Geometric Distortion Research Articles

OMIRD: Orientated Modality Independent Region Descriptor for Optical-to-SAR Image Matching

Image matching between optical and SAR images remains challenging due to their different imaging principles. Despite recent developments in image matching methods for optical-to-SAR images, few have seen practical application success due to the significant geometric and radiometric differences between the two modalities. In this letter, we propose a new method called the Orientated Modality Independent Region Descriptor (OMIRD) to overcome these challenges. Our template-based approach effectively extracts consistent structural and textural features from both optical and SAR images. Additionally, we introduce a principal orientation selection method, using the Ratio of Exponentially Weighted Averages (ROEWA) and logarithm Histogram of Gradient (HOG) to identify the most meaningful feature orientations and further simplify the OMIRD descriptor. The experimental results indicate that the proposed OMIRD exhibits superior matching accuracy and robustness under various geometric distortions and non-linear grayscale variations.

Encapsulation of Astatide by a water cage.

A 512 cage of (H2O)20 consisting of 30 hydrogen bonds encapsulates Astatide with little geometrical distortion. The cage is marginally destabilized but the non-covalent interactions are actually strengthened. Host⋯cage interactions in the [At@(H2O)20]- cluster are anti-electrostatic, placing both negatively charged atoms in direct contact as in Atδ-⋯δ-O-Hδ+. An orbital interaction analysis reveals that explicit host⋯cage contacts are "inverted" hydrogen bonds. That is, the same type of donor→acceptor charge transfer as in hydrogen bonding, with no proton bridging the two negative charges.

Usability of magnetic resonance images acquired at a novel low-field 0.55T scanner for brain radiotherapy treatment planning.

Low-field magnetic resonance imaging (MRI) may offer specific advantages over high-field MRI, e.g. lower susceptibility-dependent distortions and simpler installation. The study aim was to evaluate if a novel 0.55T MRI scanner provides sufficient image accuracy and quality for radiotherapy (RT) treatment planning. The geometric accuracy of images acquired at a low-field MRI scanner was evaluated in phantom measurements regarding gradient non-linearity-related distortions. Patient-induced B0-susceptibility changes were investigated via B0-field-mapping in ten volunteers. Patients were positioned in RT-setup using a 3D-printed insert for the head/neck-coil that was tested for sufficient signal-to-noise-ratio (SNR). The suitability of the MRI-system for detection of metastases was evaluated in eleven patients. In comparison to diagnostic images, acquired at ≥1.5T, three physicians evaluated the detectability of metastases by counting them in low- and high-field-images, respectively. The phantom measurements showed a high imaging fidelity after 3D-distortion-correction with (1.2±0.9) mm geometric distortion in 10cm radius from isocentre. At the edges remaining distortions were greater than at 1.5T. The mean susceptibility-induced distortions in the head were (0.05±0.05) mm and maximum 0.69mm. SNR analysis showed that optimised positioning of RT-patients without signal loss in the head/neck-coil was possible with the RT-insert. No significant differences (p=0.48) in detectability of metastases were found. The 0.55T MRI system provided sufficiently geometrically accurate and high-resolution images that can be used for RT-planning for brain metastases. Hence, modern low-field MRI may contribute to simply access MRI for RT-planning after further investigations.

Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition.

Leveraging the advances of natural language processing, most recent scene text recognizers adopt an encoder-decoder architecture where text images are first converted to representative features and then a sequence of characters via 'sequential decoding'. However, scene text images suffer from rich noises of different sources such as complex background and geometric distortions which often confuse the decoder and lead to incorrect alignment of visual features at noisy decoding time steps. This paper presents I2C2W, a novel scene text recognition technique that is tolerant to geometric and photometric degradation by decomposing scene text recognition into two inter-connected tasks. The first task focuses on image-to-character (I2C) mapping which detects a set of character candidates from images based on different alignments of visual features in an non-sequential way. The second task tackles character-to-word (C2W) mapping which recognizes scene text by decoding words from the detected character candidates. The direct learning from character semantics (instead of noisy image features) corrects falsely detected character candidates effectively which improves the final text recognition accuracy greatly. Extensive experiments over nine public datasets show that the proposed I2C2W outperforms the state-of-the-art by large margins for challenging scene text datasets with various curvature and perspective distortions. It also achieves very competitive recognition performance over multiple normal scene text datasets.

Quantum Geometric Transformation Based on QIRHSI Quantum Color images

The negative effects caused by geometric distortion can be removed to the maximum extent possible through appropriate geometric transformations, allowing us to focus on the image content itself in subsequent processing and recognition. Therefore, geometric transformations are often used as a pre-processing step for other image processing applications. In this paper, quantum algorithms are designed to implement geometric transformations, including two-point swapping, circular translation, flipping transformations and right-angle rotation, across a quantum image representation QIRHSI (Quantum Image Representation based on HSI color space) which is built on HSI (Hue-Saturation-Intensity) color space. The above geometric transformations are realized by quantum circuits composed of elementary quantum gates. By analyzing the complexity of the fundamental quantum gates needed for the above geometric transformations, it is found that the global transformations (circular translation, flip transformation and right-angle rotation) are lower than the local transformation (two-point swapping). The proposed geometric transforms are used to facilitate the applications of quantum images with low complexity and high efficiency.

Rapid distortion correction enables accurate magnetic resonance imaging-guided real-time adaptive radiotherapy.

Magnetic resonance imaging (MRI)-Linac systems combine simultaneous MRI with radiation delivery, allowing treatments to be guided by anatomically detailed, real-time images. However, MRI can be degraded by geometric distortions that cause uncertainty between imaged and actual anatomy. In this work, we develop and integrate a real-time distortion correction method that enables accurate real-time adaptive radiotherapy. The method was based on the pre-treatment calculation of distortion and the rapid correction of intrafraction images. A motion phantom was set up in an MRI-Linac at isocentre (P0 ), the edge (P 1) and just outside (P 2) the imaging volume. The target was irradiated and tracked during real-time adaptive radiotherapy with and without the distortion correction. The geometric tracking error and latency were derived from the measurements of the beam and target positions in the EPID images. Without distortion correction, the mean geometric tracking error was 1.3mm at P 1 and 3.1mm at P 2. When distortion correction was applied, the error was reduced to 1.0mm at P 1 and 1.1mm at P 2. The corrected error was similar to an error of 0.9mm at P0 where the target was unaffected by distortion indicating that this method has accurately accounted for distortion during tracking. The latency was 319±12ms without distortion correction and 335±34ms with distortion correction. We have demonstrated a real-time distortion correction method that maintains accurate radiation delivery to the target, even at treatment locations with large distortion.

Omnidirectional Image Super-Resolution via Latitude Adaptive Network

Omnidirectional images (ODI), also known as 360 images, have recently attracted extensive attention from both academia and industry. However, due to storage and transmission limitations, ODIs are usually at extremely low resolution. Thus, it is necessary to restore a high-resolution ODI from a low-resolution ODI, i.e., omnidirectional image super-resolution (ODI-SR). Different from traditional two-dimensional (2D) image SR, the challenge of ODI-SR is the nonuniformly distributed pixel density and geometric distortion across latitudes, which makes traditional SR methods difficult to be applied in ODI-SR. Towards ODI-SR, we propose in this paper a novel latitude-aware upscaling network, namely LAU-Net+, which fully considers the above characteristics of ODIs. In our network, different latitude bands can learn to adopt distinct upscaling factors, which significantly saves the computational resources and improves the SR efficiency. Specifically, a Laplacian multilevel pyramid network is introduced in which the upscaling factor is gradually increased with the number of levels. Each level is composed of a feature enhancement module (FEM), a drop-band decision module (DDM) and a high-latitude enhancement module (HEM). The FEM module serves to enhance the high-level features extracted from the input ODI, while the role of DDM is to dynamically drop the unnecessary high latitude bands and send the remained bands to the next level. The HEM is adopted to further enhance high-level features of dropped latitude bands with a lightweight architecture. In DDM, we develop a reinforcement learning scheme with a latitude adaptive reward to determine which band should be dropped. To the best of our knowledge, our method is the first work which considers the latitude characteristics for ODI-SR task. Extensive experimental results demonstrate that our LAU-Net+ achieves state-of-the-art results on ODI-SR both quantitatively and qualitatively on various ODI datasets.

Geometric Distortion Correction of Images Received From Biometric Fingerprint Devices

The quality of fingerprint images is very important for recognition and identification in biometric systems. Geometric distortions and different image scales may worsen fingerprint recognition. It is necessary to adjust distortions and scale in biometric finger capture systems. Algorithms for correcting distortions and scale are investigated, analyzed, and developed in this paper. A polynomial model of second order is used as a mathematical model of spatial distortions. To correct the brightness of image points, bicubic interpolation is used.

Progressive Keypoint Detection with Dense Siamese Network for SAR Image Registration

SAR image registration plays a key role in the applications such as change detection, image mosaic, which is one of the most challenging tasks in recent years due to speckle noise, geometric distortion and nonlinear radiation differences between SAR images. In the feature-based SAR image registration methods, the repeatability of keypoints and the effectiveness of feature descriptors could directly affect the registration accuracy. In this paper, we propose a stable feature intersection-based (FI) keypoint detector, which contains three progressive detectors, i.e., phase coherency (PC) detector, horizontal/vertical and major/minor diagonal oriented gradient detectors, and local coefficient of variation (LCoV) detector. Our proposed keypoint detector can not only effectively extract keypoints with high repeatability, but also greatly reduce the number of false keypoints, thus reducing the computational cost of feature description and matching. Then we propose the Cross Stage Partial Siamese Network (CSPSNet) to rapidly extract feature descriptors containing both deep and shallow features, which can be used to obtain more correct matching point pairs than traditional man-made shallow descriptors. In addition, we design a Hard Example Mining Loss (HEMLoss) to minimize the matching distance between the matching descriptor and adaptive selected non-matching descriptors. Experimental results on different pairs of SAR images demonstrate that our proposed method achieves better performance than other state-of-the-art methods.

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel. The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system. In this study, a unique pixel-based approach was designed to improve the crops' classification accuracy by using the edge-preserving features (EPF) and principal component analysis (PCA) in conjunction. The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI. In the second step, this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information. The resultant feature space (PCA-EPF) demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost. The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF. The classification performance evaluation was measured in terms of individual class accuracy, overall accuracy, average accuracy, and Cohen kappa factor. The proposed scheme achieved greater than 90 % results for all the performance evaluation metrics. The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range. The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.

Turbo Gradient and Spin-Echo BLADE-DWI for Extraocular Muscles in Thyroid-Associated Ophthalmopathy

Purpose: To investigate feasibility and diagnostic performance of turbo gradient and spin-echo BLADE (proprietary name for Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction [PROPELLER] in Siemens MR systems)-diffusion weighted imaging (TGSE-BLADE-DWI) for depicting extraocular muscle (EOM) involvement and activity in thyroid-associated ophthalmopathy (TAO), and to compare TGSE-BLADE-DWI with readout-segmented echo-planar imaging (RESOLVE). Materials and methods: Thirty-five patients with identified TAO underwent the two DWI scans. Two radiologists visually scored the image quality of the two DWIs with respect to the susceptibility artifacts and geometric distortions on a three-point scale. The maximum size (Sizemax) of EOMs and corresponding ADCs (cADCs) of each patient were compared between the active and inactive phases. The clinical activity score (CAS) was used as a reference to assess the diagnostic performance of EOM ADCs for grading TAO activity. ROC analysis, Pearson correlation, and Wilcoxon signed-rank test were used for statistical analyses. Results: For scores of EOMs, the image quality of TGSE-BLADE-DWI was significantly higher than that of RESOLVE. There were no statistically significant differences between the AUCs of the two DWIs, Sizemax, or cADCs between the active and inactive phases. TGSE-BLADE-DWI ADCs were significantly higher than the RESOLVE ADCs in the right superior rectus, right lateral rectus, left superior rectus, and left inferior rectus. There were no statistically significant correlations between the cADC or Sizemax, and CAS. The highest AUC was 0.697 for RESOLVE and 0.657 for TGSE-BLADE-DWI. The best performing ADC threshold was 1.85 × 10-3 mm2/s with 85.7% sensitivity, 58.8% specificity and 66.67% accuracy for RESOLVE and 1.99 × 10-3 mm2/s with 79.0% sensitivity, and 55.6% specificity and 65.27% accuracy for TGSE-BLADE-DWI. Conclusion: Compared to RESOLVE, TGSE-BLADE-DWI provided improved image quality with fewer susceptibility artifacts and geometric distortions for EOM visualization and showed an equivalent performance in detecting active TAO.

Model-Aware Pre-Training for Radial Distortion Rectification.

Camera lenses often suffer from optical aberrations, causing radial distortion in the captured images. In those images, there exists a clear and general physical distortion model. However, in existing solutions, such rich geometric prior is under-utilized, and the formulation of an effective prediction target is under-explored. To this end, we introduce Radial Distortion TRansformer (RDTR), a new framework for radial distortion rectification. Our RDTR includes a model-aware pre-training stage for distortion feature extraction and a deformation estimation stage for distortion rectification. Technically, on the one hand, we formulate the general radial distortion (i.e., barrel distortion and pincushion distortion) in camera-captured images with a shared geometric distortion model and perform a unified model-aware pre-training for its learning. With the pre-training, the network is capable of encoding the specific distortion pattern of a radially distorted image. After that, we transfer the learned representations to the learning of distortion rectification. On the other hand, we introduce a new prediction target called backward warping flow for rectifying images with any resolution while avoiding image defects. Extensive experiments are conducted on our synthetic dataset, and the results demonstrate that our method achieves state-of-the-art performance while operating in real-time. Besides, we also validate the generalization of RDTR on real-world images. Our source code and the proposed dataset are publicly available at https://github.com/wwd-ustc/RDTR.

The Design and Implementation of the Lookup Table in Distortion Correction for the Optical System

Image geometric distortion is common in a large field of view optical system. It becomes a technical difficulty for high distortion rate optical systems and high resolution image geometric distortion correction. But, a crucial step in distortion correction is figuring out how to precisely obtain the index coordinates and correction parameters, otherwise known as the lookup table. In this paper, the problems in the traditional lookup table method are analyzed, and an improved lookup table method is proposed. The method is based on the traditional lookup table method with the addition of removing the wrong data of position coordinates in the correction image feature points, calculating the ideal image feature point position coordinates, handling the index coordinate out-of-bounds, and adjusting the output order of index coordinates and correction parameters. The experimental results show that the improved design method is feasible, and improves the accuracy of the correction image, compared with the traditional lookup table, the root mean square error is reduced by 93%, and the average accuracy of the fitted position over the full field of view is reduced by 51.9%, and the maximum deviation is only 0.2917%. For the correction of the large field of view optical display devices, it offers good correction effects.

The Changing Dynamics of a Universe in Transition

It is shown in this paper that the distorted space model of matter can describe a universe transitioning between conception and an open-ended finality. We use the verbiage “distorted” to communicate the concept of “energetic-manifold-warping” and to distinguish “spatial-warping” from “classical matter-warping”, although the concept of “matter” is in fact, in the present “distorted-geometry” context, the “geometric distortion energy” of the spatial manifold itself without a classical “matter stress-energy source”. An energy conserving alternative to black-body radiation-emission structural-modeling is manifest as an energetically unstable Universe (an originally stable but subsequently collapsing state, followed by an explosive expansion, thereby exhibiting an energy-creation process of one day!). The energy transition dynamics are described for a spherical, gravitational, and electromagnetic, geometrically-based mimic of matter existing at quantitative measures of a size challenging observation, that is, at a calculated Universe (gravitational body) [Formula: see text][Formula: see text]m and [Formula: see text][Formula: see text]J. The Universe (electromagnetic body) radius is calculated [Formula: see text][Formula: see text]m. We have modeled the structure with a composite, two-component, geometric-coupling_constant and initial structural conditions representing Friedmann’s critical density, [Formula: see text]_critical energy-density (8.898(–10)[Formula: see text][Formula: see text]J/m3) for the gravitational energy-density and [Formula: see text]_electron (8.7(10)[Formula: see text][Formula: see text]J/m3)[Formula: see text][Formula: see text][Formula: see text]the [Formula: see text]_critical energy-density for the geometric-electromagnetic extremum; the geometric extrema are curvature and energy-density extrema. A collapsing initial-phase-1 state, posited as a Friedmann-defined, distorted-geometry (DG) configuration with two equal-energy species or energy-density states, transitions electromagnetically, via an intermediate “mediator or force carrier” state (a W-boson structure in beta-decay), to a final 3-component state (ala a mimic of the beta decay transition process).

Generation of Thermal Point Clouds From Uncalibrated Thermal Infrared Image Sequences and Mobile Laser Scans

Monitoring building efficiency is a hot topic for engineers and researchers. TIR (Thermal Infrared) images describe thermal attributes but require professional knowledge for analysis. Moreover, images can hardly describe the thermal attributes of the buildings in all aspects. Therefore, as-built thermal point clouds become the solution. In this case, geometric distortions of TIR images can not be neglected. To generate the thermal point clouds with TIR image sequences, we propose to use MLS (Mobile Laser Scanning) point clouds as control point sets for calibration and a workflow for the thermal point cloud generation. To begin with, intensity images are generated from the point clouds, and corresponding points are detected from the TIR image and intensity image frame. Moreover, the intrinsic parameters are estimated with corresponding points. Finally, the thermal point clouds are generated with calibrated TIR images and MLS point clouds. The result demonstrates that the automatic method can achieve comparable or even better calibration results than the manual method.

Study of stress distribution in the various interfaces present in the 3D printing microelectronic systems: Applies to boxes produced by additive manufacturing

Additive manufacturing (AM), also known as 3D printing, enables the construction of customized packaging microelectronic systems that are perfectly tailored to component dimensions and specifications. In fused deposition 3D printing technology (FDM), residual stresses are influenced by printing conditions, which reduce material performance and can lead to geometric distortions. During printing, time and temperature affect the thermomechanical properties and crystallization kinetics of the polymer used in FDM. The aim of this work is to evaluate the residual stress in a sample based on the printing conditions (ambient temperature, printing speed and layer thickness). Six points were selected to calculate and compare the residual stress in the sample, with three points in the first layer and three points in the second. Simulation and modeling are utilized to study the effects of printing conditions on the thermomechanical behavior of semicrystalline polymer for an effective assessment.

Bitstream-based Perceptual Quality Assessment of Compressed 3D Point Clouds.

With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we make one of the first attempts developing a bitstream-based no-reference (NR) model for perceptual quality assessment of point clouds without resorting to full decoding of the compressed data stream. Specifically, we first establish a relationship between texture complexity and the bitrate and texture quantization parameters based on an empirical rate-distortion model. We then construct a texture distortion assessment model upon texture complexity and quantization parameters. By combining this texture distortion model with a geometric distortion model derived from Trisoup geometry encoding parameters, we obtain an overall bitstream-based NR point cloud quality model named streamPCQ. Experimental results show that the proposed streamPCQ model demonstrates highly competitive performance when compared with existing classic full-reference (FR) and reduced-reference (RR) point cloud quality assessment methods with a fraction of computational cost.

Combining Phase Congruency and Self-Similarity Features for Multimodal Remote Sensing Image Matching

The structural features using self-similarity have become more popular for multimodal remote sensing image matching. However, mostly because of significant geometric distortions and nonlinear intensity differences between images, these methods produce a limited matching performance when directly applied to multimodal remote sensing images. To address that, we propose a novel feature descriptor named pyramid features of orientated self-similarity (POSS) for multimodal remote sensing image matching, which integrates phase congruency (PC) into the self-similarity model for better encoding structural information. Unlike these conventional self-similarity-based descriptors, the POSS is constructed by using PC instead of image intensity in a pyramid manner and thus has better discriminability and robustness to modality variations. In addition, we design a uniform multimoment of PC (UMMPC) detector for feature detection, which can improve the number, distribution, and repeatability of feature points. Experimental results demonstrate its superior applicability of the proposed method over the state-of-the-art methods, showing much better matching performance.

A theoretical study on pseudo Mott phase transition of vanadium dioxide.

The structural phase transition (SPT) and metal-insulator phase transition (MIT) always occur simultaneously upon heating from the low-temperature insulator M1 phase to the high-temperature metal R phase in vanadium dioxide, and it is still unclear which one of Mott correlation and Peierls distortion plays a decisive role in the thermally induced phase transition (PT) since 1959. Our density functional theory (DFT)-based calculations revealed that the intermediate phase in the PT, the so-called monoclinic metal phase, is a zero indirect band gap semimetal (P21/c space group) with a pseudo gap (199 meV). From the M1 phase to the monoclinic metal phase, the band gap decreases gradually to zero, and the bonding lengths between vanadium-vanadium atoms remain nearly constant. The SPT and MIT from the intermediate structure to the R phase occur simultaneously with a sudden change of bonding lengths between vanadium-vanadium atoms, in which electrons can jump down rather than jump up to the conduction band minimum (CBM) from the valence band maximum (VBM) under thermal fluctuation in order to lower the total energy of the system to push forward the occurrence of PT. The electron jumping does not require additional energy from Coulomb repulsion between electrons even though it is always present. This SPT is a typical Peierls PT or a pseudo Mott PT rather than an actual Mott PT in the each of the two stages. Our conclusions provide a new understanding of SPT and MIT in vanadium dioxide that has been debated for more than 80 years.

Numerical methods in predicting residual stresses in laser powder bed fusion developed parts – a scoping review

The study reviews the numerical methods for predicting residual stresses in parts manufactured with additive manufacturing (AM) technique, such as laser powder bed fusion (L-PBF). L-PBF is a fast-growing technology with enormous potential for creating complex geometries with improved properties as compared to conventional processes. However, parts produced with L-PBF are susceptible to higher magnitudes of residual stresses, particularly tensile stresses as compared to compressive stresses, leading to geometrical distortions. While newly developed materials offer excellent properties and benefits to the industrial sector, the residual stresses that develop when parts are produced using the L-PBF process remain unexplored. The study evaluates three numerical simulation methods, such as thermomechanical modelling (TMM), inherent strain method (ISM), and multi-scale modelling (MSM) used to predict residual stresses in L-PBF parts. Each method has its advantages and limitations, and the best method depends on the specific application and available resources. The review highlights the potential benefits of numerical simulation methods for predicting residual stresses in L-PBF parts and compared to experimental results. Thus, the study recommends that future research should focus on improving the accuracy of numerical simulation methods for predicting residual stresses in L-PBF parts through refinement processes and exploring the impact of residual stresses on the L-PBF parts through detailed characterization.