Estimation Task Research Articles

A unified feature-motion consistency framework for robust image matching

Establishing reliable feature matches between a pair of images in various scenarios is a long-standing open problem in photogrammetry. Attention-based detector-free matching with coarse-to-fine architecture has been a typical pipeline to build matches, but the cross-attention module with global receptive field may compromise the structural local consistency by introducing irrelevant regions (outliers). Motion field can maintain structural local consistency under the assumption that matches for adjacent features should be spatially proximate. However, motion field can only estimate local displacements between consecutive images and struggle with long-range displacements estimation in large-scale variation scenarios without spatial correlation priors. Moreover, large-scale variations may also disrupt the geometric consistency with the application of mutual nearest neighbor criterion in patch-level matching, making it difficult to recover accurate matches. In this paper, we propose a unified feature-motion consistency framework for robust image matching (MOMA), to maintain structural consistency at both global and local granularity in scale-discrepancy scenarios. MOMA devises a motion consistency-guided dependency range strategy (MDR) in cross attention, aggregating highly relevant regions within the motion consensus-restricted neighborhood to favor true matchable regions. Meanwhile, a unified framework with hierarchical attention structure is established to couple local motion field with global feature correspondence. The motion field provides local consistency constraints in feature aggregation, while feature correspondence provides spatial context prior to improve motion field estimation. To alleviate geometric inconsistency caused by hard nearest neighbor criterion, we propose an adaptive neighbor search (soft) strategy to address scale discrepancy. Extensive experiments on three datasets demonstrate that our method outperforms solid baselines, with AUC improvements of 4.73/4.02/3.34 in two-view pose estimation task at thresholds of 5°/10°/20° on Megadepth test, and 5.94% increase of accuracy at threshold of 1px in homography task on HPatches datasets. Furthermore, in the downstream tasks such as 3D mapping, the 3D models reconstructed using our method on the self-collected SYSU UAV datasets exhibit significant improvement in structural completeness and detail richness, manifesting its high applicability in wide downstream tasks. The code is publicly available at https://github.com/BunnyanChou/MOMA.

Geometry-semantic aware for monocular 3D Semantic Scene Completion

Monocular Semantic Scene Completion (SSC) empowers intelligent devices to comprehend voxel occupancy (geometry) and semantics in 3D scenes, attracting significant attention in indoor and autonomous driving scenarios. However, existing monocular SSC models primarily map 2D images into 3D space, neglecting the potential benefits of leveraging semantic and geometric understanding in 2D. To address this, we propose the Proxy-embedding Parallel Multi-task Network (PPMNet), which aims to perceive the geometry and semantics of 3D space through depth estimation and semantic segmentation proxy tasks on the 2D perspective plane. Moreover, 2D plane features can be inversely projected into 3D space and subsequently processed using the 3D network. In addition, we enhance contextual awareness in both perspective planes and voxel grids through parallel 2D and 3D decoders. Furthermore, we employ Dual-Head Pyramid Pooling (DHPP) to aggregate information from these two representations. Finally, considering the class imbalance and label incompleteness in practical data, we design a local-to-global loss to prioritize challenging categories. Extensive experiments validate our superiority over state-of-the-art methods on the NYUv2 and SemanticKITTI datasets. The code is available at: https://github.com/luzonghao1/PPMNet.

Development of the algorithm for determining the thickness of the structural layers of the non-rigid pavement and searching for local inclusions using sub-surface georadiolocation methods

Problem. The article is part of the authors' research in the field of road construction using GPR technologies. Ensuring a certain level of pavement reliability on highways requires the development of methods and means for continuous information acquisition and processing in real time, taking into account the parameters of structural heterogeneity, heterogeneity of physical and mechanical parameters of structures, as well as their changes over time. The solution of this problem is limited by the complexity of tasks of obtaining and interpreting the data of subsurface sensing and the associated errors in determining the parameters of structures, imperfection of methods of flaw detection of structures with subsurface inhomogeneities, multifactorial tasks of estimation of physical and mechanical parameters of multicomponent materials. Goal. Improving the accuracy and reliability of determining the boundaries of layers of non-rigid pavement structure, which have a low level of electrodynamic contrast due to interpenetration of layer materials or changes in their physical and mechanical properties. The solution of this problem is connected with the development of a method of obtaining reflected GPR signals and the improvement of algorithms for their processing. Methodology. The development of methods for the analysis of the electromagnetic field with a variable state of polarization makes it possible to assess the localization of violations of the integrity of the pavement structure at a qualitatively new level and allows the positioning of small inclusions even with a slight contrast in the dielectric constant. The proposed method of recording the reflection of an ultra-wideband GPR pulse signal at different polarizations is implemented by measuring the reflected signal from a metal sheet at oblique incidence and parallel polarization, recording the reflected pulse at perpendicular polarization, followed by measuring the reflection coefficient from the structure of the nonrigid pavement under study at parallel and perpendicular polarization. The electrophysical parameter recovery procedure is based on the solution of inverse scattering problems and ensures the accuracy of determining the low-contrast boundaries of the pavement structure and increases the accuracy of positioning subsurface heterogeneities. GPR signal processing algorithms are based on procedures for analyzing their polarization state, which allows determining the geometric parameters of subsurface heterogeneities. Practical value. The combination of the obtained results increases the accuracy and reliability of the results of GPR diagnosis and assessment of the condition of non-rigid pavement based on the most complete and reliable information about the subsu

A novel hybrid genetic algorithm and Nelder-Mead approach and it’s application for parameter estimation

Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named the Genetic and Nelder-Mead Algorithm (GANMA). This hybrid approach aims to enhance performance across various benchmark functions and parameter estimation tasks. Methods GANMA combines the global search capabilities of GA with the local refinement strength of NM. It is first tested on 15 benchmark functions commonly used to evaluate optimization strategies. The effectiveness of GANMA is also demonstrated through its application to parameter estimation problems, showcasing its practical utility in real-world scenarios. Results GANMA outperforms traditional optimization methods in terms of robustness, convergence speed, and solution quality. The hybrid algorithm excels across different function landscapes, including those with high dimensionality and multimodality, which are often encountered in real-world optimization issues. Additionally, GANMA improves model accuracy and interpretability in parameter estimation tasks, enhancing both model fitting and prediction. Conclusions GANMA proves to be a flexible and powerful optimization method suitable for both benchmark optimization and real-world parameter estimation challenges. Its capability to efficiently explore parameter spaces and refine solutions makes it a promising tool for scientific, engineering, and economic applications. GANMA offers a valuable solution for improving model performance and effectively handling complex optimization problems.

Prescribed‐time distributed direct estimation under relative state measurements

AbstractThe distributed estimation technology is prevalently utilized to solve the leader‐following multi‐agent tracking problem. This technology poses a challenge in practice, since it generally relies on the available absolute state measurements. For this reason, a novel distributed estimation approach based on relative state measurements is developed in this article. The proposed method directly estimates the tracking error between the leader and each follower, rather than using an existing indirect way of estimating and making subtraction under absolute state measurements. Specifically, a distributed directed estimation is first studied to complete estimation tasks within prescribed time under the known directed networks. Then, a fully distributed directed estimation problem is considered under the unknown directed networks. Both distributed and fully distributed results are extended to the robustness cases to resist external disturbances. Simulation examples, including numerical examples and a multiship coordination example, are provided to demonstrate the effectiveness and advantages of the proposed distributed estimation method.

Mathematical knowledge of pre-service teachers when performing measurement estimation tasks

Mathematical knowledge of pre-service teachers when performing measurement estimation tasks

Probing bidirectional serial dependence in an N-back orientation estimation task

Probing bidirectional serial dependence in an N-back orientation estimation task

Multisensory perceptual and causal inference is largely preserved in medicated post-acute individuals with schizophrenia.

Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs. We combined psychophysics, Bayesian modeling, and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in response to audiovisual flash-beep sequences in medicated individuals with schizophrenia who exhibited limited psychotic symptoms. Seventeen participants with schizophrenia and 23 healthy controls reported either the number of flashes or the number of beeps of audiovisual sequences that varied in their audiovisual numeric disparity across trials. Both groups balanced sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both also showed comparable weighting of prior information regarding the signals' causal structure, although the schizophrenia group slightly overweighted prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of independent estimates of the flash and beep counts, followed by estimates that flexibly combine audiovisual inputs. Our results demonstrate that the core neurocomputational mechanisms for audiovisual perceptual and causal inference in number estimation tasks are largely preserved in our limited sample of medicated post-acute individuals with schizophrenia. Future research should explore whether these findings generalize to unmedicated patients with acute psychotic symptoms.

Multichannel algorithm for ambiguity resolution of combined code and phase measurements in a task of angular attitude estimation using signals of Global Navigation Systems

Navigation apparatus of Global Positioning Systems are wildly used in many applications one of which is attitude determination using global satellite navigation signals. For decision of this task there are used code and phase measurements for radiosignals received at space distribute points. It is known that phase measurements are characterized by ambiguity for resolution of which there are used different methods. Using more perfect method of ambiguity resolution one can get better angel estimation accuracy. In the article there is synthesized effective ambiguity resolution method of combined code and phase measurements using multichannel approach. Objective. To get more effective algorithm of code and phase ambiguity resolution in a task of attitude determination using global satellite navigation signals.

Validation of behavioral measures of social cognition in individuals diagnosed with schizophrenia.

Schizophrenia, a complex neuropsychiatric disorder, manifests severe impairments in social cognition, notably in Theory of Mind (ToM), empathy, and emotion recognition, which significantly influence social competence and overall functioning. These aspects are crucial for prognosis in individuals diagnosed with schizophrenia (SZ). This study validates a comics strip paradigm for ToM and empathy assessment, the Montreal Affective Voices (MAV) for measuring emotion recognition, and a Go-NoGo task for inhibition control estimation in individuals diagnosed with SZ, comparing their performance with healthy controls. SZ participants exhibited diminished abilities in the comics strip task, especially in ToM and empathy conditions, alongside challenges in identifying emotions from vocal cues in MAV. They responded slower and tended to be less accurate in the Go-NoGo task. The validated behavioral battery addresses the limitations of previous measures and emerges as a promising tool for future investigations into the neural systems underlying social cognition in schizophrenia. Such insights can lead to the development of long-needed treatment for negative symptoms and social dysfunctions in schizophrenia.

A Bayesian inference model can predict the effects of attention on the serial dependence in heading estimation from optic flow.

It has been demonstrated that observers can accurately estimate their self-motion direction (i.e., heading) from optic flow, which can be affected by attention. However, it remains unclear how attention affects the serial dependence in the estimation. In the current study, participants conducted two experiments. The results showed that the estimation accuracy decreased when attentional resources allocated to the heading estimation task were reduced. Additionally, the estimates of currently presented headings were biased toward the headings of previously seen headings, showing serial dependence. Especially, this effect decreased (increased) when the attentional resources allocated to the previously (currently) seen headings were reduced. Furthermore, importantly, we developed a Bayesian inference model, which incorporated attention-modulated likelihoods and qualitatively predicted changes in the estimation accuracy and serial dependence. In summary, the current study shows that attention affects the serial dependence in heading estimation from optic flow and reveals the Bayesian computational mechanism behind the heading estimation.

A general framework for generative self-supervised learning in non-invasive estimation of physiological parameters using photoplethysmography

Aligning physiological parameter labels with large-scale photoplethysmographic (PPG) data for deep learning is challenging and resource-intensive. While self-supervised representation learning (SSRL) can handle limited annotated data, the challenge lies in learning robust shared representations from vast unlabeled data and integrating various contextual cues to learn distinctive representations. To alleviate these challenges, a generative SSRL framework TS2TC is proposed to collaboratively utilize the temporal, spectrogram, and temporal-spectrogram mixed domains to explore and incorporate the unique features of PPG for universal and non-invasive physiological parameter estimation. Initially, a pretext task named Cross-Temporal Fusion Generative Anchor (CTFGA) is designed, modeling temporal dependencies and reconstructing independent segments at a coarse level to provide robust global feature extraction and local semantic contextual representation. The framework also includes sub-signals from PPG with diverse frequency scales and order derivatives reflecting hemodynamics to facilitate learning shared representations at varying semantic levels. Secondly, an advanced cognitive-inspired dual-process transfer (DPT) strategy is formulated, consisting of prior-dependent autonomous processes and posterior observation reasoning processes, to leverage the independent and integrated advantages of shared and specific representations. Furthermore, TS2TC introduces a novel bilinear temporal-spectrogram fusion method in the mixed domain, aligning latent representations from different domains, and establishing fine-grained contextual interactions at the feature level across multiple sources of information. Extensive experiments on physiological parameter estimation tasks showed that the joint performance of CTFGA and DPT outperforms standard generative learning significantly. TS2TC achieved an average 2.49% improvement in RMSE over the current state-of-the-art estimation methods with only 10% training data.

The effect of calibration training on the calibration of intelligence analysts' judgments

AbstractExperts are expected to make well‐calibrated judgments within their field, yet a voluminous literature demonstrates miscalibration in human judgment. Calibration training aimed at improving subsequent calibration performance offers a potential solution. We tested the effect of commercial calibration training on a group of 70 intelligence analysts by comparing the miscalibration and bias of their judgments before and after a commercial training course meant to improve calibration across interval estimation and binary choice tasks. Training significantly improved calibration and bias overall, but this effect was contingent on the task. For interval estimation, analysts were overconfident before training and became better calibrated after training. For the binary choice task, however, analysts were initially underconfident and bias increased in this same direction post‐training. Improvement on the two tasks was also uncorrelated. Taken together, results indicate that the training shifted analyst bias toward less confidence rather than having improved metacognitive monitoring ability.

Iterated rational quadratic kernel - High-order unscented Kalman filtering algorithm for spacecraft tracking

The high-speed development of space defense technology demands a high state estimation capacity for spacecraft tracking methods. However, reentry flight is accompanied by complex flight environments, which brings to the uncertain, complex, and strongly coupled non-Gaussian detection noise. As a result, there are several intractable considerations on the problem of state estimation tasks corrupted by complex non-Gaussian outliers for non-linear dynamics systems in practical application. To address these issues, a new iterated rational quadratic (RQ) kernel high-order unscented Kalman filtering (IRQ-HUKF) algorithm via capturing the statistics to break through the limitations of the Gaussian assumption is proposed. Firstly, the characteristic analysis of the RQ kernel is investigated in detail, which is the first attempt to carry out an exploration of the heavy-tailed characteristic and the ability on capturing high-order moments of the RQ kernel. Subsequently, the RQ kernel method is first introduced into the UKF algorithm as an error optimization criterion, termed the iterated RQ kernel-UKF (RQ-UKF) algorithm by derived analytically, which not only retains the high-order moments propagation process but also enhances the approximation capacity in the non-Gaussian noise problem for its ability in capturing high-order moments and heavy-tailed characteristics. Meanwhile, to tackle the limitations of the Gaussian distribution assumption in the linearization process of the non-linear systems, the high-order Sigma Points (SP) as a subsidiary role in propagating the state high-order statistics is devised by the moments matching method to improve the RQ-UKF. Finally, to further improve the flexibility of the IRQ-HUKF algorithm in practical application, an adaptive kernel parameter is derived analytically grounded in the Kullback-Leibler divergence (KLD) method and parametric sensitivity analysis of the RQ kernel. The simulation results demonstrate that the novel IRQ-HUKF algorithm is more robust and outperforms the existing advanced UKF with respect to the kernel method in reentry vehicle tracking scenarios under various noise environments.

9D Rotation Representation-SVD Fusion with Deep Learning for Unconstrained Head Pose Estimation

Abstract Accurately estimating human head pose poses a significant challenge across various application domains. To address the inherent limitations of previous approaches, this research proposes an unconstrained head pose estimation strategy. The method combines deep learning with rotation matrices, utilizing nine-dimensional vectors output by the neural network, which are projected back to rotation matrices in SO (3) space through singular value decomposition. This ensures both the smoothness and uniqueness of the rotation representation. The approach demonstrates distinct advantages in handling the rotation estimation task, particularly when the rotated representation is used as the model output. It not only avoids the discontinuity and double-coverage issues associated with prior methods but also enhances the stability of the representation in high-dimensional space, thereby improving the learning process. Additionally, the geodesic loss function is incorporated to train the network. The proposed strategy surpasses previous state-of-the-art methods, as evidenced by experiments conducted on the AFLW2000 and BIWI datasets.

Can we estimate which colors our participants see? Comparing results from different gamma correction methods

In experimental color research, one must ensure that color is displayed and described reliably. When monitors are involved, colors are displayed through device-dependent color systems. However, these values must be translated into device-independent color systems to interpret what people perceive, often involving techniques such as gamma correction. We sought to explore the feasibility of estimating gamma instead of relying on direct gamma measurements, which typically require specialized equipment like a chromameter. Potential solutions include a computerized perception-based gamma estimation task or adopting the industry-standard gamma value of 2.2. We compared these two solutions against the chromameter measurements in the context of a color-matching task. Thirty-nine participants visually matched red, yellow, green, and blue physical objects using a computerized color picker. Starting from these color choices, we applied two RGB-to-CIE Lab color conversion methods: one using a perception-based gamma estimation and another using the industry-standard gamma. Color values obtained with the chromameter differed from the other two methods by 6–15 JNDs. Small differences existed between the results obtained using the perception-based task and the industry-standard gamma. Thus, we conclude that when standard viewing conditions cannot be assumed, adopting a gamma value of 2.2 should suffice.

CRefNet: Learning Consistent Reflectance Estimation With a Decoder-Sharing Transformer.

We present CRefNet, a hybrid transformer-convolutional deep neural network for consistent reflectance estimation in intrinsic image decomposition. Estimating consistent reflectance is particularly challenging when the same material appears differently due to changes in illumination. Our method achieves enhanced global reflectance consistency via a novel transformer module that converts image features to reflectance features. At the same time, this module also exploits long-range data interactions. We introduce reflectance reconstruction as a novel auxiliary task that shares a common decoder with the reflectance estimation task, and which substantially improves the quality of reconstructed reflectance maps. Finally, we improve local reflectance consistency via a new rectified gradient filter that effectively suppresses small variations in predictions without any overhead at inference time. Our experiments show that our contributions enable CRefNet to predict highly consistent reflectance maps and to outperform the state of the art by 10% WHDR.

Affective and cognitive factors associated with Chinese and Italian children’s arithmetic performance

BackgroundThis study aimed to investigate the cognitive and affective factors associated with cross-cultural differences in arithmetic tasks.MethodsA total of 404 third- and fourth- graders were recruited from China and Italy to complete exact arithmetic, arithmetic estimation and cognitive tasks (i.e., short-term memory, executive functions, and fluid reasoning). Their mathematical anxiety was also measured.ResultsThe results showed that Chinese children performed better than Italian children in both arithmetic tasks and in shifting task. Italian children performed better in visuospatial updating task and reported higher levels of mathematical anxiety than their Chinese peers. Multi-group path analyses showed that the patterns of relations among cognitive factors (i.e., short-term memory, inhibition and shifting), mathematical anxiety, and arithmetic performance were similar across groups. The only exception was that visuospatial updating uniquely predicted arithmetic estimation for Chinese but not for Italian children.ConclusionsChinese children outperformed their Italian peers in the exact arithmetic task, likely due to the greater emphasis on arithmetic fluency in Chinese mathematics education, both in schools and at home. They also had a slight advantage than Italian peers in the arithmetic estimation task. The unique link between updating and arithmetic estimation found in Chinese children but not Italian children suggests that, although arithmetic estimation is not emphasized in the curricula of either country, instruction and practice in exact arithmetic may enhance Chinese children’s efficiency in solving arithmetic estimation problems.

Enhancing pressure transient analysis in reservoir characterization through deep learning neural networks

Deep learning neural networks exhibit remarkable efficiency in numerous tasks within machine learning applications across various disciplines, including reservoir engineering. Traditional pressure transient analysis (PTA) methodologies, however, are challenged by limitations such as human error in accurately estimating critical reservoir parameters like permeability and wellbore damage (skin). In this study, we evaluate the efficiency and accuracy of various machine learning models in pressure transient analysis (PTA) tasks. Our investigation initially focused on integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance the precision in estimating reservoir parameters and identifying reservoir models. However, through comprehensive testing and validation using simulated well-test data and out-of-distribution samples, we discovered that tree-based models, specifically XGBoost and Random Forest, exhibit superior performance, particularly in generalization tasks. These models outperform CNN-LSTM and 1D-CNN models, demonstrating enhanced robustness and accuracy in handling diverse and unseen data, thereby addressing traditional challenges in pressure transient analysis more effectively. Moreover, Savitzky–Golay Filters were used to handle the noise problems of data, and this method proved effective for data with noise levels up to a standard deviation of 2. Compared with other models, the XGBoost model has a better performance in reservoir model identification with an out-of-distribution accuracy of 92%. On the other hand, Random Forest showed a remarkable result in the task of reservoir parameter estimation, especially for the permeability and skin with out-of-distribution R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 98% and 97%, respectively. The findings of this study can help for a better understanding of the different deep learning models and their applications in pressure transient analysis. Specifically, the superior performance of the tree-based models in overcoming traditional pressure transient analysis challenges demonstrates their potential to enhance reservoir characterization predictions. Employing these advanced machine learning techniques can significantly reduce human error, increase the accuracy of parameter estimation, and improve the analysis process, thereby aiding strategic decision-making in reservoir management

POLO: Pose estimation with one-stage location-sensitive coding

The precise monitoring of fish keypoints and behavioral patterns is crucial in fish farming, influencing decisions on feeding schedules and assessing fish health. Traditional approaches to multi-object pose estimation often lean towards either Bottom-up or Top-down methods. Our innovative solution introduces a streamlined single-stage multi-object pose estimation framework, utilizing a unique approach to instance assignment based on location. By incorporating position encoding as an index for candidate pose heatmap groups, we achieve end-to-end multi-object pose estimation with reduced redundancy through non-maximum suppression. Our framework, named POLO, has been validated on a meticulously annotated fish keypoint dataset, demonstrating outstanding performance with a remarkable 65.34% OKS AP at 71.4 FPS on Tesla v100. With its real-time capabilities, POLO is highly adaptable, making it suitable for deployment on various edge computing devices and addressing real-world challenges effectively. We believe our framework can serve as a solid baseline for diverse pose estimation tasks across different domains.