Search

We prove nonemptyness of domains of proper discontinuity of Anosov groups of affine Lorentzian transformations of Rn.

Due to the wide use of digital images in the virtual world and the growing attacks on them, passive image authentication schemes became an urgent and necessary need. The forgery attacks threaten the ownership of the digital image. Copy- move forgery detection is the main type of image forgery detection schemes. In this forgery, at least one region is copied and pasted at same image. In the proposed scheme, we proposed anew serial strategy through combining feature-based detector and block-based detector by using Region growing merging Segmentation method (RGMS).Multi scale Hessian-affine detector as primary detector, Polar cosine transform(PCT) as final detector and Simple linear iterative clustering segmentation(SLIC) with Factor Graph to construct RGMS. We designed serial random fitting model to include the true results and exclude the undesired results. Serial random supports single and multi-cloning regions. Our strategy provides facility to reveal and extract the iterative regions under different constraints. In addition, the proposed scheme is able to detect the slight and invisible cloning regions in the smooth digital image. This scheme is invariant to simple and complex attacks. Furthermore, it invariant to affine transformation likes skewness, distortion and perspective changes. Different datasets are used like GRIP, MICC-F600, MICC-F8.The experimental results confirmed that the proposed scheme has high percentage of True positive rates (99%), and low percentage of false positive rates (1.5%). The proposed scheme can be used as passive image forensic scheme in the cybercrimes and court rooms. [ABSTRACT FROM AUTHOR]

Background: Visium is a widely-used spatially-resolved transcriptomics assay available from 10x Genomics. Standard Visium capture areas (6.5mm by 6.5mm) limit the survey of larger tissue structures, but combining overlapping images and associated gene expression data allow for more complex study designs. Current software can handle nested or partial image overlaps, but is designed for merging up to two capture areas, and cannot account for some technical scenarios related to capture area alignment. Results: We generated Visium data from a postmortem human tissue sample such that two capture areas were partially overlapping and a third one was adjacent. We developed the R/Bioconductor package visiumStitched, which facilitates stitching the images together with Fiji (ImageJ), and constructing SpatialExperiment R objects with the stitched images and gene expression data. visiumStitched constructs an artificial hexagonal array grid which allows seamless downstream analyses such as spatially-aware clustering without discarding data from overlapping spots. Data stitched with visiumStitched can then be interactively visualized with spatialLIBD. Conclusions: visiumStitched provides a simple, but flexible framework to handle various multi-capture area study design scenarios. Specifically, it resolves a data processing step without disrupting analysis workflows and without discarding data from overlapping spots. visiumStitched relies on affine transformations by Fiji, which have limitations and are less accurate when aligning against an atlas or other situations. visiumStitched provides an easy-to-use solution which expands possibilities for designing multi-capture area study designs. [ABSTRACT FROM AUTHOR]

Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentation. We use a unique augmentation process for images and masks involving keypoint extraction, Delaunay triangulation, local affine transformations, and mask refinement. This approach accurately captures the natural variability of the corneal endothelium, enriching the dataset with realistic and diverse images. The proposed method achieved an increase in the mean intersection over union (mIoU) and Dice coefficient (DC) metrics of 17.2% and 4.8% respectively, for the segmentation task in corneal endothelial images on multiple CNN architectures. Our data augmentation strategy successfully models the natural variability in corneal endothelial images, thereby enhancing the performance and generalization capabilities of semi-supervised CNNs in medical image cell segmentation tasks. [ABSTRACT FROM AUTHOR]

The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug–drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI prediction and improved prediction performance. However, there are still some problems with the present models, such as low accuracy due to information loss during molecular representation or incomplete drug feature mining during the training process. Aiming at these problems, this study proposes an accurate and simple framework named PTB-DDI for drug–drug interaction prediction. The PTB-DDI framework consists of four key modules: (1) ChemBerta tokenizer for molecular representation, (2) Bidirectional Long Short-Term Memory (BiLSTM) to capture the bidirectional context-aware features of drugs, (3) Multilayer Perceptron (MLP) for mining the nonlinear relationship of drug features, and (4) interaction predictor to perform an affine transformation and final prediction. In addition, we investigate the effect of dual-mode on parameter-sharing and parameter-independent within the PTB-DDI framework. Furthermore, we conducted comprehensive experiments on the two real-world datasets (i.e., BIOSNAP and DrugBank) to evaluate PTB-DDI framework performance. The results show that our proposed framework has significant improvements over the baselines based on both datasets. Based on the BIOSNAP dataset, the AUC-ROC, PR-AUC, and F1 scores are 0.997, 0.995, and 0.984, respectively. These metrics are 0.896, 0.873, and 0.826 based on the DrugBank dataset. Then, we conduct the case studies on the three newly approved drugs by the Food and Drug Administration (FDA) in 2024 using the PTB-DDI framework in dual modes. The obtained results indicate that our proposed framework has advantages for predicting drug–drug interactions and that the dual modes of the framework complement each other. Furthermore, a free website is developed to enhance accessibility and user experience. [ABSTRACT FROM AUTHOR]

In the regenerative aluminum smelting process, the furnace temperature is critical for the quality and energy consumption of the product. However, the process requires protective sensors, making real-time furnace temperature measurement costly, while the strong nonlinearity and distribution drift of the process data affect furnace temperature prediction. To handle these issues, a multi-step prediction model for furnace temperature that incorporates reversible instance normalization (RevIN), convolutional neural network (CNN), and Transformer is proposed. First, the self-attention mechanism of the Transformer is combined with CNN to extract global and local information in the furnace temperature data, thus addressing the strong nonlinear characteristics of the furnace temperature. Second, RevIN with learnable affine transformation is utilized to address the distribution drift in the furnace temperature data. Third, the temporal correlation of the prediction model is enhanced by a time-coding method. The experimental results show that the proposed model demonstrates higher prediction accuracy for furnace temperature at different prediction steps in the regenerative aluminum smelting process compared to other models. [ABSTRACT FROM AUTHOR]

Introduction: Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance. Methods: We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately. Results: The combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set. Discussions: Our work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth—often overlooked factors– can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies. [ABSTRACT FROM AUTHOR]

Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring. [ABSTRACT FROM AUTHOR]

The article discusses the use of MRI scans from the ADNI dataset to develop biomarkers for early detection of Alzheimer's disease. The study focuses on data pre-processing, data augmentation, and the experimental setup of convolutional neural networks. Results show the effectiveness of specific augmentation strategies and the importance of model depth in achieving optimal performance. The study also evaluates the impact of dropout and generalization across image resolution, providing insights for future research in this area. [Extracted from the article]

Crop canopy temperature can often be acquired using the thermal imager. Non-contact and non-destructive automated detection can be expected to achieve for crop water stress status. Automatic image alignment can be used to treat the fuzzy edge distribution, strong noise, as well as shape and texture information lacking in thermal infrared images, according to the information complementarity between visible light and thermal infrared images. The automated extraction can be realized on the crop canopy temperature. This study aims to solve the problems of differences in the radiation, shape, and texture between visible light images and thermal infrared images, leading to the low align images of different modalities. Multimodal image registration was also proposed to integrate the improved brain storm optimization (BSO) and Powell algorithm. Firstly, the original visible light image was downsampled and cropped, according to the normalized cross-correlation value. The area with the most similarity region was obtained in the thermal infrared image under the same resolution; Then, the target area was extracted from the cropped image. The target area image and the original thermal infrared image were decomposed by wavelet transform, where the multilayered low-frequency information was retained; Thirdly, the primitive affine transformation matrix was obtained by the image moments in the low-resolution layer; At the same time, the global search was used to optimize the affine transform matrix in the low-resolution layer using the improved BSO; Fourthly, the optimization was used as the initial point of the Powell algorithm. The optimization was performed in the high-resolution layer; Lastly, the optimization in the previous step was input into the Powell algorithm again. The original image layer was optimized again to obtain the final affine transformation matrix. The original BSO optimization was improved for the optimal affine transformation matrix in the image alignment task. The specific improvements included the following five aspects: The BSO population distribution was initialized using a chaotic mapping function; The mutation range of new individual was modified; The number of K-means clusters was dynamically adjusted in the BSO by the elbow; The chaotic local search was incorporated into the strategy of individual variation; and the probability parameters were dynamically adjusted, according to the different BSO in the early and late stages. Mutual information (MI), normalized mutual information (NMI), root mean square error (RMSE) and mean structure similarity index measure (MSSIM) were taken as the evaluation indexes. A comparison was made with Powell optimization, genetic algorithm (GA) and BSO_Powell algorithm. Specifically, MI indexes were improved by 0.054 2, 0.076 9, 0.040 5, respectively; NMI indexes were improved by 0.015 9, 0.023 1, 0.052 7, respectively; RMSE indexes were reduced by 15.02, 13.03, 27.08, respectively; and MSSIM indexes were improved by 0.0523, 0.0488, 0.1224, respectively, in greenhouse data; In field data, MI indexes were improved by 0.064 2, 0.066 7, 0.035 5, respectively; NMI indexes were improved by 0.007 7, 0.0125, 0.0124, respectively; RMSE indexes were reduced by 14.06, 10.57, 15.40, respectively; and MSSIM indexes were improved by 0.047 1, 0.038 1, 0.042 9, respectively. The strong robustness can accurately achieved in the multimodal image registration tasks for potatoes under complex environments. [ABSTRACT FROM AUTHOR]

The study of the extensive data sets generated by spectrometers, which are of the type commonly referred to as big data, plays a crucial role in extracting valuable information on mineral composition in various fields, such as chemistry, geology, archaeology, pharmacy and anthropology. The analysis of these spectroscopic data falls into the category of big data, which requires the application of advanced statistical methods such as principal component analysis and cluster analysis. However, the large amount of data (big data) recorded by spectrometers makes it very difficult to obtain reliable results from raw data. The usual method is to carry out different mathematical transformations of the raw data. Here, we propose to use the affine transformation for highlight the underlying features for each sample. Finally, an application to spectroscopic data collected from minerals or rocks recorded by NASA's Jet Propulsion Laboratory is performed. An illustrative example has been included by analysing three mineral samples, which have different diageneses and parageneses and belong to different mineralogical groups. [ABSTRACT FROM AUTHOR]

The limitation of medical image data in open source is a big challenge for medical image processing. Medical data is closed because of confidential and ethical issues, also manual labeling of medical data is an expensive process. We propose a new augmentation method named MetaLung (Meticulous affine-transformation-based lung cancer augmentation method) for lung CT image augmentation. The key feature of the proposed method is the ability to expand the training dataset while preserving clinical and instrumental features. MetaLung shows a stable increase in image segmentation quality for three CNN-based models with different computational complexity (U-Net, DeepLabV3, and MaskRCNN). Also, the method allows in reduce the number of False Positive predictions. [ABSTRACT FROM AUTHOR]

Currently, digital communication generates a considerable amount of data from digital images. Preserving the confidentiality of these images during transmission through network channels is of crucial importance. To ensure the security of this data, this article proposes an image encryption approach based on enhancing the Hill cipher by constructing pseudo-random matrices operating in the ring Z/212Z injected into a controlled affine transformation. This approach relies on the use of chaotic maps for generating matrices used in the encryption process. The use of the ring Z/212Z aims to expand the key space of our cryptosystem, thus providing increased protection against bruteforce attacks. Moreover, to enhance security against differential attacks, a matrix of size (4×4), not necessarily invertible, is also integrated into a diffusion phase. The effectiveness of our technique is evaluated through specific tests, such as key space analysis, histogram analysis, entropy calculation, negative pixel count rate (NPCR) and unified average changing intensity (UACI) values, correlation analysis, as well as avalanche effect assessment. [ABSTRACT FROM AUTHOR]

Alchemical free energy calculations via molecular dynamics have been applied to obtain thermodynamic properties related to solid–liquid equilibrium conditions, such as melting points. In recent years, the pseudo-supercritical path (PSCP) method has proved to be an important approach to melting point prediction due to its flexibility and applicability. In the present work, we propose improvements to the PSCP alchemical cycle to make it more compact and efficient through a concerted evaluation of different potential energies. The multistate Bennett acceptance ratio (MBAR) estimator was applied at all stages of the new cycle to provide greater accuracy and uniformity, which is essential concerning uncertainty calculations. In particular, for the multistate expansion stage from solid to liquid, we employed the MBAR estimator with a reduced energy function that allows affine transformations of coordinates. Free energy and mean derivative profiles were calculated at different cycle stages for argon, triazole, propenal, and the ionic liquid 1-ethyl-3-methyl-imidazolium hexafluorophosphate. Comparisons showed a better performance of the proposed method than the original PSCP cycle for systems with higher complexity, especially the ionic liquid. A detailed study of the expansion stage revealed that remapping the centers of mass of the molecules or ions is preferable to remapping the coordinates of each atom, yielding better overlap between adjacent states and improving the accuracy of the methodology. [ABSTRACT FROM AUTHOR]

Purpose: To develop a new method for free‐breathing 3D extracellular volume (ECV) mapping of the whole heart at 3 T. Methods: A free‐breathing 3D cardiac ECV mapping method was developed at 3 T. T1 mapping was performed before and after contrast agent injection using a free‐breathing electrocardiogram‐gated inversion recovery sequence with spoiled gradient echo readout. A linear tangent space alignment model‐based method was used to reconstruct high‐frame‐rate dynamic images from (k,t)‐space data sparsely sampled along a random stack‐of‐stars trajectory. Joint T1 and transmit B1 estimation were performed voxel‐by‐voxel for pre‐ and post‐contrast T1 mapping. To account for the time‐varying T1 after contrast agent injection, a linearly time‐varying T1 model was introduced for post‐contrast T1 mapping. ECV maps were generated by aligning pre‐ and post‐contrast T1 maps through affine transformation. Results: The feasibility of the proposed method was demonstrated using in vivo studies with six healthy volunteers at 3 T. We obtained 3D ECV maps at a spatial resolution of 1.9 × 1.9 × 4.5 mm3 and a FOV of 308 × 308 × 144 mm3, with a scan time of 10.1 ± 1.4 and 10.6 ± 1.6 min before and after contrast agent injection, respectively. The ECV maps and the pre‐ and post‐contrast T1 maps obtained by the proposed method were in good agreement with the 2D MOLLI method both qualitatively and quantitatively. Conclusion: The proposed method allows for free‐breathing 3D ECV mapping of the whole heart within a practically feasible imaging time. The estimated ECV values from the proposed method were comparable to those from the existing method. [ABSTRACT FROM AUTHOR]

This article investigates the problem of estimating the powers of scale parameters under order restriction of two shifted exponential populations when the location parameters are assumed to be unknown and equal. Several classical estimators have been proposed, such as the maximum likelihood estimators, plug-in type restricted maximum likelihood estimators, and the uniform minimum variance unbiased estimators. Sufficient conditions for constructing improved estimators under the scale and affine group of transformations have been derived. Consequently, several improved estimators for the powers of the scale parameters under order restriction have been proposed. Furthermore, using the quadratic loss function, a simulation study has been carried out to compare all the proposed estimators in terms of risk values, and recommendations are made there. [ABSTRACT FROM AUTHOR]

AbstractGaussian distributions are widely used in Bayesian variational inference to approximate intractable posterior densities, but the ability to accommodate skewness can improve approximation accuracy significantly, when data or prior information is scarce. We study the properties of a subclass of closed skew normals constructed using affine transformation of independent standardized univariate skew normals as the variational density, and illustrate how it provides increased flexibility and accuracy in approximating the joint posterior in various applications, by overcoming limitations in existing skew normal variational approximations. The evidence lower bound is optimized using stochastic gradient ascent, where analytic natural gradient updates are derived. We also demonstrate how problems in maximum likelihood estimation of skew normal parameters occur similarly in stochastic variational inference, and can be resolved using the centered parameterization. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Many deployable structures in nature, as well as human-made mechanisms, preserve symmetry as their configurations evolve. Examples in nature include blooming flowers, dilation of the iris within the human eye, viral capsid maturation and molecular and bacterial motors. Engineered examples include opening umbrellas, elongating scissor jacks, variable apertures in cameras, expanding Hoberman spheres and some kinds of morphing origami structures. In these cases, the structures either preserve a discrete symmetry group or are described as an evolution from one discrete symmetry group to another of the same type as the structure deploys. Likewise, elastic metamaterials built from lattice structures can also preserve symmetry type while passively deforming and changing lattice parameters. A mathematical formulation of such transitions/deployments is articulated here. It is shown that if X is Euclidean space, G is a continuous group of motions of Euclidean space and Γ is the type of the discrete subgroup of G describing the symmetries of the deploying structure, then the symmetry of the evolving structure can be described by time-dependent subgroups of G of the form Γαt:=αtΓαt−1 , where αt is a time-dependent affine transformation. Then, instead of considering the whole structure in X , a 'sector' of it that lives in the orbit space Γαt\X can be considered at each instant in time, and instead of considering all motions in G , only representatives from right cosets in the space Γαt\G need to be considered. This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 1)'. [ABSTRACT FROM AUTHOR]

Invariant relations are used to analyze while loops; while their primary application is to derive the function of a loop, they can also be used to derive loop invariants, weakest preconditions, strongest postconditions, sufficient conditions of correctness, necessary conditions of correctness, and termination conditions of loops. In this paper we present two generic invariant relations that capture the semantics of loops whose loop body applies affine transformations on numeric variables. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method. [ABSTRACT FROM AUTHOR]

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method's capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets. [ABSTRACT FROM AUTHOR]

Medical image registration is vital for precise healthcare diagnosis, treatment planning, and disease progression tracking, but traditional methods fail to capture complex spatial transformations and anatomical variations. A Kernel Principal Component Analysis (KPCA) driven Teaching Learning based optimization (TLBO) approach is proposed to overcome these limitations. The proposed approach is categorized into three phases, i.e., pre-processed phase, contour extraction phase with feature extraction using KPCA, and evaluating robust affine transformation parameters leveraging TLBO for accurate alignment. In the pre-processing phase, gaussian filter is applied to remove noise from source and target images, followed by normalization process. Afterwards, contour extraction is carried out to create a feature image that represents the boundaries of an image. Centroid localization is then utilized to compute translation parameters, which determine the spatial alignment between the images. By utilizing KPCA, this method captures non-linear relationships in the data to enhance the representation of image features. TLBO is employed to optimize the rigid transformation parameters to improve the accurate alignment of source and target images. Extensive experiments are carried out on monomodal and multimodal medical images such as CT and MRI taken from the Harvard Brain ATLAS, Kaggle as well as in-house clinical dataset to demonstrate the effectiveness of the proposed approach. The proposed approach significantly outperforms state-of-the-art methods, with improvements of 44.37% in Root Mean Square Error (RMSE), 19.93% in Structural Similarity Index Measure (SSIM), 20.01% in Peak Signal-to-Noise Ratio (PSNR), and 16.21% in Cross Correlation (CC) quality assessment metrics. [ABSTRACT FROM AUTHOR]

In this paper, we take an axiomatic approach to the design of ecological footprint indices. Our focus is put on the heterogeneity of land with respect to types and regions, at the core of an inherent aggregation problem. We propose an axiomatic characterization of the ecological footprint index with two fundamentally new axioms, symmetry and independence, which can resolve the problem of land heterogeneity. It is shown that a unique index, up to an affine transformation, exists meeting the axiom system. This index simplifies the aggregation procedure considerably and avoids the need for a synthetic unit of measurement, like global hectares, as well as complex transformations of variables by means of weighting schemes. Our findings reveal differences with the Global Footprint Network (GFN) index, in particular with regard to the treatment of land heterogeneity. Finally, the axiomatic methodology employed may open up perspectives for the development of ecological measures in general, and especially of measures for sustainability and tipping points. [ABSTRACT FROM AUTHOR]

An innovative approach to enhance image alignment through affine transformation, allowing images to be rotated from 0 to 135 degrees. This transformation is a crucial step in improving the diagnostic process, as image misalignment can lead to inaccurate results. The accurate alignment sets the stage for a robust U-Net model, which excels in image segmentation. Precise segmentation is vital for isolating affected brain regions, aiding in the identification of PD-related anomalies. Finally, we introduce the DenseNet architecture model for disease classification, distinguishing between PD and non-PD cases. The combination of these DL models outperforms existing diagnostic approaches in terms of acceptance precision (99.45%), accuracy (99.95%), sensitivity (99.67%), and F1-score (99.84%). In addition, we have developed user-friendly graphical interface software that enables efficient and reasonably accurate class detection via Magnetic Resonance Imaging (MRI). This software exhibits superior efficiency contrasted to current cutting-edges technique, presenting an encouraging opportunity for early disease detection. In summary, our research tackles the problem of low accuracy in existing PD diagnostic models and addresses the critical need for more precise and timely PD diagnoses. By enhancing image alignment and employing advanced DL models, we have achieved substantial improvements in diagnostic accuracy and provided a valuable tool for early PD detection. [ABSTRACT FROM AUTHOR]

The first step in spatial normalization of magnetic resonance (MR) images commonly is an affine transformation, which may be vulnerable to image imperfections (such as inhomogeneities or “unusual” heads). Additionally, common software solutions use internal starting estimates to allow for a more efficient computation, which may pose a problem in datasets not conforming to these assumptions (such as those from children). In this technical note, three main questions were addressed: one, does the affine spatial normalization step implemented in SPM12 benefit from an initial inhomogeneity correction. Two, does using a complexity-reduced image version improve robustness when matching “unusual” images. And three, can a blind “brute-force” application of a wide range of parameter combinations improve the affine fit for unusual datasets in particular. A large database of 2081 image datasets was used, covering the full age range from birth to old age. All analyses were performed in Matlab. Results demonstrate that an initial removal of image inhomogeneities improved the affine fit particularly when more inhomogeneity was present. Further, using a complexity-reduced input image also improved the affine fit and was beneficial in younger children in particular. Finally, blindly exploring a very wide parameter space resulted in a better fit for the vast majority of subjects, but again particularly so in infants and young children. In summary, the suggested modifications were shown to improve the affine transformation in the large majority of datasets in general, and in children in particular. The changes can easily be implemented into SPM12. [ABSTRACT FROM AUTHOR]

Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain. [ABSTRACT FROM AUTHOR]

Local features of an image provide a robust way of image matching if they are invariant to large variations in scale, viewpoint, illumination, rotation, and affine transformations. In this paper, we propose a novel feature descriptor based on circular and elliptical local sampling of image pixels to attain fast and robust results under varying imaging conditions. The proposed descriptor is tested on a standard benchmark dataset comprising of images with varying imaging conditions and compression quality. Results show that the proposed method generates sufficient or more number of stable and correct matches between an image pair (original image and distorted image) as compared to SIFT with a speedup of 1.6 on average basis. The paper also discusses the reason of choosing SIFT descriptor for comparison and its efficacy in different scenarios. The paper also reasons the robustness of hand crafted feature descriptors and why they hold an upper hand among many other deep learning methods. [ABSTRACT FROM AUTHOR]

The classification of medical images enables physicians to perform expeditious and accurate data analysis, increasing the chances of timely disease diagnosis and early intervention to the patient. However, classification is a time‐consuming and labour intensive process when done manually. The Capsule Network (CapsNet) architecture has advantages in accurately and quickly classifying medical images due to its ability to evaluate images within part‐whole relationships, robustness to data rotations and affine transformations, and good performance on small datasets. However, CapsNet may demonstrate low performance on complex datasets. In this study, a new CapsNet model named MResCaps is proposed to overcome this disadvantage and enhance its performance on complex images. MResCaps utilizes an increasing number of residual blocks in each layer in parallel lane to obtain rich feature maps at different levels, aiming to achieve high success in the classification of various medical images. To evaluate the model's performance, the CIFAR10 dataset and the DermaMNIST, PneumoniaMNIST, and OrganMNIST‐S datasets from the MedMNIST dataset collection are used. MResCaps outperformed CapsNet by 20% in terms of accuracy on the CIFAR10 dataset. In addition, AUC values of 96.25%, 96.30%, and 97.12% were achieved in DermaMNIST, PneumoniaMNIST, and OrganMNIST‐S datasets, respectively. The results show that the proposed new model MResCaps improves the performance of CapsNet in the classification of complex and medical images. Furthermore, the model has demonstrated a better performance in comparison with extant studies in the literature. This study aims to contribute significantly to the literature by introducing a novel perspective on CapsNet‐based architectures for the classification of medical images through a parallel‐laned architecture and a rich feature capsule‐focused approach. [ABSTRACT FROM AUTHOR]

A family of fractal arrangements of circles is introduced for each imaginary quadratic field K. Collectively, these arrangements contain (up to an affine transformation) every set of circles in the extended complex plane with integral curvatures and Zariski dense symmetry group. When that set is a circle packing, we show how the ambient structure of our arrangement gives a geometric criterion for satisfying the asymptotic local–global principle. Connections to the class group of K are also explored. Among them is a geometric property that guarantees certain ideal classes are group generators. [ABSTRACT FROM AUTHOR]

The novelty of this work is that it is the first to introduce complex-valued suprametric spaces and apply it to Fractal Generation and mixed Volterra–Fredholm Integral Equations. In the realm of fuzzy logic, complex-valued suprametric spaces provide a robust framework for quantifying the similarity between fuzzy sets; for instance, utilizing a complex-valued suprametric approach, we compared the similarity between fuzzy sets represented by complex-valued feature vectors, yielding quantitative measures of their relationships. Thereafter, we establish related fixed point results and their applications in algorithmic and numerical contexts. The study then delves into the generation of fractals, exemplified by the Barnsley Fern fractal, utilizing sequences of affine transformations within complex-valued suprametric spaces. Moreover, this article presents two algorithms for soft computing and fractal generation. The first algorithm uses complex-valued suprametric similarity for fuzzy clustering, iteratively assigning fuzzy sets to clusters based on similarity and updating cluster centers until convergence. The distinctive pattern of the Barnsley Fern fractal is produced by the second algorithm's repetitive affine transformations, which are chosen at random. These techniques demonstrate how well complex numbers cluster and how simple procedures can create complicated fractals. Moving beyond fractal generation, the paper addresses the solution of mixed Volterra–Fredholm integral equations in the complex plane using our results, demonstrating numerical illustrations of complex-valued integral equations. [ABSTRACT FROM AUTHOR]

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τ i , weighted nuclear norm, and the L 2 , 1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (L w , ∗) to assign weights to singular values to each retinal images and utilize the L 2 , 1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τ i is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τ i , by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method's superiority over existing state-of-the-art methods across various datasets. [ABSTRACT FROM AUTHOR]

In this paper, we examined the codimension-two bifurcation analysis of a two-dimensional discrete epidemic model. More precisely, we examined the codimension-two bifurcation analysis at an endemic equilibrium state associated with $ 1:2 $, $ 1:3 $ and $ 1:4 $ strong resonances by bifurcation theory and series of affine transformations. Finally, theoretical results were carried out numerically.

Nowadays, with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology, people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral images. The optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral images. In this study, the geometric correction accuracy under various geometric correction models (including affine transformation model, local triangulation model, polynomial model, direct linear transformation model, and rational function model) and resampling methods (including nearest neighbor resampling method, bilinear interpolation resampling method, and cubic convolution resampling method) were analyzed. Furthermore, the distribution, number, and accuracy of control points were analyzed based on the control variable method, and precise ground control points (GCPs) were analyzed. The results showed that the average geometric positioning error of UAV hyperspectral images (at 80 m altitude AGL) without geometric correction was as high as 3.4041 m (about 65 pixels). The optimal geometric correction model and parameter combination of the UAV hyperspectral image (at 80 m altitude AGL) used a local triangulation model, adopted a bilinear interpolation resampling method, and selected 12 edgemiddle distributed GCPs. The correction accuracy could reach 0.0493 m (less than one pixel). This study provides a reference for the geometric correction of UAV hyperspectral images. [ABSTRACT FROM AUTHOR]

Urine sediment examination (USE) is an essential aspect in detecting urinary system diseases, and it is a prerequisite for diagnostic procedures. Urine images are complex, containing numerous particles, which makes a detailed analysis and interpretation challenging. It is crucial for both patients and medical professionals to conduct urine analysis automatically, quickly and inexpensively, without compromising reliability. In this paper, we present a deep multi-modal fusion system, commonly employed in artificial intelligence, capable of automatically distinguishing particles in urine sediment. To achieve this objective, we first created a new dataset comprising erythrocytes, leukocytes, yeast, epithelium, bacteria, crystals, cylinders, and other particles (such as sperm). The data were gathered from urinalysis requests made between July 2022 and September 2022 at the biochemistry laboratory of Fethi Sekin Medical Center Hospital. A dataset containing 8509 images was compiled using the Optika B293PLi microscope with trinocular brightfield. We propose a 5-step process for detecting particles in the dataset using a multi-modal fusion deep learning model: i) The obtained images were augmented by applying affine transformation. ii) To distinguish images, we opted for ResNet18 and ResNet50 models, which yielded high performance in medical data. iii) Feature vectors from both models were fused to generate more consistent, accurate, and useful particle features. iv) We employed ReliefF, Neighborhood Component Analysis (NCA), and Minimum-Redundancy Maximum-Relevancy (mRMR) feature selection methods, widely used to determine features that maximise particle discrimination success. v) In the final step, Support Vector Machine (SVM) was utilised to distinguish the particles. The results demonstrate that the highest accuracy value achieved is 98.54 % when employing the ReliefF algorithm. Contributions of the study include eliminating standardisation differences in manual microscopy, achieving high accuracy in particle discrimination, offering an artificial intelligence-based system applicable in laboratory environments, and providing the dataset as educational and practical material for biochemistry professionals. [ABSTRACT FROM AUTHOR]

We present several algorithms designed to learn a pattern of correspondence between 2 data sets in situations where it is desirable to match elements that exhibit a relationship belonging to a known parametric model. In the motivating case study, the challenge is to better understand micro-RNA regulation in the striatum of Huntington's disease model mice. The algorithms unfold in 2 stages. First, an optimal transport plan P and an optimal affine transformation are learned, using the Sinkhorn–Knopp algorithm and a mini-batch gradient descent. Second, P is exploited to derive either several co-clusters or several sets of matched elements. A simulation study illustrates how the algorithms work and perform. The real data application further illustrates their applicability and interest. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is widely used in clinical diagnoses because of its nonionizing radiation, low cost, and noninvasive operation. However, making a diagnosis based on ultrasound images is a labor-intensive process. An accurate lesion classification system can thus be used to assist doctors in making diagnoses. The performance of classification algorithms typically improves when they are trained on large, labeled datasets. However, collecting labeled data is an expensive and time-consuming task. Therefore, performing lesion classification via ultrasound images is still challenging due to the small number of available training samples. To address this issue, a data augmentation method for ultrasound images based on a conditional generative adversarial network was proposed in this study to perform lesion classification. A real image was input into the generative adversarial network to constrain the mapping between the images. Then, the data augmentation process based on the conditional generative adversarial network generated the corresponding segmentation masks by category. Considering that the data augmentation method based on affine transformation can generate only fake ultrasound images or segmentation masks separately, this study proposed to use image-to-image translation to generate fake ultrasound images from the corresponding segmentation masks. The ResNet-50 was used to classify benign and malignant lesions to validate the effectiveness of the proposed approach. The results showed that, by comparing to the traditional data augmentation method based on affine transformation in terms of four evaluation metrics, the average performances of the proposed method increased by approximately 13.05% and 12.85% for the classification of lesions in the segmented masks and ultrasound images of lymph nodes and breasts, respectively. The results suggested that the proposed method could realize the purpose of data augmentation and greatly improve the classification performance. [ABSTRACT FROM AUTHOR]

In this paper, we examined the codimension-two bifurcation analysis of a two-dimensional discrete epidemic model. More precisely, we examined the codimension-two bifurcation analysis at an endemic equilibrium state associated with 1: 2, 1: 3 and 1: 4 strong resonances by bifurcation theory and series of affine transformations. Finally, theoretical results were carried out numerically. [ABSTRACT FROM AUTHOR]

Integrating optical images with Light Detection and Ranging (LiDAR) data is an important advance in Photogrammetry, Geomatics and Computer Vision, registering the strengths of both modalities (height and spectral information). Most orthoimages and aerial LiDAR data are georeferenced to a common ground coordinate system; however, a registration gap remains, and achieving high-accuracy registration between these datasets is challenging due to their differing data formats and frames of reference. In this paper, we propose an approach to enhance camera-LiDAR registration through combined LiDAR feature layer generation and Deep Learning. Our method involves creating weighted combinations of feature layers from LiDAR data, leveraging intensity, elevation, and bearing angle attributes. Subsequently, a 2D-2D Graph Neural Network (GNN) pipeline serves as an intermediate step for feature detection and matching, followed by a 2D-3D affine transformation model to register optical images to point clouds. Experimental validation across aerial scenes demonstrates significant improvements in registration accuracy. Notably, in urban building areas, we achieved an RMSE of around 1.1 pixel, marking a reduction of 5 pixels compared to georeferenced baseline values. In rural road scenes, our method yielded a pixel RMSE of 1.3, with a 4-pixel reduction compared to baseline results. Additionally, in water scenes, which tend to be noisy in LiDAR data, we achieved a pixel RMSE of 1.8, representing a slight half-pixel reduction compared to the baseline. Therefore, by using weighted and combined LiDAR feature layer and GNN feature matching, this approach augments the number of key points and matches, directly correlating with the observed registration reduction in pixel RMSE across diverse aerial scene types. [ABSTRACT FROM AUTHOR]

To study the fruit image fusion technology in the case of thermal infrared and visible heterogeneous sources and the method of online defect detection on fruit fusion images, this paper takes apple as the research object and proposes a registration algorithm for thermal infrared and visible images of apple based on the registration of feature points with an active contour model. First, by designing a thermal infrared and visible image acquisition system, thermal infrared and visible images of apple in the same scene are obtained simultaneously. Then, the improved Chan-vese model is adopted to obtain the active contour segmentation curves of the thermal infrared and visible images of apple respectively. Next, the average Euclidean distance of all adjacent edge points on the active contour segmentation curve is calculated, and the alignment feature point set is constructed by the linear interpolation method based on the obtained average distance, and the optimal scale transformation factor and the optimal horizontal transformation factor are obtained by calculating the partial Hausdorff distance between the two feature point sets. Finally, the registered visible image is acquired based on the obtained affine transformation matrix, thus realizing the registration of the thermal infrared and visible images of apple. The experimental results on the self-built image dataset indicate that the algorithm proposed in this paper can accurately match the heterogeneous images of intact fruit, calyx/stems, and defective fruit, and it performs excellently in terms of precise matching rate and root mean square error, and the high alignment success rate of 96%. Also, it has much better performance than other methods. The proposed registration algorithm can accurately match thermal infrared and visible images of apple, and lays the foundation for further research on the image fusion of thermal infrared and visible, apple surface defect detection, as well as the construction of online dual-light apple grading systems. [ABSTRACT FROM AUTHOR]

Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming approach. However, many existing LfD methods encounter issues such as convergence failure and lack of generalization ability. In this paper, we propose: (1) a generative adversarial network (GAN)-based model with multilayer perceptron (MLP) architecture, coupled with a novel loss function designed to mitigate convergence issues; (2) an affine transformation-based generalization method aimed at enhancing LfD tasks by improving their generalization performance; (3) a data preprocessing method tailored to facilitate deployment on robotics platforms. We conduct experiments on a UR5 robotic platform tasked with handwritten digit recognition. Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations. This leads to outstanding convergence and generalization performance. [ABSTRACT FROM AUTHOR]

One of the classical problems of optimization theory in a finite-dimensional space is to find a minimum of a function on a nonempty set. Usually, finding the precise solution to this task analytically requires a lot of computational resources or is even impossible at all. So, approximate methods are used most often in practical cases. One of the simplest and the most well-known among such approximate methods for unconditional optimization is the method of gradient descent; its generalization for conditional optimization was found in 1964, the method of projected gradient. For some simple sets (line segment, parallelepiped, ball), the projection of the point on the set can be easily found by an explicit formula. However, for more complicated sets (e.g., an ellipse), projecting becomes a separate task. Nevertheless, sometimes computing projection can be simplified by affine transform; e.g., an ellipse can be transformed into a ball by affine (moreover, by linear) transformation. The paper aims to simplify the problem of minimizing function on the set by changing the condition set by affine transform F(x)= Ax+b, where A is a non-degenerated square matrix, and b is a fixed vector of proper dimension. [ABSTRACT FROM AUTHOR]

Enriching and updating maps are among the most important tasks of any urban management organization for informed decision making. Urban cadastral map enrichment is a time-consuming and costly process, which needs an expert's opinion for quality control. This research proposes a smart framework to enrich a cadastral base map using a more up-to-date map automatically by machine learning algorithms. The proposed framework has three main steps, including parcel matching, parcel change detection and base map enrichment. The matching step is performed by checking the center point of each parcel in the other map parcels. Support vector machine and random forest classification algorithms are used to detect the changed parcels in the base map. The proposed models employ the genetic algorithm for feature selection and grey wolf optimization and Harris hawks optimization for hyperparameter optimization to improve accuracy and performance. By assessing the accuracies of the models, the random forest model with feature selection and grey wolf optimization, with an F1-score of 0.9018, was selected for the parcel change detection method. Finally, the detected changed parcels in the base map are deleted and relocated automatically with corresponding parcels in the more up-to-date map by the affine transformation. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the Zuker RNA folding algorithm, a bioinformatics task with non-serial polyadic dynamic programming and non-uniform loop dependencies. The intricate dependence pattern is represented using affine formulas, enabling the automatic application of tiling strategies via the polyhedral method. Three source-to-source compilers—PLUTO, TRACO, and DAPT—are employed, utilizing techniques such as affine transformations, the transitive closure of dependence relation graphs, and space–time tiling to generate cache-efficient codes, respectively. A dedicated transpose code technique for non-serial polyadic dynamic programming codes is also examined. The study evaluates the performance of these optimized codes for speed-up and scalability on multi-core machines and explores energy efficiency using RAPL. The paper provides insights into related approaches and outlines future research directions within the context of bioinformatics algorithm optimization. [ABSTRACT FROM AUTHOR]

We study partial orders defined on the set of points of space-time that are invariant under Lorentz transformations. Kirszbraun's Theorem allows to show that world lines of particles evolving in space-time are precisely the maximal chains in the causality order. We show that the causality order is well behaved in the sense that it is gradable and level sets under various gradings are precisely the anti-chain cutsets. We also show that the causality orders corresponding to different light speed parameters c are essentially the only partial orders invariant under Lorentz transformations and under some other, more obvious affine transformations of space-time. We characterize optical lines and hyperplanes, inertial lines and planes, and separation lines as well, in terms of the causality order and use these characterizations to provide a variant proof of the Alexandrov-Zeeman Theorem. [ABSTRACT FROM AUTHOR]

In this paper, we determine the set S of straight lines L 0 that have intersections with four given distinct lines L 1 , ... , L 4 in R 3. If any two of the four given lines are skew, i.e., not co-planar, Bielinski and Lapinska used techniques in projective geometry to show that there are either zero, one, or two elements in the set S. Using linear algebra techniques, we determine S and show that there are no, one, two or infinitely many elements L 0 in S , where the last case was overlooked in the earlier paper. For the sake of completeness, we provide a comprehensive determination of all the elements L 0 in S if at least two of the four given lines are co-planar. In this scenario, there may also be zero, one, two, or infinitely many solutions. [ABSTRACT FROM AUTHOR]

This article, titled "Calculation of the Limit of a Special Sequence of Trigonometric Functions," discusses the problem of calculating the exact spectral radius of a special family of functional operators. The authors provide a theorem that solves this problem for rational and simple radical values, but the problem remains open for arbitrary algebraic numbers. The authors also present an elementary proof of a general formula that gives the value of the limit and an estimate of the rate of convergence of the sequence. The article includes theorems, proofs, and auxiliary assertions to support their findings. [Extracted from the article]

In the last several decades, application of affine transformation and optimization approaches have received enough attention in the domain of monomodal/multimodal image registration. A novel and robust image registration method is proposed which leverages teaching learning-based optimization (TLBO) to obtain the optimal value of rigid transformation parameters. Further, speeded up robust features (SURF) framework is employed to extract features along with Random sample consensus (RANSAC) algorithm. Afterwards, projective transformation is used to obtain more accurate registered image. To remove noise, gaussian filter is used during pre-processing and then normalization is carried out. TLBO is used to identify the optimal geometric transformation parameters by considering the mutual information (MI) as an objective function. This method detects keypoints (features) using SURF then K-nearest neighbour (KNN) is employed to match the detected features. Furthermore, RANSAC eliminates false matches. The registered image with optimal value of rigid transformation parameters is obtained and then enhanced by using SURF-RANSAC followed by projective transformation. Experiments are conducted on Whole Brain ATLAS and KAGGLE datasets. Robustness of proposed approach is verified by the improvement in evaluation metric, such as structure similarity index measure (SSIM) by 8%, MI by 12.71% on monomodal and root mean square error (RMSE) by 41.44% on multimodal images as compared to recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Eye movements offer precious information about persons’ state. Video surveillance, marketing, driver fatigue as well as medical diagnosis assistance applications manage eye behavior. We propose a new method for efficiently detecting eye movement. In this paper, we combine circle eye model with eye feature method to improve the accuracy. A set of detectors estimate the eyes centers to increase the localization rate. As a pre-processing stage, the mean of the edges yields the center of the two eye regions. Image treatment operations reduce the ROI. A Circle Hough Transform (CHT) algorithm is adopted in a modified version as a detector to find the circle eye in the image; the circle center found represents the eye's pupil estimation. We introduced the Maximally Stable Extremal Region (MSER) as a second detector, which has never been used for eye localization. Invariant to continuous geometric transformations and affine intensity changes and detected at several scales, MSERs efficiently detect regions of interest, in our case eye regions, and precisely, their centers. Ellipses fit MSERs, and their centroid estimation match eyes center. We demonstrate that the true eye centers can be found by combining these methods. The validation of the proposed method is performed on a very challenging BioID base. The proposed approach compares well with existing state-of-the-art techniques and achieves an accuracy of 82.53% on the BioID database when the normalized error is less than 0.05, without prior knowledge or any learning model. [ABSTRACT FROM AUTHOR]

In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%. [ABSTRACT FROM AUTHOR]