Affine Transformation Research Articles

Digital holographic content manipulation for wide-angle holographic near-eye displays

In recent years, the development of holographic near-eye displays (HNED) has surpassed the progress of digital hologram recording systems, especially in terms of wide-angle viewing capabilities. Thus, there is capture-display parameters incompatibility, which makes it impossible to reconstruct recorded objects in wide-angle display. This paper presents a complete imaging chain extending the available content for wide-angle HNED of pupil and non-pupil configuration with narrow-angle digital holograms of real objects. To this end, a new framework based on the phase-space approach is proposed that includes a set of affine transformations required to account for all differences in capture-display cases. The developed method allows free manipulation of the geometry of reconstructed objects, including axial and lateral positioning and size scaling. At the same time, it has a low computational effort. The presented work is supported with non-paraxial formulas developed using the phase-space approach, enabling accurate tracing of the holographic signal, its reconstruction, and measuring appearing deformations. The applicability of the proposed hologram manipulation method is proven with experimental results of digital hologram reconstruction in wide-angle HNED.

Multi-AUV Formation Reconfiguration Obstacle Avoidance Algorithm Based on Affine Transformation and Improved Artificial Potential Field Under Ocean Currents Disturbance

Multi-AUV Formation Reconfiguration Obstacle Avoidance Algorithm Based on Affine Transformation and Improved Artificial Potential Field Under Ocean Currents Disturbance

Energy-Efficiency Optimization With Model Convexification for Wireless Ad Hoc Networks With Multi-Packet Reception Capability

Energy efficiency is a significant requirement of resource management and design optimization in information networks. In this work, we propose an iterative fractional programming framework embedded with a distributed primal-dual extra-gradient projection algorithm, which addresses a wide class of the energy-efficiency optimization problems in wireless ad hoc networks with full-duplex radios and multi-packet reception capability. Specifically, we propose a model convexification mechanism by joining an affine transformation and an exponential transformation into the nonlinear fractional programming, which enables us to deal with the challenge arising from the complexity and non-convex structure of the original problem. With the model convexification, we can map the non-convex power control space into a convex space and equivalently derive a sequence of convex subproblems, which relaxes the convexity assumption widely adopted in the existing literature. We further propose a distributed primal-dual algorithm based on extra-gradient projection to solve the convex subproblem at each iteration of the fractional programming. The convergence of the proposed iterative fractional programming and the distributed optimization method is theoretically proven. Numerical results also verify the proposed method and demonstrate its superior performance over other representative distributed and centralized schemes in terms of achieving global energy efficiency.

A Note On Kantorovich Type Operators Which Preserve Affine Functions

The authors present an integral widening of operators which preserve affine functions. Influenced by the operators which preserve affine functions, we define the integral extension of these operators. We give quantitative type theorem using weighted modulus of continuity. Withal quantitative Voronovskaya theorem is aquired by classical modulus of continuity. When the moments of the operator are known, convergence results with the moments obtained for the Kantorovich form of the same operator is given.

Robust Learning from Demonstration Based on GANs and Affine Transformation

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.

“The Anamorphic Journey”: Jean‐Luc Marion on Revelation

AbstractIn part four of D'ailleurs, la Révélation (2020) Jean‐Luc Marion evokes the state of being seized by a saturated phenomenon, whether divinely revelatory or not, which guides one “across the desert on an anamorphic journey [parcours d'anamorphose].” The figure of anamorphosis is well‐known to anyone familiar with sixteenth‐century art, visual or literary; it comes directly from experiments with mirrors or perspective, giving rise with the latter to examples of affine transformation. Undoubtedly, Hans Holbein the Younger's double portrait “The Ambassadors” (1533) is the best‐known visual example: if one looks directly at the canvas in the National Gallery in London, one sees a diagonal blur across its bottom center, beneath Jean de Dinteville (1504‐1555), French Ambassador to London, and Georges de Selve (1508‐1541), Bishop of Lavaur and French Ambassador to the Republic of Venice. Only when one views the blur from a particular point—either low on the left‐hand side or high on the right‐hand side—can one see that it is a skull. The vanity of human achievement or, worse, human pride, is made manifest only when the artwork is viewed from a perspective that one is constrained to adopt. The sole sign of hope is a crucifix in the top left of the canvas.

Scaling Up Pareto Optimization for Tree Structures with Affine Transformations: Evaluating Hybrid Floating Solar-Hydropower Systems in the Amazon

Sustainability challenges inherently involve the consideration of multiple competing objectives. The Pareto frontier – the set of all optimal solutions that cannot be improved with respect to one objective without negatively affecting another – is a crucial decision-making tool for navigating sustainability challenges as it highlights the inherent trade-offs among conflicting objectives. Our research is motivated by the strategic planning of hydropower in the Amazon basin, one of the earth’s largest and most biodiverse river systems, where the need to increase energy production coincides with the pressing requirement of minimizing detrimental environmental impacts. We investigate an innovative strategy that pairs hydropower with Floating Photovoltaic Solar Panels (FPV). We provide a new extended multi-tree network formulation, which enables the consideration of multiple dam configurations. To address the computational challenge of scaling up the Pareto optimization framework to tackle multiple objectives across the entire Amazon basin, we further enhance the state-of-the-art algorithm for Pareto frontiers in tree-structured networks with two improvements. We introduce affine transformations induced by the sub-frontiers to compute Pareto dominance and provide strategies for merging sub-trees, significantly increasing the pruning of dominated solutions. Our experiments demonstrate considerable speedups, in some cases by more than an order of magnitude, while maintaining optimality guarantees, thus allowing us to more effectively approximate the Pareto frontiers. Moreover, our findings suggest significant shifts towards higher energy values in the Pareto frontier when pairing hybrid hydropower with FPV solutions, potentially amplifying energy production while mitigating adverse impacts.

Continuous Piecewise-Affine Based Motion Model for Image Animation

Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertainment, and education. Recent unsupervised methods utilize affine and thin-plate spline transformations based on keypoints to transfer the motion in driving frames to the source image. However, limited by the expressive power of the transformations used, these methods always produce poor results when the gap between the motion in the driving frame and the source image is large. To address this issue, we propose to model motion from the source image to the driving frame in highly-expressive diffeomorphism spaces. Firstly, we introduce Continuous Piecewise-Affine based (CPAB) transformation to model the motion and present a well-designed inference algorithm to generate CPAB transformation from control keypoints. Secondly, we propose a SAM-guided keypoint semantic loss to further constrain the keypoint extraction process and improve the semantic consistency between the corresponding keypoints on the source and driving images. Finally, we design a structure alignment loss to align the structure-related features extracted from driving and generated images, thus helping the generator generate results that are more consistent with the driving action. Extensive experiments on four datasets demonstrate the effectiveness of our method against state-of-the-art competitors quantitatively and qualitatively. Code will be publicly available at: https://github.com/DevilPG/AAAI2024-CPABMM.

Direction-Aware Video Demoiréing with Temporal-Guided Bilateral Learning

Moiré patterns occur when capturing images or videos on screens, severely degrading the quality of the captured images or videos. Despite the recent progresses, existing video demoiréing methods neglect the physical characteristics and formation process of moiré patterns, significantly limiting the effectiveness of video recovery. This paper presents a unified framework, DTNet, a direction-aware and temporal-guided bilateral learning network for video demoiréing. DTNet effectively incorporates the process of moiré pattern removal, alignment, color correction, and detail refinement. Our proposed DTNet comprises two primary stages: Frame-level Direction-aware Demoiréing and Alignment (FDDA) and Tone and Detail Refinement (TDR). In FDDA, we employ multiple directional DCT modes to perform the moiré pattern removal process in the frequency domain, effectively detecting the prominent moiré edges. Then, the coarse and fine-grained alignment is applied on the demoiréd features for facilitating the utilization of neighboring information. In TDR, we propose a temporal-guided bilateral learning pipeline to mitigate the degradation of color and details caused by the moiré patterns while preserving the restored frequency information in FDDA. Guided by the aligned temporal features from FDDA, the affine transformations for the recovery of the ultimate clean frames are learned in TDR. Extensive experiments demonstrate that our video demoiréing method outperforms state-of-the-art approaches by 2.3 dB in PSNR, and also delivers a superior visual experience.

Semantic Lens: Instance-Centric Semantic Alignment for Video Super-resolution

As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR named Semantic Lens, predicated on semantic priors drawn from degraded videos. Specifically, video is modeled as instances, events, and scenes via a Semantic Extractor. Those semantics assist the Pixel Enhancer in understanding the recovered contents and generating more realistic visual results. The distilled global semantics embody the scene information of each frame, while the instance-specific semantics assemble the spatial-temporal contexts related to each instance. Furthermore, we devise a Semantics-Powered Attention Cross-Embedding (SPACE) block to bridge the pixel-level features with semantic knowledge, composed of a Global Perspective Shifter (GPS) and an Instance-Specific Semantic Embedding Encoder (ISEE). Concretely, the GPS module generates pairs of affine transformation parameters for pixel-level feature modulation conditioned on global semantics. After that the ISEE module harnesses the attention mechanism to align the adjacent frames in the instance-centric semantic space. In addition, we incorporate a simple yet effective pre-alignment module to alleviate the difficulty of model training. Extensive experiments demonstrate the superiority of our model over existing state-of-the-art VSR methods.

VLCounter: Text-Aware Visual Representation for Zero-Shot Object Counting

Zero-Shot Object Counting~(ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars. To deal with ZSOC, preceding studies proposed a two-stage pipeline: discovering exemplars and counting. However, there remains a challenge of vulnerability to error propagation of the sequentially designed two-stage process. In this work, we propose an one-stage baseline, Visual-Language Baseline (VLBase), exploring the implicit association of the semantic-patch embeddings of CLIP. Subsequently, we extend the VLBase to Visual-language Counter (VLCounter) by incorporating three modules devised to tailor VLBase for object counting. First, we introduce Semantic-conditioned Prompt Tuning (SPT) within the image encoder to acquire target-highlighted representations. Second, Learnable Affine Transformation (LAT) is employed to translate the semantic-patch similarity map to be appropriate for the counting task. Lastly, we transfer the layer-wisely encoded features to the decoder through Segment-aware Skip Connection (SaSC) to keep the generalization capability for unseen classes. Through extensive experiments on FSC147, CARPK, and PUCPR+, we demonstrate the benefits of our end-to-end framework, VLCounter. Code is available at https://github.com/seunggu0305/VLCounter

NTK-3D chaotic map and a non-associative LA-field sequences for image encryption

The imperative protection of digital information and its secure transmission has evolved into a complex challenge. Within this context, non-linear dynamical systems assume a pivotal role in fortifying content security by their unpredictable behavior and susceptibility to initial conditions. This study introduces an innovative 3D-chaotic system. The proposed map is then examined through bifurcation diagrams and randomness tests, revealing an intricate chaotic behavior of the system. Moreover, an advanced method of securing information is introduced, incorporating a non-associative LA-field sequences-oriented affine transformation. The initial procurement of a non-associative LA field through computational approaches is succeeded by utilizing sequences of its components to establish an affine transformation. This transformative process serves as the central aspect of the suggested color image encryption method. In this strategy, the NTK-3D map and the innovative affine transformation collaborate to introduce both confusion and diffusion, thereby fortifying the overall security stance. The non-associative behavior of the involved algebraic structure, combined with a dynamical system, results in highly resilient encryption. The proposed scheme is not only time-efficient but also has an expanded key space. The results of some performance assessments demonstrate that the suggested encryption technique for the digital medium is capable of resisting well-known attacks.

Mode-shape magnification in high-speed camera measurements

If motion, identified using image-based methods, is too small to be seen with the naked eye, motion magnification can be used to help with the visualization. Established motion-magnification methods magnify (typically up to 1000 times) the band-passed content of the image data. Especially at higher frequencies, the amplitudes of measured displacements are often below the noise floor. In this research, a novel method for amplifying vibrations, based on experimental modal analysis (EMA), is introduced. The response of the examined structure to dynamic excitation is measured with a simplified, gradient-based, optical flow method and used to perform a hybrid modal analysis in conjunction with a reference accelerometer response measurement. Such a hybrid approach can: (a) identify structural dynamics significantly in the sub-pixel range, and (b) significantly below the image noise floor. The image of the vibrating structure is subdivided using a planar triangle mesh, which is then warped in accordance with the identified mode shape. A mesh-element-wise affine transformation is performed to obtain an image of the magnified mode shape. In the experimental part, the proposed method achieved magnification factors of approximately 40 thousand times, which is an order of magnitude deeper into noise than available before; additionally, the proposed approach is numerically significantly less demanding.The introduced mode-shape magnification presents an alternative to existing motion-magnification methods for applications where the harmonic displacement information is hidden by image noise.

Differential Effects of Benzalkonium Chloride on Human Trabecular Meshwork Cells Not Treated or Treated with Transforming Growth Factor-β2 or Dexamethasone.

Purpose: The objective of the present study was to evaluate the effects of low concentrations of benzalkonium chloride (BAC) (10-7%, 10-6%, or 10-5%) on healthy and glaucomatous human trabecular meshwork (HTM) cells. For this purpose, we used in vitro models replicating a healthy HTM and HTM with primary open-angle glaucoma (POAG) or steroid-induced glaucoma (SG) using two-dimensional (2D) cultures of HTM cells not treated or treated with a 5 ng/mL solution of transforming growth factor-β2 or 250 nM dexamethasone (DEX). Methods: Analyses were carried out for (1) the intercellular affinity function of 2D HTM monolayers, as determined by transepithelial electrical resistance (TEER) measurements; (2) cell viability; (3) cellular metabolism by using a Seahorse bioanalyzer; and (4) expression of extracellular matrix (ECM) molecules, an ECM modulator, and cell junction-related molecules. Results: In the absence and presence of BAC (10-7% or 10-5%), intercellular affinity function determined by TEER and cellular metabolic activities were significantly and dose dependently affected in both healthy and glaucomatous HTM cells despite the fact that there was no significant decrease in cell viabilities. However, the effects based on TEER values were significantly greater in the healthy HTM. The mRNA expression of several molecules that were tested was not substantially modulated by these concentrations of BAC. Conclusions: The findings reported herein suggest that low concentrations of BAC may have unfavorable adverse effects on cellular metabolic capacity by inducing increases in the intercellular affinity properties of the HTM, but those effects of BAC were different in healthy and glaucomatous HTM cells.

Correction to "The β-Blocker Carvedilol and Related Aryloxypropanolamines Promote ERK1/2 Phosphorylation in HEK293 Cells with K A Values Distinct From Their Equilibrium Dissociation Constants as β 2-Adrenoceptor Antagonists: Evidence for Functional Affinity".

Correction to "The β-Blocker Carvedilol and Related Aryloxypropanolamines Promote ERK1/2 Phosphorylation in HEK293 Cells with K A Values Distinct From Their Equilibrium Dissociation Constants as β 2-Adrenoceptor Antagonists: Evidence for Functional Affinity".

Enhanced Vigenere and affine ciphers surrounded by dual genetic crossover mechanisms for encrypting color images

This paper introduces an enhanced technique for encrypting color images, surpassing the effectiveness of genetic crossover and substitution methods. The approach integrates dynamic random functions to bolster the integrity of the resulting vector, elevating temporal complexity to deter potential attacks. The enhancement entails amalgamating genetic crossover using two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our method commences with an initial genetic crossover inspired by DNA behavior at the pixel level. This process is followed by a confusion-diffusion lap, strengthening the relationship between encrypted pixels and their neighboring counterparts. The confusion-diffusion mechanism employs dynamic pseudorandom affine functions at the pixel level. Subsequently, a second genetic crossover operator is applied. Simulations conducted on various images with varying sizes and formats demonstrate the resilience of our approach against statistical and differential attacks.

In vivo affinity maturation of mouse B cells reprogrammed to express human antibodies.

Mice adoptively transferred with mouse B cells edited via CRISPR to express human antibody variable chains could help evaluate candidate vaccines and develop better antibody therapies. However, current editing strategies disrupt the heavy-chain locus, resulting in inefficient somatic hypermutation without functional affinity maturation. Here we show that these key B-cell functions can be preserved by directly and simultaneously replacing recombined mouse heavy and kappa chains with those of human antibodies, using a single Cas12a-mediated cut at each locus and 5' homology arms complementary to distal V segments. Cells edited in this way to express the human immunodeficiency virus type 1 (HIV-1) broadly neutralizing antibody 10-1074 or VRC26.25-y robustly hypermutated and generated potent neutralizing plasma in vaccinated mice. The 10-1074 variants isolated from the mice neutralized a global panel of HIV-1 isolates more efficiently than wild-type 10-1074 while maintaining its low polyreactivity and long half-life. We also used the approach to improve the potency of anti-SARS-CoV-2 antibodies against recent Omicron strains. In vivo affinity maturation of B cells edited at their native loci may facilitate the development of broad, potent and bioavailable antibodies.

Multi-body dynamical modeling and prediction of flexible origami/kirigami structures by affine transformation

A generalized multi-body dynamical modeling method of Origami/Kirigami structures considering the elastic–flexible coupling of surfaces and nonlinear creases is proposed. For different structures with complex spatial configuration and constraints, an Affine Transformation (AT) technique based on Absolute Nodal Coordinate Formulation (ANCF) is proposed to make the modeling more convenient and efficient. The surfaces are discretized by triangle reduced elements, and the constitutive relations of creases are described by nonlinear state-dependent torque constraints. The proof of the topological invariance of configurations is also given. The simulation of the folding capacity and dynamic behaviors on two Origami structures, Origami solar array mechanism, and enclosed Yoshimura Origami, are given, which verifies the effectiveness of the modeling method. Additionally, the quantitative dynamic behaviors contributes to the design principle of a Kirigami structure for multi-stability. Comparisons between theoretical results from modeling and experiments demonstrate the effectiveness of the modeling method and crease design. The AT-ANCF lays a theoretical foundation for dynamic prediction and design for large deformation Origami/ Kirigami structures, which has significant potential applications in dynamic properties design in the fields of aerospace, robotic etc.

An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

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.

Prescribed performance adaptive fault‐tolerant control for nonlinear systems with actuator faults and input dead‐zone

AbstractThis paper investigates the prescribed performance adaptive fault‐tolerant control problem for nonlinear systems with actuator faults and input dead‐zone (IDZ). The partial loss of effectiveness fault and bias fault are considered. First, the asymmetric nonlinear IDZ is transformed into an affine function in the backstepping process. Second, the radial basis function neural network technique is applied to deal with the unknown uncertainty of the studied system. The state observer is designed to estimate the unmeasurable states in the nonlinear systems. Third, a fault‐tolerant controller is developed such that all signals are semi‐globally uniformly ultimately bounded for the closed‐loop system, and the tracking error remains within the specified performance requirements even though the actuator faults and IDZ occur. Finally, an electromechanical system example is provided to exhibit the utilizability of the obtained control technique.