Abstract Computer vision and sensor technology advancements have enabled significant progress in 3D reconstruction. However, critical challenges remain when extending these methods to underwater environments. This paper addresses the disparities between terrestrial and underwater 3D reconstruction by presenting a novel comparative framework that evaluates identical objects in these distinct settings under real-world conditions. To mitigate underwater-specific challenges such as light absorption, scattering, and reduced image clarity, a new enhancement methodology combining RGB stretching and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed. Experimental results demonstrate that the enhancement increased processing time by 2.99% compared to the underwater model, while improving reconstructed points by 5.05%, detected features by 3.12%, and reconstructed features by 6.58%. Furthermore, structural metrics such as smoothness, genus connectivity, and average skewness showed significant positive improvements. Furthermore, structural metrics showed significant improvements with genus connectivity increasing by 1.29%, smoothness improving by 0.69%, and average skewness increasing by 0.18%, based on the raw underwater model and underwater enhanced model. By providing a direct cross-environment comparison, this study bridges the gap between terrestrial and underwater 3D reconstruction, offering critical insights into methodological adaptations necessary for robust and reliable modeling in diverse environmental conditions. The proposed framework lays the foundation for advancing underwater 3D reconstruction technology to achieve parity with its terrestrial counterpart, with applications spanning cultural heritage preservation, marine exploration, and environmental monitoring.

Abstract AR applications are rapidly gaining adoption, expanding users’ perceptual capabilities. Their potential for addressing visual occlusions and effectively extending the user’s line of sight significantly enhances their value in various contexts, from professional to personal use. This work presents a novel system designed to project 3D human poses onto AR glasses, enabling users to perceive concealed individuals behind solid objects, addressing a critical limitation of traditional visual perception. To achieve real-time and accurate 3D projection, we employ fiducial markers strategically placed within the environment. The markers are periodically fused with IMU sensor data to accurately estimate the user’s head orientation, a crucial step for correct spatial alignment. Furthermore, we leverage a multi-view 3D human pose estimation method using calibrated cameras and incorporate attention mechanisms. These mechanisms focus the system on relevant features, improving accuracy and minimizing 3D joint error. Our experiments demonstrate that the proposed framework accurately projects 3D skeletal representations onto AR glasses, even when significant occlusions are caused by solid objects or other occupants within the scene. This novel approach offers a method to enhance situational awareness in dynamic environments where visibility is compromised, potentially benefiting various applications, from first response scenarios to security and surveillance.

This work addresses the problem of 3D human pose and shape estimation from monocular video. While significant progress has been made in this domain, most existing approaches focus solely on forward human motion and neglect the reverse process. To overcome this oversight, we propose dual stream networks consisting of decoders that analyze both forward and reverse human motion. The network outputs are fused using residual connectors, which effectively integrate the predictions. Exploiting temporal transformers further enhance ac-curacy. Our method achieves state-of-the-art results on well-established datasets in terms of both 3D error and temporal consistency metrics, demonstrating its robustness and effectiveness. Furthermore, our network operates at over 150 FPS, making it suitable for real-time inference.

Abstract Human pose estimation (HPE) is a computer vision application that estimates human body joints from images. It gives machines the capability to better understand the interaction between humans and the environment. For this accomplishment, many HPE methods have been deployed in robots, vehicles, and unmanned aerial vehicles (UAVs). This effort raised the challenge of balance between algorithm performance and efficiency, especially in UAVs, where computational resources are limited for saving battery power. Despite the considerable progress in the HPE problem, there are very few methods that are proposed to face this challenge. To highlight the severity of this fact, the proposed paper presents a brief review and an HPE benchmark from the aspect of algorithms performance and efficiency under UAV operation. More specifically, the contribution of HPE methods in the last 22 years is covered, along with the variety of methods that exist. The benchmark consists of 36 pose estimation models in 3 known datasets with metrics that fulfill the paper aspect. From the results, MobileNet-based models achieved competitive performance and the lowest computational cost, in comparison with ResNet-based models. Finally, benchmark results are projected in edge devices hardware specifications to analyze the appropriateness of these algorithms for UAV deployment.

This work concerns a gait matching problem. An experimental simulation of people walking in an interior area, some of which had wearables with accelerometer and gyroscope, and some of which were recorded by some camera set up in this area, was conducted. From all devices data from single-point vectors were acquired, thus preserving privacy. The aim was to match each person carrying a wearable with the correct camera recording, if this recording existed, or not to match the person with any camera recording, if it was not recorded by a camera for sufficient time. For this matching, deliberately chosen pairs of wearable and camera features were correlated after state-of-the-art-based automatic synchronization, and the correlation matrices resulting from the feature pairs, after rationally transformed to improve results, were fused with the best linear combination. From the experiments, perfect matching results were obtained when the time series to be compared lasted at least 50sec, no matter any other assumption.

Knowledge modelling at industrial level consists an importunate activity nowadays due to the ceaseless advances in technologies and standards applied as well as the extensive amount of unrelated real-time and historical data at shop-floor level. A Common Interface Data Exchange Model (CIDEM) is hereby introduced towards unifying continuously produced data from heterogeneous and distributed information sources - on different levels and granularities - into a shared vocabulary that can unobtrusively communicate with industrial standards and protocols (i.e. B2MML, gbXML, MIMOSA). For further enhancing the information model a conceptual definition is employed leading to a semantically enriched model which enables more understandable high level knowledge diffusion. This model has been applied to various industrial applications, one of the most important being industrial safety, through incident recognition.

In this work, a modified version of a Slope Statistic Profile (SSP) method is proposed, capable to detect real-time incidents that occur in two interdependent time series. The estimation of incident time point is based on the combination of their linear trend profiles test statistics, computed on a consecutive overlapping data window. Furthermore, the proposed method uses a self-adaptive sliding data window. The adaptation of the size of the sliding data window is based on real-time classification of the linear trend profiles in constant and equal time intervals, according to two different linear trend scenarios, suitably adjusted to the conditions of the problem we face. The proposed method is used for the robust identification of a malfunction and it is demonstrated to real datasets from a chemical process pilot plant that is situated at the premises of CERTH / CPERI during the evolution of the performed experiments at the process unit.

A novel big data building performance evaluation knowledge processing and mining system utilizing visual analytics is going to be presented in this paper. A large dataset comprised of building information, energy consumption, environmental measurements, human presence and behavior and business processes is going to be exploited for the building performance evaluation. Building performance evaluation is one of the most important factors in engineering that leads to building renovation and construction with low energy consumption and gas emissions in conjunction with comfort, utility and durability. For this purpose, business processes occurring in the building are correlated with the energy consumption and the human flows in the spatiotemporal domain modeling the dynamic behavior of the building. These models lead to the extraction of useful semantic information and the detection of spatiotemporal patterns that are important for the evaluation of the building performance. Furthermore, a number of novel visual analytics techniques allow the end-users to process data in different temporal resolutions and with different temporal filters, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed visual analytics techniques support design and energy management decisions by visualizing the building measurements regarding business and comfort aspects. To do so, the proposed system includes a variety of techniques and components, properly selected to offer quick identification of focal points and evaluation of the building performance. Considering the increasing interest and the green building goals of almost all world governments including EU, the suggested methodology and application could be rendered a very useful tool for the Architecture and Engineering Community working on Building Performance Simulation and Analysis, and all related communities in Architect, Engineering and Construction (AEC) industry.

In this work, a novel, multi-space, real-time and robust human tracking system is going to be presented. The system exploits a multi-camera network monitoring the multi-space dynamic environment under interest, detecting and tracking the humans in it. The system is able to handle the dynamic changes of the environment, as well as partial occlusions utilizing virtual top cameras. Furthermore, the system is able to real-time visualize the detection and tracking results on the architectural map of the dynamic environment, as well as a variety of statistics. The visual spatio-temporal analysis of the tracked data are presented in a consolidated form for the overall monitoring area and analytically for each space separately and for each tracked human. These statistics could be also combined with the energy consumption in the area, as well as with other environmental data providing semantic information such as comfort. The overall system is equipped with a number of visual interactive tools providing real-time spatio-temporal human presence analysis offering to the user the opportunity to capture and isolate the areas/spaces with high human presence, the days and times of high human presence, to correlate this information with the potential energy consumption and indicators such as comfort.

The performance of a number of image processing methods depends on the output quality of a distance transformation (DT) process. Most of the fast DT methodologies are not accurate, whereas other error-free DT algorithms are not very fast. In this paper, a novel, fast, simple, and error-free DT algorithm is presented. By recording the relative x- and y-coordinates of the examined image pixels, an optimal algorithm can be developed to achieve the DT of an image correctly and efficiently in constant time without any iteration. Furthermore, the proposed method is general since it can be used by any kind of distance function, leading to accurate image DTs.

This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.

This paper presents a novel approach for object skeleton family extraction. The introduced technique utilizes a 2-D physics-based deformable model that parameterizes the objects shape. Deformation equations are solved exploiting modal analysis, and proportional to model physical characteristics, a different skeleton is produced every time, generating, in this way, a family of skeletons. The theoretical properties and the experiments presented demonstrate that obtained skeletons match to hand-labeled skeletons provided by human subjects, even in the presence of significant noise and shape variations, cuts and tears, and have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches without the need of any known skeleton pruning method.

This paper presents a novel fast model for active contours to detect objects in an image, based on techniques of curve evolution. The proposed model can detect objects whose boundaries are not necessarily defined by gradient, based on the minimization of a fuzzy energy, which can be seen as a particular case of a minimal partition problem. This fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary. However, the stopping term does not depend on the gradient of the image, as most of the classical active contours, but instead is related to the image color and spatial segments. The fuzziness of the energy provides a balanced technique with a strong ability to reject "weak" local minima. Moreover, this approach converges to the desired object boundary very fast, since it does not solve the Euler-Lagrange equations of the underlying problem, but, instead, calculates the fuzzy energy alterations directly. The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies.

This paper addresses the issue of accurate, effective, computationally efficient, fast, and fully automated 2-D object orientation and scaling factor estimation. The object orientation is calculated using object principal axes estimation. The approach relies on the object's frequency-based features. The frequency-based features used by the proposed technique are extracted by a 2-D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations.

A major disadvantage of image retrieval systems is their lack of objects orientation and scaling matching. This paper addresses the issue of accurate, effective, computationally efficient, fast and fully-automated 2D object orientation and scaling determination. The approach relies on the objects frequency-based features. The frequency-based features used by the proposed technique, are extracted by a 2D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations

This paper presents an accurate, very fast approach for the deformations of two-dimensional physically based shape models representing open and closed curves. The introduced models are much faster than other deformable models (e.g., finite-element methods). The approach relies on the determination of explicit deformation governing equations that involve neither eigenvalue decomposition, nor any other computationally intensive numerical operation. The approach was evaluated and compared with another fast and accurate physics-based deformable shape odel, both in terms of deformation accuracy and computation time. The conclusion is that the introduced model is completely accurate and is deformed very fast on current personal computers (Pentium III), achieving more than 380 contour deformations per second.

An accurate, computationally efficient, and fully automated algorithm for the alignment of two-dimensional (2-D) serially acquired sections forming a three-dimensional (3-D) volume is presented. The approach relies on the optimization of a global energy function, based on the object shape, measuring the similarity between a slice and its neighborhood in the 3-D volume. Slice similarity is computed using the distance transform measure in both directions. No particular direction is privileged in the method avoiding global offsets, biases in the estimation and error propagation. The method was evaluated on real images [medical, biological, and other computerized tomography (CT) scanned 3-D data] and the experimental results demonstrated its accuracy as reconstuction errors are less than one degree in rotation and less than one pixel in translation.

This paper presents an accurate, computationally efficient, fast and fully-automated algorithm for the alignment of 2D serially acquired sections forming a 3D volume. The method accounts for the main shortcomings of 3D image alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices and missing slices. The approach relies on the determination of inter-slice correspondences. The features used for correspondence are extracted by a 2D physics-based deformable model parameterizing the object shape. Correspondence affinities and global constraints render the method efficient and reliable. The method has been evaluated on real images and the experimental results demonstrate its accuracy, as reconstruction errors are smaller than 1 degree in rotation and smaller than 1 pixel in translation.