(5), 1618; https://doi.org/10.3390/s18051618 - 18 May 2018

Cited by 34 | Viewed by 4586

Abstract

The post-anesthesia care unit (PACU) is the central hub for recovery after surgery, especially when the surgery is performed under general anesthesia. Aside from clinical aspects, respiratory impairment is one of the major causes of morbidity and affected recovery in the PACU and should therefore be monitored. In previous studies, infrared thermography was applied to assess the breathing rate (BR) of healthy volunteers. Here, the transferability of published methods for postoperative patients in the PACU was examined. Video recordings of 28 patients were acquired using a long-wave infrared camera, and analyzed offline. For validation purposes, BRs derived from body surface electrocardiography were measured simultaneously. In general, a close agreement between the two techniques (r = 0.607, p = 0.002 upon arrival, and r = 0.849, p < 0.001 upon discharge from the PACU) was obtained. In conclusion, the algorithm was demonstrated to be feasible and reliable under these challenging conditions. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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18 pages, 3237 KiB

Open AccessArticle

Multi-Touch Tabletop System Using Infrared Image Recognition for User Position Identification

by Shota Suto, Toshiya Watanabe, Susumu Shibusawa and Masaru Kamada

Sensors 2018, 18(5), 1559; https://doi.org/10.3390/s18051559 - 14 May 2018

Cited by 6 | Viewed by 4294

Abstract

A tabletop system can facilitate multi-user collaboration in a variety of settings, including small meetings, group work, and education and training exercises. The ability to identify the users touching the table and their positions can promote collaborative work among participants, so methods have been studied that involve attaching sensors to the table, chairs, or to the users themselves. An effective method of recognizing user actions without placing a burden on the user would be some type of visual process, so the development of a method that processes multi-touch gestures by visual means is desired. This paper describes the development of a multi-touch tabletop system using infrared image recognition for user position identification and presents the results of touch-gesture recognition experiments and a system-usability evaluation. Using an inexpensive FTIR touch panel and infrared light, this system picks up the touch areas and the shadow area of the user’s hand by an infrared camera to establish an association between the hand and table touch points and estimate the position of the user touching the table. The multi-touch gestures prepared for this system include an operation to change the direction of an object to face the user and a copy operation in which two users generate duplicates of an object. The system-usability evaluation revealed that prior learning was easy and that system operations could be easily performed. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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19 pages, 1795 KiB

Open AccessArticle

Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images

by Atmane Khellal, Hongbin Ma and Qing Fei

Sensors 2018, 18(5), 1490; https://doi.org/10.3390/s18051490 - 9 May 2018

Cited by 56 | Viewed by 5717

Abstract

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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8 pages, 2595 KiB

Open AccessArticle

Infrared Thermography Approach for Effective Shielding Area of Field Smoke Based on Background Subtraction and Transmittance Interpolation

by Runze Tang, Tonglai Zhang, Yongpeng Chen, Hao Liang, Bingyang Li and Zunning Zhou

Sensors 2018, 18(5), 1450; https://doi.org/10.3390/s18051450 - 6 May 2018

Cited by 5 | Viewed by 4027

Abstract

Effective shielding area is a crucial indicator for the evaluation of the infrared smoke-obscuring effectiveness on the battlefield. The conventional methods for assessing the shielding area of the smoke screen are time-consuming and labor intensive, in addition to lacking precision. Therefore, an efficient and convincing technique for testing the effective shielding area of the smoke screen has great potential benefits in the smoke screen applications in the field trial. In this study, a thermal infrared sensor with a mid-wavelength infrared (MWIR) range of 3 to 5 μm was first used to capture the target scene images through clear as well as obscuring smoke, at regular intervals. The background subtraction in motion detection was then applied to obtain the contour of the smoke cloud at each frame. The smoke transmittance at each pixel within the smoke contour was interpolated based on the data that was collected from the image. Finally, the smoke effective shielding area was calculated, based on the accumulation of the effective shielding pixel points. One advantage of this approach is that it utilizes only one thermal infrared sensor without any other additional equipment in the field trial, which significantly contributes to the efficiency and its convenience. Experiments have been carried out to demonstrate that this approach can determine the effective shielding area of the field infrared smoke both practically and efficiently. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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15 pages, 6821 KiB

Open AccessArticle

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging

by Bingquan Chu, Keqiang Yu, Yanru Zhao and Yong He

Sensors 2018, 18(4), 1259; https://doi.org/10.3390/s18041259 - 19 Apr 2018

Cited by 29 | Viewed by 6189

Abstract

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874–1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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13 pages, 21821 KiB

Open AccessArticle

Infrared and Visible Image Fusion Based on Different Constraints in the Non-Subsampled Shearlet Transform Domain

by Yan Huang, Duyan Bi and Dongpeng Wu

Sensors 2018, 18(4), 1169; https://doi.org/10.3390/s18041169 - 11 Apr 2018

Cited by 29 | Viewed by 3747

Abstract

There are many artificial parameters when fuse infrared and visible images, to overcome the lack of detail in the fusion image because of the artifacts, a novel fusion algorithm for infrared and visible images that is based on different constraints in non-subsampled shearlet transform (NSST) domain is proposed. There are high bands and low bands of images that are decomposed by the NSST. After analyzing the characters of the bands, fusing the high level bands by the gradient constraint, the fused image can obtain more details; fusing the low bands by the constraint of saliency in the images, the targets are more salient. Before the inverse NSST, the Nash equilibrium is used to update the coefficient. The fused images and the quantitative results demonstrate that our method is more effective in reserving details and highlighting the targets when compared with other state-of-the-art methods. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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13 pages, 2604 KiB

Open AccessArticle

Edge-Aware Unidirectional Total Variation Model for Stripe Non-Uniformity Correction

by Ayoub Boutemedjet, Chenwei Deng and Baojun Zhao

Sensors 2018, 18(4), 1164; https://doi.org/10.3390/s18041164 - 11 Apr 2018

Cited by 24 | Viewed by 3905

Abstract

The problem of stripe non-uniformity in array-based infrared imaging systems has been the focus of many research studies. Among the proposed correction techniques, total variation models have been proven to significantly reduce the effect of this type of noise on the captured image. However, they also cause the loss of some image details and textures due to over-smoothing effect. In this paper, a correction scheme is proposed based on unidirectional variation model to exploit the direction characteristic of the stripe noise, in which an edge-aware weighting is incorporated to convey image structure retaining ability to the overall algorithm. Moreover, a statistical-based regularization is also introduced to further enhance correction performance around strong edges. The proposed approach is thoroughly scrutinized and compared to the state-of-the-art de-striping techniques using real stripe non-uniform images. Results demonstrate a significant improvement in edge preservation with better correction performance. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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22 pages, 22265 KiB

Open AccessArticle

Convolutional Neural Network-Based Classification of Driver’s Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors

by Kwan Woo Lee, Hyo Sik Yoon, Jong Min Song and Kang Ryoung Park

Sensors 2018, 18(4), 957; https://doi.org/10.3390/s18040957 - 23 Mar 2018

Cited by 52 | Viewed by 6162

Abstract

Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver’s body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver’s emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver’s face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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12 pages, 1593 KiB

Open AccessCommunication

First Evaluation of Infrared Thermography as a Tool for the Monitoring of Udder Health Status in Farms of Dairy Cows

by Mauro Zaninelli, Veronica Redaelli, Fabio Luzi, Valerio Bronzo, Malcolm Mitchell, Vittorio Dell’Orto, Valentino Bontempo, Donata Cattaneo and Giovanni Savoini

Sensors 2018, 18(3), 862; https://doi.org/10.3390/s18030862 - 14 Mar 2018

Cited by 68 | Viewed by 5879

Abstract

The aim of the present study was to test infrared thermography (IRT), under field conditions, as a possible tool for the evaluation of cow udder health status. Thermographic images (n. 310) from different farms (n. 3) were collected and evaluated using a dedicated software application to calculate automatically and in a standardized way, thermographic indices of each udder. Results obtained have confirmed a significant relationship between udder surface skin temperature (USST) and classes of somatic cell count in collected milk samples. Sensitivity and specificity in the classification of udder health were: 78.6% and 77.9%, respectively, considering a level of somatic cell count (SCC) of 200,000 cells/mL as a threshold to classify a subclinical mastitis or 71.4% and 71.6%, respectively when a threshold of 400,000 cells/mL was adopted. Even though the sensitivity and specificity were lower than in other published papers dealing with non-automated analysis of IRT images, they were considered acceptable as a first field application of this new and developing technology. Future research will permit further improvements in the use of IRT, at farm level. Such improvements could be attained through further image processing and enhancement, and the application of indicators developed and tested in the present study with the purpose of developing a monitoring system for the automatic and early detection of mastitis in individual animals on commercial farms. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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20 pages, 2535 KiB

Open AccessArticle

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor

by Xin Li, Xingtao Ou, Zhi Li, Henglu Wei, Wei Zhou and Zhemin Duan

Sensors 2018, 18(2), 433; https://doi.org/10.3390/s18020433 - 2 Feb 2018

Cited by 7 | Viewed by 4252

Abstract

Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2

^{\circ}

C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times. Full article

(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)

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