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In this paper, a high dynamic range (HDR) imaging method based on the stereo vision system is presented. The proposed method uses differently exposed low dynamic range (LDR) images captured from a stereo camera. The stereo LDR images are first converted to initial stereo HDR images using the inverse camera response function estimated from the LDR images. However, due to the limited dynamic range of the stereo LDR camera, the radiance values in under/over-exposed regions of the initial main-view (MV) HDR image can be lost. To restore these radiance values, the proposed stereo matching and hole-filling algorithms are applied to the stereo HDR images. Specifically, the auxiliary-view (AV) HDR image is warped by using the estimated disparity between initial the stereo HDR images and then effective hole-filling is applied to the warped AV HDR image. To reconstruct the final MV HDR, the warped and hole-filled AV HDR image is fused with the initial MV HDR image using the weight map. The experimental results demonstrate objectively and subjectively that the proposed stereo HDR imaging method provides better performance compared to the conventional method.

Currently, most wave observation equipment is used for fixed-point measurements, and there is a relative scarcity of ship-borne real-time wave measurement devices, which limits comprehensive and three-dimensional monitoring of wave characteristics. This paper introduces the Wave Acquisition Stereo System (WASS) and describes the design and construction of a ship-borne stereoscopic vision experimental apparatus. Sea trials were conducted to evaluate the system’s ship-borne wave-measurement performance and to quantify the effect of deployment parameters on accuracy. The results indicate that the device reliably retrieves wave parameters; compared with concurrent buoy observations, the error in significant wave height did not exceed 0.14 m. Research confirms that deployment parameters have a significant impact on measurement outcomes: sampling frequency directly affects the accuracy of wave-parameter estimation; a higher sampling rate (10 Hz) improves the reliability of the calculated results. The baseline-to-height ratio has an optimal range (0.1–0.3), and values outside this interval reduce measurement accuracy. Under a fixed geometric configuration, the observation field exhibits a band-shaped low-error zone aligned with the baseline direction.

Soundscape is the perception of an acoustic environment. This perception includes thoughts and feelings of the human being owing to the environment interaction, and physical properties of such environment. Soundscape recordings involve the human perception usually captured by questionnaires. However, human perception is inherently subjective, and questionnaires are tools to gather information with several bias (e.g., design, response, sampling, event recall, and management). To move towards the study of the soundscape effects in terms of not only involving questionnaire-based information, but also the neurophysiological reaction, this works aims to provide a database of 30 individuals, who experienced four types of soundscapes in Monterrey, N. L., Mexico (ecological park, riverwalk, music avenue, and traffic) in two audio formats (stereo and binaural). The sense of being in the auditory environment was gathered by applying the Usoh and Steed questionnaire and recording the electroencephalographic activity (brain electrical reaction) of the individuals. This database could be useful to study stress level and cognitive load induced by traffic, to measure whether ecological park or riverwalk reduce stress, to evaluate the engagement and excitedness level arisen in music venues, and to improve user-experiences in immersive environments such virtual reality and augmented reality.

Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.

The stereo correspondence problem exists because false matches between the images from multiple sensors camouflage the true (veridical) matches. True matches are correspondences between image points that have the same generative source; false matches are correspondences between similar image points that have different sources. This problem of selecting true matches among false ones must be overcome by both biological and artificial stereo systems in order for them to be useful depth sensors. The proposed re-examination of this fundamental issue shows that false matches form a symmetrical pattern in the array of all possible matches, with true matches forming the axis of symmetry. The patterning of false matches can therefore be used to locate true matches and derive the depth profile of the surface that gave rise to them. This reverses the traditional strategy, which treats false matches as noise. The new approach is particularly well-suited to extract the 3-D locations and shapes of camouflaged surfaces and to work in scenes characterized by high degrees of clutter. We demonstrate that the symmetry of false-match signals can be exploited to identify surfaces in random-dot stereograms. This strategy permits novel depth computations for target detection, localization, and identification by machine-vision systems, accounts for physiological and psychophysical findings that are otherwise puzzling and makes possible new ways for combining stereo and motion signals.

Background The skin surface becomes wrinkled and rough due to various internal and external factors. A three‐dimensional (3D) analysis of the skin is required to improve skin conditions. Stereophotogrammetry, a noninvasive 3D analysis method, is easy to install and use, but most stereo systems have a fixed baseline and scale. Previous stereo systems are not suitable for observing micro‐range skin features. Therefore, we suggest the optimal conditions and methods for the 3D analysis of skin microrelief using a multi‐conditioned stereo system. Methods We constructed a nonconvergence model using a mobile device and acquired stereo images under multiscale and multi‐baseline conditions. We extracted 3D information of the skin through our process: preprocessing, skin feature extraction, feature matching, and actual depth mapping. We improved the accuracy of the 3D analysis of the skin by using disparity values instead of disparity maps. We compared and analyzed the performances of six local feature detector and descriptor algorithms. In addition, we suggested depth‐mapping formulas to estimate the actual depth of the skin microrelief. Results We confirmed that stereo images with a working distance of 70–75 mm and a baseline of 4–8 mm are effective for the 3D analysis of skin microrelief. In addition, accelerated KAZE exhibited the best performance for features extraction and stereo matching. Finally, the extracted 3D information was converted to the actual depth, and the performance of the 3D analysis was verified. Conclusion The proposed system and method that provide texture information are effective for 3D skin disease analysis and evaluation.

Underwater caves represent the most challenging scenario for exploration, mapping and 3D modelling. In such complex environment, unsuitable to humans, highly specialized skills and expensive equipment are normally required. Technological progress and scientific innovation attempt, nowadays, to develop safer and more automatic approaches for the virtualization of these complex and not easily accessible environments, which constitute a unique natural, biological and cultural heritage. This paper presents a pilot study realised for the virtualization of 'Grotta Giusti' (Fig. 1), an underground semi-submerged cave system in central Italy. After an introduction on the virtualization process in the cultural heritage domain and a review of techniques and experiences for the virtualization of underground and submerged environments, the paper will focus on the employed virtualization techniques. In particular, the developed approach to simultaneously survey the semi-submersed areas of the cave relying on a stereo camera system and the virtualization of the virtual cave will be discussed.

Many consumer products deliver their utility over time, and the decision to purchase such products often depends on predictions of future product enjoyment. The present research shows that consumers often fail to predict hedonic adaptation to products and explores the antecedents and consequences of this misprediction. We demonstrate that the failure to predict diminishing enjoyment with a product arises because of a failure to spontaneously consider adaptation and apply correct intuitive beliefs about adaptation. We further show that making prospective duration salient can cue beliefs about hedonic adaptation. Finally, we find that these beliefs, once cued, influence choices.

This paper shows a new low-cost technology for the measurement of crack propagation in quasi-fragile materials based on a stereo pair of cameras and LED light spots. The two cameras record the displacement experienced by a series of LED white lights. For each frame, the X , Y and Z 3D coordinates of all the centroids of the LED points are obtained. From this information, it is possible to determine the variation of the distance between any two of them. In this case, 2 strips of 12 LED lights each were arranged in such a way that the points of both strips coincided in pairs in height. The algorithm made it possible to monitor the increase in distance that occurred between each pair of lights at the same height. The paper shows the mathematical basis of this technological solution. A test has been carried out by installing this system in a concrete cube 150 mm side and subjected to a wedge-splitting test. The results show that it is possible to monitor the crack propagation (position of the crack front) during the test and to know the crack width too. At present, the accuracy of this technique is only limited by the camera resolution and the computer processing capability.

The belief propagation (BP) algorithm has some limitations, including ambiguous edges and textureless regions, and slow convergence speed. To address these problems, we present a novel algorithm that intrinsically improves both the accuracy and the convergence speed of BP. First, traditional BP generally consumes time due to numerous iterations. To reduce the number of iterations, inspired by the crucial importance of the initial value in nonlinear problems, a novel initial-value belief propagation (IVBP) algorithm is presented, which can greatly improve both convergence speed and accuracy. Second, .the majority of the existing research on BP concentrates on the smoothness term or other energy terms, neglecting the significance of the data term. In this study, a self-adapting dissimilarity data term (SDDT) is presented to improve the accuracy of the data term, which incorporates an additional gradient-based measure into the traditional data term, with the weight determined by the robust measure-based control function. Finally, this study explores the effective combination of local methods and global methods. The experimental results have demonstrated that our method performs well compared with the state-of-the-art BP and simultaneously holds better edge-preserving smoothing effects with fast convergence speed in the Middlebury and new 2014 Middlebury datasets.