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Robust Visibility Surface Determination in Object Space via Plücker Coordinates -
Determining Chess Game State from an Image -
Utilizing a Terrestrial Laser Scanner for 3D Luminance Measurement of Indoor Environments -
CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks -
From IR Images to Point Clouds to Pose: Point Cloud-Based AR Glasses Pose Estimation
Journal Description
Journal of Imaging
Journal of Imaging
is an international, multi/interdisciplinary, peer-reviewed, open access journal of imaging techniques published online monthly by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), PubMed, PMC, dblp, Inspec, and many other databases.
- Journal Rank: CiteScore - Q1 (Radiology, Nuclear Medicine and Imaging)
- Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 17.6 days after submission; acceptance to publication is undertaken in 4.5 days (median values for papers published in this journal in the first half of 2021).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
Mobile-Based 3D Modeling: An In-Depth Evaluation for the Application in Indoor Scenarios
J. Imaging 2021, 7(9), 167; https://doi.org/10.3390/jimaging7090167 (registering DOI) - 29 Aug 2021
Abstract
Indoor environment modeling has become a relevant topic in several application fields, including augmented, virtual, and extended reality. With the digital transformation, many industries have investigated two possibilities: generating detailed models of indoor environments, allowing viewers to navigate through them; and mapping surfaces
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Indoor environment modeling has become a relevant topic in several application fields, including augmented, virtual, and extended reality. With the digital transformation, many industries have investigated two possibilities: generating detailed models of indoor environments, allowing viewers to navigate through them; and mapping surfaces so as to insert virtual elements into real scenes. The scope of the paper is twofold. We first review the existing state-of-the-art (SoA) of learning-based methods for 3D scene reconstruction based on structure from motion (SFM) that predict depth maps and camera poses from video streams. We then present an extensive evaluation using a recent SoA network, with particular attention on the capability of generalizing on new unseen data of indoor environments. The evaluation was conducted by using the absolute relative (AbsRel) measure of the depth map prediction as the baseline metric.
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(This article belongs to the Section Mixed, Augmented and Virtual Reality)
Open AccessArticle
Visible Light Spectrum Extraction from Diffraction Images by Deconvolution and the Cepstrum
J. Imaging 2021, 7(9), 166; https://doi.org/10.3390/jimaging7090166 (registering DOI) - 28 Aug 2021
Abstract
Accurate color determination in variable lighting conditions is difficult and requires special devices. We considered the task of extracting the visible light spectrum using ordinary camera sensors, to facilitate low-cost color measurements using consumer equipment. The approach uses a diffractive element attached to
[...] Read more.
Accurate color determination in variable lighting conditions is difficult and requires special devices. We considered the task of extracting the visible light spectrum using ordinary camera sensors, to facilitate low-cost color measurements using consumer equipment. The approach uses a diffractive element attached to a standard camera and a computational algorithm for forming the light spectrum from the resulting diffraction images. We present two machine learning algorithms for this task, based on alternative processing pipelines using deconvolution and cepstrum operations, respectively. The proposed methods were trained and evaluated on diffraction images collected using three cameras and three illuminants to demonstrate the generality of the approach, measuring the quality by comparing the recovered spectra against ground truth measurements collected using a hyperspectral camera. We show that the proposed methods are able to reconstruct the spectrum, and, consequently, the color, with fairly good accuracy in all conditions, but the exact accuracy depends on the specific camera and lighting conditions. The testing procedure followed in our experiments suggests a high degree of confidence in the generalizability of our results; the method works well even for a new illuminant not seen in the development phase.
Full article
(This article belongs to the Section Color, Multi-spectral, and Hyperspectral Imaging)
Open AccessReview
Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey
J. Imaging 2021, 7(9), 165; https://doi.org/10.3390/jimaging7090165 - 27 Aug 2021
Abstract
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this
[...] Read more.
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.
Full article
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)
Open AccessArticle
SpineDepth: A Multi-Modal Data Collection Approach for Automatic Labelling and Intraoperative Spinal Shape Reconstruction Based on RGB-D Data
by
, , , , , , and
J. Imaging 2021, 7(9), 164; https://doi.org/10.3390/jimaging7090164 - 27 Aug 2021
Abstract
Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these
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Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these procedures. However, developing such methods requires vast amount of data. To this end, a multi-modal approach that enables acquisition of large clinical data, tailored to pedicle screw placement, using RGB-D sensors and a co-calibrated high-end optical tracking system was developed. The resulting dataset comprises RGB-D recordings of pedicle screw placement along with individually tracked ground truth poses and shapes of spine levels L1–L5 from ten cadaveric specimens. Besides a detailed description of our setup, quantitative and qualitative outcome measures are provided. We found a mean target registration error of 1.5 mm. The median deviation between measured and ground truth bone surface was 2.4 mm. In addition, a surgeon rated the overall alignment based on 10% random samples as 5.8 on a scale from 1 to 6. Generation of labeled RGB-D data for orthopedic interventions with satisfactory accuracy is feasible, and its publication shall promote future development of data-driven artificial intelligence methods for fast and reliable intraoperative registration.
Full article
(This article belongs to the Special Issue The Application of Imaging Technology in Medical Intervention and Surgery)
Open AccessArticle
Comparative Study of Data Matrix Codes Localization and Recognition Methods
by
and
J. Imaging 2021, 7(9), 163; https://doi.org/10.3390/jimaging7090163 - 27 Aug 2021
Abstract
We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image
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We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image processing techniques such as edge detection, adaptive thresholding, or connected component labeling are used to identify the Finder Pattern. The recognition rate of the compared methods was tested on a set of images with Data Matrix codes, which is published together with the article. The experimental results show that methods based on adaptive thresholding achieved a better recognition rate than methods based on edge detection.
Full article
(This article belongs to the Special Issue Edge Detection Evaluation)
Open AccessArticle
Ground Truth Data Generator for Eye Location on Infrared Driver Recordings
by
and
J. Imaging 2021, 7(9), 162; https://doi.org/10.3390/jimaging7090162 - 27 Aug 2021
Abstract
Labeling is a very costly and time consuming process that aims to generate datasets for training neural networks in several functionalities and projects. In the automotive field of driver monitoring it has a huge impact, where much of the budget is used for
[...] Read more.
Labeling is a very costly and time consuming process that aims to generate datasets for training neural networks in several functionalities and projects. In the automotive field of driver monitoring it has a huge impact, where much of the budget is used for image labeling. This paper presents an algorithm that will be used for generating ground truth data for 2D eye location in infrared images of drivers. The algorithm is implemented with many detection restrictions, which makes it very accurate but not necessarily very constant. The resulting dataset shall not be modified by any human factor and will be used to train neural networks, which we expect to have a very good accuracy and a much better consistency for eye detection than the initial algorithm. This paper proves that we can automatically generate very good quality ground truth data for training neural networks, which is still an open topic in the automotive industry.
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(This article belongs to the Special Issue Imaging Studies for Face and Gesture Analysis)
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Open AccessArticle
Efficient Face Recognition System for Operating in Unconstrained Environments
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, , , , and
J. Imaging 2021, 7(9), 161; https://doi.org/10.3390/jimaging7090161 - 26 Aug 2021
Abstract
Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient
[...] Read more.
Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.
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(This article belongs to the Special Issue 3D Human Understanding)
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A Benchmark Evaluation of Adaptive Image Compression for Multi Picture Object Stereoscopic Images
J. Imaging 2021, 7(8), 160; https://doi.org/10.3390/jimaging7080160 - 23 Aug 2021
Abstract
A stereopair consists of two pictures related to the same subject taken by two different points of view. Since the two images contain a high amount of redundant information, new compression approaches and data formats are continuously proposed, which aim to reduce the
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A stereopair consists of two pictures related to the same subject taken by two different points of view. Since the two images contain a high amount of redundant information, new compression approaches and data formats are continuously proposed, which aim to reduce the space needed to store a stereoscopic image while preserving its quality. A standard for multi-picture image encoding is represented by the MPO format (Multi-Picture Object). The classic stereoscopic image compression approaches compute a disparity map between the two views, which is stored with one of the two views together with a residual image. An alternative approach, named adaptive stereoscopic image compression, encodes just the two views independently with different quality factors. Then, the redundancy between the two views is exploited to enhance the low quality image. In this paper, the problem of stereoscopic image compression is presented, with a focus on the adaptive stereoscopic compression approach, which allows us to obtain a standardized format of the compressed data. The paper presents a benchmark evaluation on large and standardized datasets including 60 stereopairs that differ by resolution and acquisition technique. The method is evaluated by varying the amount of compression, as well as the matching and optimization methods resulting in 16 different settings. The adaptive approach is also compared with other MPO-compliant methods. The paper also presents an Human Visual System (HVS)-based assessment experiment which involved 116 people in order to verify the perceived quality of the decoded images.
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(This article belongs to the Special Issue New and Specialized Methods of Image Compression)
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Open AccessArticle
Usability of Graphical Visualizations on a Tool-Mounted Interface for Spine Surgery
J. Imaging 2021, 7(8), 159; https://doi.org/10.3390/jimaging7080159 - 21 Aug 2021
Abstract
Screw placement in the correct angular trajectory is one of the most intricate tasks during spinal fusion surgery. Due to the crucial role of pedicle screw placement for the outcome of the operation, spinal navigation has been introduced into the clinical routine. Despite
[...] Read more.
Screw placement in the correct angular trajectory is one of the most intricate tasks during spinal fusion surgery. Due to the crucial role of pedicle screw placement for the outcome of the operation, spinal navigation has been introduced into the clinical routine. Despite its positive effects on the precision and safety of the surgical procedure, local separation of the navigation information and the surgical site, combined with intricate visualizations, limit the benefits of the navigation systems. Instead of a tech-driven design, a focus on usability is required in new research approaches to enable advanced and effective visualizations. This work presents a new tool-mounted interface (TMI) for pedicle screw placement. By fixing a TMI onto the surgical instrument, physical de-coupling of the anatomical target and navigation information is resolved. A total of 18 surgeons participated in a usability study comparing the TMI to the state-of-the-art visualization on an external screen. With the usage of the TMI, significant improvements in system usability (Kruskal–Wallis test p < 0.05) were achieved. A significant reduction in mental demand and overall cognitive load, measured using a NASA-TLX (p < 0.05), were observed. Moreover, a general improvement in performance was shown by means of the surgical task time (one-way ANOVA p < 0.001).
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(This article belongs to the Special Issue The Application of Imaging Technology in Medical Intervention and Surgery)
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Open AccessArticle
A Dataset of Annotated Omnidirectional Videos for Distancing Applications
J. Imaging 2021, 7(8), 158; https://doi.org/10.3390/jimaging7080158 - 21 Aug 2021
Abstract
Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two
[...] Read more.
Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications.
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(This article belongs to the Special Issue 2020 Selected Papers from Journal of Imaging Editorial Board Members)
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Multimodal Emotion Recognition from Art Using Sequential Co-Attention
J. Imaging 2021, 7(8), 157; https://doi.org/10.3390/jimaging7080157 - 21 Aug 2021
Abstract
In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the model to focus on learning informative and refined representations for
[...] Read more.
In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the model to focus on learning informative and refined representations for both feature extraction and modality fusion. The resulting system can be used to categorize artworks according to the emotions they evoke; recommend paintings that accentuate or balance a particular mood; search for paintings of a particular style or genre that represents custom content in a custom state of impact. Experimental results on the WikiArt emotion dataset showed the efficiency of the approach proposed and the usefulness of three modalities in emotion recognition.
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(This article belongs to the Special Issue Fine Art Pattern Extraction and Recognition)
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Documenting Paintings with Gigapixel Photography
J. Imaging 2021, 7(8), 156; https://doi.org/10.3390/jimaging7080156 - 21 Aug 2021
Abstract
Digital photographic capture of pictorial artworks with gigapixel resolution (around 1000 megapixels or greater) is a novel technique that is beginning to be used by some important international museums as a means of documentation, analysis, and dissemination of their masterpieces. This line of
[...] Read more.
Digital photographic capture of pictorial artworks with gigapixel resolution (around 1000 megapixels or greater) is a novel technique that is beginning to be used by some important international museums as a means of documentation, analysis, and dissemination of their masterpieces. This line of research is extremely interesting, not only for art curators and scholars but also for the general public. The results can be disseminated through online virtual museum displays, offering a detailed interactive visualization. These virtual visualizations allow the viewer to delve into the artwork in such a way that it is possible to zoom in and observe those details, which would be negligible to the naked eye in a real visit. Therefore, this kind of virtual visualization using gigapixel images has become an essential tool to enhance cultural heritage and to make it accessible to everyone. Since today’s professional digital cameras provide images of around 40 megapixels, obtaining gigapixel images requires some special capture and editing techniques. This article describes a series of photographic methodologies and equipment, developed by the team of researchers, that have been put into practice to achieve a very high level of detail and chromatic fidelity, in the documentation and dissemination of pictorial artworks. The result of this research work consisted in the gigapixel documentation of several masterpieces of the Museo de Bellas Artes of Valencia, one of the main art galleries in Spain. The results will be disseminated through the Internet, as will be shown with some examples.
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(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)
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Dataset Growth in Medical Image Analysis Research
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and
J. Imaging 2021, 7(8), 155; https://doi.org/10.3390/jimaging7080155 - 20 Aug 2021
Abstract
Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI
[...] Read more.
Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3–10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.
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(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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Design of an Ultrasound-Navigated Prostate Cancer Biopsy System for Nationwide Implementation in Senegal
by
, , , , , , , , and
J. Imaging 2021, 7(8), 154; https://doi.org/10.3390/jimaging7080154 - 20 Aug 2021
Abstract
This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods
[...] Read more.
This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on “frugal technology”, intended to be affordable for low–middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.
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(This article belongs to the Special Issue The Application of Imaging Technology in Medical Intervention and Surgery)
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Spline-Based Dense Medial Descriptors for Lossy Image Compression
J. Imaging 2021, 7(8), 153; https://doi.org/10.3390/jimaging7080153 - 19 Aug 2021
Abstract
Medial descriptors are of significant interest for image simplification, representation, manipulation, and compression. On the other hand, B-splines are well-known tools for specifying smooth curves in computer graphics and geometric design. In this paper, we integrate the two by modeling medial descriptors with
[...] Read more.
Medial descriptors are of significant interest for image simplification, representation, manipulation, and compression. On the other hand, B-splines are well-known tools for specifying smooth curves in computer graphics and geometric design. In this paper, we integrate the two by modeling medial descriptors with stable and accurate B-splines for image compression. Representing medial descriptors with B-splines can not only greatly improve compression but is also an effective vector representation of raster images. A comprehensive evaluation shows that our Spline-based Dense Medial Descriptors (SDMD) method achieves much higher compression ratios at similar or even better quality to the well-known JPEG technique. We illustrate our approach with applications in generating super-resolution images and salient feature preserving image compression.
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(This article belongs to the Special Issue New and Specialized Methods of Image Compression)
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A Novel Methodology for Measuring the Abstraction Capabilities of Image Recognition Algorithms
J. Imaging 2021, 7(8), 152; https://doi.org/10.3390/jimaging7080152 - 19 Aug 2021
Abstract
Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present
[...] Read more.
Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.
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(This article belongs to the Section AI in Imaging)
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A Virtual Reality System for Improved Image-Based Planning of Complex Cardiac Procedures
by
, , , , , , , , , , , , and
J. Imaging 2021, 7(8), 151; https://doi.org/10.3390/jimaging7080151 - 19 Aug 2021
Abstract
The intricate nature of congenital heart disease requires understanding of the complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens, and therefore, display
[...] Read more.
The intricate nature of congenital heart disease requires understanding of the complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens, and therefore, display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, the control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper, we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography, which enables fast prototyping for visualisation such as volume rendering, multiplanar reformatting, flow visualisation and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation and multiuser interactions. The available features were evaluated by imaging and nonimaging clinicians, showing that the virtual reality system can help improve the understanding and communication of three-dimensional echocardiography imaging and potentially benefit congenital heart disease treatment.
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(This article belongs to the Special Issue The Application of Imaging Technology in Medical Intervention and Surgery)
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Hue-Preserving Saturation Improvement in RGB Color Cube
J. Imaging 2021, 7(8), 150; https://doi.org/10.3390/jimaging7080150 - 18 Aug 2021
Abstract
This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram
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This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement.
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(This article belongs to the Special Issue Advances in Color Imaging)
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Classification of Geometric Forms in Mosaics Using Deep Neural Network
J. Imaging 2021, 7(8), 149; https://doi.org/10.3390/jimaging7080149 - 18 Aug 2021
Abstract
The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that
[...] Read more.
The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.
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(This article belongs to the Special Issue Fine Art Pattern Extraction and Recognition)
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An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer
J. Imaging 2021, 7(8), 148; https://doi.org/10.3390/jimaging7080148 - 17 Aug 2021
Abstract
Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone
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Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its high sensitivity and widespread availability at nuclear medicine facilities. To date, the assessment of bone scans relies solely on the interpretation of an expert physician who visually assesses the scan. Besides this being a time consuming task, it is also subjective, as there is no absolute criteria neither to identify bone metastases neither to quantify them by a straightforward and universally accepted procedure. In this paper, a new algorithm for the false positives reduction of automatically detected hotspots in bone scintigraphy images is proposed. The motivation relies in the difficulty of building a fully annotated database. In this way, our algorithm is a semisupervised method that works in an iterative way. The ultimate goal is to provide the physician with a fast, precise and reliable tool to quantify bone scans and evaluate disease progression and response to treatment. The algorithm is tested in a set of bone scans manually labeled according to the patient’s medical record. The achieved classification sensitivity, specificity and false negative rate were 63%, 58% and 37%, respectively. Comparison with other state-of-the-art classification algorithms shows superiority of the proposed method.
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(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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Sensors, Future Internet, Information, J. Imaging, MTI
Augmented and Mixed Reality
Editors-in-Chief: Andrea Sanna, Federico Manuri, Francesco De PaceDeadline: 31 May 2022
Topic in
Applied Sciences, Biology, J. Imaging, Technologies
Digital Biotechnology
Editor-in-Chief: Aristotelis ChatziioannouDeadline: 31 December 2022
Conferences
Special Issues
Special Issue in
J. Imaging
Crime Prevention, Detection and Investigation Using Digital Evidence and Artificial Intelligence
Guest Editors: Sule Yildirim Yayilgan, Edlira Kalemi VakajDeadline: 31 August 2021
Special Issue in
J. Imaging
Blind Image Restoration
Guest Editor: Yitzhak YitzhakyDeadline: 20 September 2021
Special Issue in
J. Imaging
Radiomics and Texture Analysis in Medical Imaging
Guest Editors: Renato Cuocolo, Lorenzo Ugga, Valeria RomeoDeadline: 1 October 2021
Special Issue in
J. Imaging
Visual Localization
Guest Editor: Rémi BoutteauDeadline: 20 October 2021



