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.

We explored how study participants interacted with virtual characters in a virtual reality study. Specifically, we developed a 3 (proximity: close vs. middle vs. far) $\times 2$ (gesture: passive vs. active) experimental design (N = 26) to understand how combinations of proximity between two virtual characters and gestures assigned to them influence study participants’ self-reported ratings (co-presence, attentional allocation, behavioral interdependence, emotional reactivity, and perceived politeness). We also examined their avoidance movements (duration, trajectory length, and speed) and their avoidance decisions (passing through/around and minimum distance side). We collected both survey responses and our participants’ trajectories. Our study revealed that 1) the proximity factor impacted how our participants rated their co-presence and behavioral interdependence, as well as whether they decided to pass through or around the virtual characters, and 2) the gesture factor impacted how participants rated their behavioral interdependence, emotional reactivity, perceived politeness, and also affected their duration, trajectory length, and speed. Our research contributes to understanding personal space and social norms in virtual environments, offering valuable insights for virtual reality developers on the importance of social dynamics in designing interactions with virtual characters.

Abstract The aim of our study was to investigate whether different tactile feedback conditions could affect the behavior of participants who were instructed to walk within a virtual reality environment surrounded by a virtual crowd of people. A road crossing scenario that takes place in a virtual metropolitan city was developed for this study. Participants were asked to walk toward the opposite sidewalk while wearing a tactile vest. At each road crossing, one of several tactile feedback conditions was generated, including No Tactile, Side Tactile, Back Tactile, Front Tactile, Accurate Tactile, and Random Tactile. During the virtual road crossing, the movement of the participants was captured, and movement‐related measurements were extracted and analyzed to evaluate the effects of tactile feedback on the participant's movement behavior. Additional data were collected through the distribution of a questionnaire to consider self‐reported ratings of the experimental conditions. The results revealed that tactile feedback conditions had significant effects on movement behavior, while the participants' ratings also indicated that they were affected by the tactile feedback conditions. We found that when the participants were immersed in a high‐density crowd simulation, they became sensitive to tactile feedback. However, they were not able to distinguish between the accurate feedback and the random feedback.

Abstract We investigated the movement behavior of participants walking within a virtual crowd in an immersive virtual environment. We investigated three different parameters that characterize a moving virtual crowd: density, speed, and direction. An immersive road‐crossing scenario that took place in a virtual metropolitan city was created. In this scenario, the participants were instructed to walk toward the opposite sidewalk. Three measurements (speed, deviation, and trajectory length) were used to evaluate the impact of the parameters assigned to the virtual crowd on the movement behavior of the participants. Significant results were found for both the main and interaction effects. The results suggested that the high density, low speed, and diagonal direction situations associated with the virtual crowd had the greatest impacts on the speed, deviation, and trajectory lengths of participants when they walked in a virtual environment and were surrounded by a moving virtual population.

A virtual reality study was conducted to understand participants' anxiety when immersed in a virtual reality trip with a self-driving car. Participants were placed as passengers in a virtual car, and they were seated in the co-driver seat. Five different conditions were developed and examined. For this experiment, the Anxiety Modality Questionnaire that captures the cognitive anxiety of participants was used. The obtained results indicated that the participants' level of anxiety for the partial awareness of the driver condition is influenced less than expected. Specifically, lower levels of anxiety were found when the driver is either fully or partially aware of the traffic and the behavior of the car, and higher anxiety levels were found when the driver is completely unaware.

The present study investigates users' movement behavior in a virtual environment when they attempted to avoid a virtual character. At each iteration of the experiment, four conditions (Self-Avatar LookAt, No Self-Avatar LookAt, Self-Avatar No LookAt, and No Self-Avatar No LookAt) were applied to examine users' movement behavior based on kinematic measures. During the experiment, 52 participants were asked to walk from a starting position to a target position. A virtual character was placed at the midpoint. Participants were asked to wear a head-mounted display throughout the task, and their locomotion was captured using a motion capture suit. We analyzed the captured trajectories of the participants' routes on four kinematic measures to explore whether the four experimental conditions influenced the paths they took. The results indicated that the Self-Avatar LookAt condition affected the path the participants chose more significantly than the other three conditions in terms of length, duration, and deviation, but not in terms of speed. Overall, the length and duration of the task, as well as the deviation of the trajectory from the straight line, were greater when a self-avatar represented participants. An additional effect on kinematic measures was found in the LookAt (Gaze) conditions. Implications for future research are discussed.

Automated assessment tools for dermoscopy images play an important assistive role mainly due to inter- and intra-observer variations in human interpretation. Prior to classification, a robust registration module is necessary. In this paper we propose a fully automatic method for successful registration (matching) of melanocytic nevi images acquired from digital dermoscope to ensure reliable quantitative comparison of the image later. A modified SIFT algorithm (called ROI-SIFT), followed by RANSAC to identify outliers, is used to localize and match correspondence interest points, that will be used to compute affine transformations for the registration of a dermatological image with the respective follow-up examination image. Experimental evaluations of the registration method are reported for image pairs acquired during follow-up examinations. Finally, a short discussion of scheduled future work is proposed.

In this paper, a new algorithm for vehicle logo recognition on the basis of an enhanced scale-invariant feature transform (SIFT)-based feature-matching scheme is proposed. This algorithm is assessed on a set of 1200 logo images that belong to ten distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1200 images to a training set and a testing set, respectively. It is shown that the enhanced matching approach proposed in this paper boosts the recognition accuracy compared with the standard SIFT-based feature-matching method. The reported results indicate a high recognition rate in vehicle logos and a fast processing time, making it suitable for real-time applications.

In this paper a statistical approach for estimating the evolution of categorized web page populations in web directories is proposed. The proposal is based on the capture-recapture method used in wildlife biological studies and it is modified accordi

License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.

This paper proposes an innovative method of monitoring the ability of Internet search services to maintain new and up-to-date results in their directories. The method is based on capture-recapture experiments performed in real wildlife biological studies. After the necessary modifications needed for conducting similar experiments on the Web, a sixteen-week period of capture-recapture experiments was made over the caches of Google and MSN. During this period the birth as well as the survival rates of Google's and MSN's cached results were estimated based on the Pollock's robust design method. The paper provides the implementation details of the applied model along with its initial assessment. The experimental results showed that this approach can be applied as a method for measuring the ability of Internet search services in terms of maintaining new and up-to-date results in their caches.

In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low.