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Automatic insertion machines play an important role in the assembly of through-hole electronic components on printed circuit boards (PCBs). However, for irregular multi-pin components, fixed-position registration strategies may fail to recover feasible insertion poses under grasping errors, manufacturing tolerances, and transportation-induced pin perturbations. To address this issue, this paper proposes a maximum-clearance optimization model with chaotic initialization for PCB component pin registration. First, the Graham–Scan algorithm is employed to generate convex polygons to simulate the geometric layout of component pins. Subsequently, random perturbations following two-dimensional normal distributions are introduced to simulate pin deformation caused by manufacturing and transportation processes. Then, a maximum-clearance objective is formulated to determine a feasible insertion pose under bounded translational and rotational adjustments. Finally, the resulting registration model is first evaluated through a solver-level comparison among three general-purpose numerical optimizers—namely, SLSQP, genetic algorithm, and simulated annealing—all applied to the same maximum-clearance formulation. In addition, representative registration baselines are introduced in a supplementary cross-paradigm comparison to clarify the difference between alignment-oriented registration accuracy and clearance-oriented insertion feasibility. Experimental results under an industrially motivated simulation setting show that, when solved by SLSQP, the proposed model achieved a clearance-based surrogate success rate of 98.44% over a complete simulated dataset of 1,400,000 samples, while maintaining an average computation time of 0.0198 s per sample. These results suggest that the proposed method provides computational evidence for the potential usefulness of a clearance-oriented geometric registration framework under simulated PCB insertion conditions motivated by field investigation data from Dalian Rijia Electronics Co., Ltd., rather than direct validation on a fully instrumented production line.

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. •We propose a deep learning approach to rapidly predict 3D deformable registrations.•We predict the initial momentum of LDDMM patch-wise, assuring diffeomorphisms.•We propose a correction network to further improve prediction accuracy.•We achieve an order of magnitude speed-up compared to a standard optimization method.•We extensively validate our approach on over 2400 image registrations pairs.

Time continuous analyses, such as statistical parametric mapping (SPM), have been increasingly used in biomechanics research to determine differences between populations, interventions and methodologies. Currently, it is not known how sensitive time-continuous analyses are to timing variability that occur in gait data. We evaluated this sensitivity by examining the frequency of significant SPM outcomes between two walking speeds when lower limb kinematics and kinetics were segmented and aligned based on 40 repeatable gait events. These events, defined in the supplementary material, include a commonly used event like foot contact and other events that have been previously demonstrated to be repeatable. Repeatable gait events were determined from joint and segment kinematics, joint kinetics as well as ground reaction forces. We examined the frequency of statistical outcomes for a single subject with different numbers of strides analyzed and for a cohort of 10 subjects. Our findings demonstrate that gait interventions, such as changes in walking speed, can induce temporal shifts that affect time-continuous outcomes for both cohort- and subject-level analyses. As both timing and magnitude are important in gait data, researchers are encouraged to perform additional analyses to understand how both of these variables affect time-continuous analysis outcomes. Finally, we demonstrate that multiple SPM tests can be performed to determine if statistical outcomes are due to temporal shifting or differences in magnitude. It is important to understand how both timing and magnitude of biomechanical data influences time continuous analyses as these analyses inform injury prevention, device development and basic understanding of biomechanics.

Many microscopic investigations of materials may benefit from the recording of multiple successive images. This can include techniques common to several types of microscopy such as frame averaging to improve signal-to-noise ratios (SNR) or time series to study dynamic processes or more specific applications. In the scanning transmission electron microscope, this might include focal series for optical sectioning or aberration measurement, beam damage studies or camera-length series to study the effects of strain; whilst in the scanning tunnelling microscope, this might include bias-voltage series to probe local electronic structure. Whatever the application, such investigations must begin with the careful alignment of these data stacks, an operation that is not always trivial. In addition, the presence of low-frequency scanning distortions can introduce intra-image shifts to the data. Here, we describe an improved automated method of performing non-rigid registration customised for the challenges unique to scanned microscope data specifically addressing the issues of low-SNR data, images containing a large proportion of crystalline material and/or local features of interest such as dislocations or edges. Careful attention has been paid to artefact testing of the non-rigid registration method used, and the importance of this registration for the quantitative interpretation of feature intensities and positions is evaluated.

For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal image registration while Discrete Wavelet Transform (DWT) along with Principal Component Averaging (PCAv) is utilized for image fusion. The proposed MRR method provides more accurate results as compared with Single Rigid Registration (SRR), while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time. The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset. The fusion results of the proposed method are compared with the existing fusion techniques. The quality assessment metrics such as Mutual Information (MI), Normalize Cross-correlation (NCC) and Feature Mutual Information (FMI) are computed for statistical comparison of the proposed method. The proposed methodology provides more accurate results, better image quality and valuable information for medical diagnoses.

Objective Cone-beam computed tomography (CBCT) has been widely adopted for patient setup evaluation and error correction in head and neck radiotherapy. Although different anatomical registration regions are routinely used in clinical practice, the characteristics of six-degree-of-freedom (6DoF) setup errors associated with different registration regions have not been systematically compared within the same CBCT acquisition. This study aimed to compare the distributional characteristics of 6DoF setup errors across different anatomical registration regions using a within-scan comparison framework and to describe their structural features from a multivariate perspective. Methods CBCT data acquired during the radiotherapy course of patients with head and neck cancer were retrospectively analyzed. For each CBCT scan, multiple anatomical registration regions were independently applied based on the same image dataset, and the corresponding 6DoF setup errors were recorded. Paired statistical methods were used to compare the distributions of setup errors among different registration regions. Principal component analysis (PCA) was further performed to characterize the overall structural patterns of the 6DoF error components. Results Within the same CBCT acquisition, the distributions of 6DoF setup errors differed among anatomical registration regions. The observed differences were mainly reflected in certain rotational components, whereas the differences among translational components were relatively less pronounced. PCA results demonstrated that the structure of 6DoF setup errors could be characterized by different variation patterns dominated by either rotational or translational components. Although partial overlap was observed among registration regions in the principal component space, differences in their distributional characteristics were still evident. Conclusions Within the same CBCT acquisition, the selection of anatomical registration regions may influence both the distribution and structural characteristics of 6DoF setup errors in head and neck radiotherapy. These findings suggest that the potential impact of registration region selection should be considered during clinical setup evaluation.

What are the main findings? * We construct a large-scale, high-resolution optical–SAR registration dataset, pairing 3-m SAR imagery from the HongTu-1 satellite with Google Earth optical imagery at zoom level 17, covering the major geographical regions of China, and we release the standardized pipeline—including full-scene pairing, DEM-based terrain correction, geometric refinement, standardized 512 × 512 slicing and multi-stage quality filtering—that was used to build it. * Our proposed Log-domain reformulation of the Total Variation (Log-TV) filter substantially improves SAR image preprocessing by converting the multiplicative speckle noise model into an additive one, thereby enabling effective suppression of speckle while preserving edge structures and providing a much cleaner foundation for subsequent keypoint detection. * Combining a machine learning-based edge filter (Structured Random Forest, SRF) with the hand-crafted phase congruency filter yields a strong synergistic effect for cross-modal optical–SAR edge filtering, producing more stable and consistent shared structural responses than either component alone. We construct a large-scale, high-resolution optical–SAR registration dataset, pairing 3-m SAR imagery from the HongTu-1 satellite with Google Earth optical imagery at zoom level 17, covering the major geographical regions of China, and we release the standardized pipeline—including full-scene pairing, DEM-based terrain correction, geometric refinement, standardized 512 × 512 slicing and multi-stage quality filtering—that was used to build it. Our proposed Log-domain reformulation of the Total Variation (Log-TV) filter substantially improves SAR image preprocessing by converting the multiplicative speckle noise model into an additive one, thereby enabling effective suppression of speckle while preserving edge structures and providing a much cleaner foundation for subsequent keypoint detection. Combining a machine learning-based edge filter (Structured Random Forest, SRF) with the hand-crafted phase congruency filter yields a strong synergistic effect for cross-modal optical–SAR edge filtering, producing more stable and consistent shared structural responses than either component alone. What are the implications of the main findings? * Large-scale, high-resolution optical–SAR datasets are both essential and scarce for registration and other downstream tasks. Only on larger and more complex benchmarks do the robustness and the true relative performance of competing algorithms become evident, making such datasets a necessary foundation for future research in this area. * Different imaging modalities require different filtering strategies: for heavily speckled data such as SAR imagery, regularisation in the logarithmic domain is more appropriate than directly applying denoisers designed for additive noise, highlighting the importance of modality-aware preprocessing in cross-modal registration. * Hybrid pipelines that integrate learning-based components with hand-crafted filters are a promising direction: beyond edge filtering, similar combinations of deep features and classical hand-crafted operators may also benefit cross-modal feature description and matching stages. Large-scale, high-resolution optical–SAR datasets are both essential and scarce for registration and other downstream tasks. Only on larger and more complex benchmarks do the robustness and the true relative performance of competing algorithms become evident, making such datasets a necessary foundation for future research in this area. Different imaging modalities require different filtering strategies: for heavily speckled data such as SAR imagery, regularisation in the logarithmic domain is more appropriate than directly applying denoisers designed for additive noise, highlighting the importance of modality-aware preprocessing in cross-modal registration. Hybrid pipelines that integrate learning-based components with hand-crafted filters are a promising direction: beyond edge filtering, similar combinations of deep features and classical hand-crafted operators may also benefit cross-modal feature description and matching stages. Optical and Synthetic Aperture Radar (SAR) image registration is a fundamental task in remote sensing information fusion, yet it remains challenging due to significant differences in imaging mechanisms, radiation characteristics, and noise properties between the two modalities. Existing public datasets suffer from limited resolution, small scale, and insufficient scene diversity, and these limitations have hindered algorithm development. This paper constructs a large-scale, high-resolution optical–SAR registration dataset based on the HongTu-1 satellite 3-m SAR imagery and Google Earth optical imagery at zoom level 17, covering diverse scenes across China with a standardized pipeline including terrain correction, geometric alignment, standardized slicing, and quality filtering. Building upon this dataset, a hand-crafted keypoint-based cross-modal registration method is proposed, incorporating multi-level edge filtering and hybrid feature detection. Unlike conventional hand-crafted methods such as RIFT, SRIF, and LNIFT, which mainly refine keypoint detection, description, or matching within a SIFT-style pipeline, the core novelty of this work lies in SAR-specific preprocessing and multi-level hybrid filtering. These components are designed to suppress speckle while extracting more stable and discriminative shared edge responses for cross-modal registration. An improved Log-domain Total Variation (Log-TV) denoising model is introduced for SAR preprocessing. A hybrid edge filtering framework combining phase congruency analysis and Structured Random Forest (SRF) edge detection is constructed within a Gaussian scale space. A dual-branch feature detection scheme integrating blob and corner features is designed with a robust orientation assignment strategy. Feature description uses the Gradient Location–Orientation Histogram (GLOH) descriptor with Principal Component Analysis (PCA) reduction, while geometric estimation employs the Fast Sample Consensus (FSC) algorithm. Experiments on the self-constructed HT dataset and on the public OSdataset and SAR2Opt benchmarks show that the proposed method consistently achieves low RMSE and high success rates. It also maintains competitive efficiency among hand-crafted methods while retaining strong robustness to scale and rotation variations.

Despite evidence from previous case studies showing that agronomic traits partially determine the resulting yield of different rice ( L.) varieties, it remains unclear whether this is true at the ecotype level. Here, an extensive dataset of the traits of 7686 rice varieties, released in China from 1978 to 2017, was used to study the relationship between yield and other agronomic traits. We assessed the association between yield and other agronomic traits for four different rice ecotypes, i.e., indica inbred, indica hybrid, japonica inbred, and japonica hybrid. We found that associations between agronomic traits and yield were ecotype-dependent. For both the indica inbred and indica hybrid ecotypes, we found that greater values of certain traits, including the filled grain number per panicle, 1000-grain-weight, plant height, panicle length, grains per panicle, seed setting rate, long growth period, low panicle number per unit area, and low seed length/width ratio, have accounted for high grain yield. In the japonica inbred and japonica hybrid ecotypes, we found that only high panicle number per unit area and long growth period led to high grain yield. Indirectly, growth period consistently had a positive effect on yield in all ecotypes, and plant height had a positive effect on yield for the indicas and japonica inbred only. Plant height had a negative effect for the japonica hybrid. Altogether, our findings potentially have valuable implications for improving the breeds of rice ecotypes.

The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention. Recently, Davatzikos et al. (2009) supported the hypothesis that pathologic atrophy in AD is an accelerated aging process, implying accelerated brain atrophy. In order to recognize faster brain atrophy, a model of healthy brain aging is needed first. Here, we introduce a framework for automatically and efficiently estimating the age of healthy subjects from their T1-weighted MRI scans using a kernel method for regression. This method was tested on over 650 healthy subjects, aged 19–86 years, and collected from four different scanners. Furthermore, the influence of various parameters on estimation accuracy was analyzed. Our age estimation framework included automatic preprocessing of the T1-weighted images, dimension reduction via principal component analysis, training of a relevance vector machine (RVM; Tipping, 2000) for regression, and finally estimating the age of the subjects from the test samples. The framework proved to be a reliable, scanner-independent, and efficient method for age estimation in healthy subjects, yielding a correlation of r=0.92 between the estimated and the real age in the test samples and a mean absolute error of 5 years. The results indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy. Applying the framework to people with mild AD resulted in a mean brain age gap estimate (BrainAGE) score of +10 years.

Three-dimensional (3D) ear morphology is critical for the design of in-the-ear hearing aids, earphones, transcutaneous auricular vagus nerve stimulation (taVNS) electrodes, and auricular reconstruction, yet most existing ear shape models still rely on manually placed landmarks. Here, a fully annotation-free pipeline is presented for constructing a 3D ear atlas and statistical shape model (SSM) of the auricular bowl from 50 surface meshes. Individual ears are iteratively registered to a current atlas using rigid the iterative closest point (ICP) algorithm followed by a bidirectional thin-plate spline (BiTPS) deformation, and dense surface correspondences are established by nearest-neighbour mapping. Registration quality is quantified using mean and maximum nearest-neighbour distance, symmetric Chamfer-L2 distance and coverage. Furthermore, SSM-derived bowl height and width are validated against manual 3D mesh measurements in Geomagic Design X. Across five atlas iterations, the BiTPS pipeline substantially reduces registration errors and increases coverage, and principal component analysis (PCA) derived dimensions show excellent agreement with manual measurements (Pearson r≥0.98, ICC ≥0.98). The proposed framework yields a stable, anatomically plausible ear atlas and an interpretable low-dimensional SSM without manual landmarks, providing a computational basis for the geometric optimization of ear-related medical and wearable devices.