Explore the vast range of titles available.

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 seminal multiple-view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis (MVS) methodology. The somewhat small size and variability of these data sets, however, limit their scope and the conclusions that can be derived from them. To facilitate further development within MVS, we here present a new and varied data set consisting of 80 scenes, seen from 49 or 64 accurate camera positions. This is accompanied by accurate structured light scans for reference and evaluation. In addition all images are taken under seven different lighting conditions. As a benchmark and to validate the use of our data set for obtaining reasonable and statistically significant findings about MVS, we have applied the three state-of-the-art MVS algorithms by Campbell et al., Furukawa et al., and Tola et al. to the data set. To do this we have extended the evaluation protocol from the Middlebury evaluation, necessitated by the more complex geometry of some of our scenes. The data set and accompanying evaluation framework are made freely available online. Based on this evaluation, we are able to observe several characteristics of state-of-the-art MVS, e.g. that there is a tradeoff between the quality of the reconstructed 3D points (accuracy) and how much of an object’s surface is captured (completeness). Also, several issues that we hypothesized would challenge MVS, such as specularities and changing lighting conditions did not pose serious problems. Our study finds that the two most pressing issues for MVS are lack of texture and meshing (forming 3D points into closed triangulated surfaces).

At some point most Black and Latino/a college students — even long-term high achievers — question their own abilities because of multiple forms of racial bias. The 38 high-achieving Black and Latino/a STEM study participants, who attended institutions with racially hostile academic spaces, deployed an arsenal of strategies (e.g., stereotype management) to deflect stereotyping and other racial assaults (e.g., racial microaggressions), which are particularly prevalent in STEM fields. These students rely heavily on coping strategies that alter their authentic racial identities but create internal turmoil. Institutions of higher education, including minority-serving schools, need to examine institutional racism and other structural barriers that damage the racial identities of Black and Latino/a students in STEM and cause lasting psychological strain.

This paper presents a novel concept of real-time catadioptric stereo tracking using a single ultrafast mirror-drive pan-tilt active vision system that can simultaneously switch between hundreds of different views in a second. By accelerating video-shooting, computation, and actuation at the millisecond-granularity level for time-division multithreaded processing in ultrafast gaze control, the active vision system can function virtually as two or more tracking cameras with different views. It enables a single active vision system to act as virtual left and right pan-tilt cameras that can simultaneously shoot a pair of stereo images for the same object to be observed at arbitrary viewpoints by switching the direction of the mirrors of the active vision system frame by frame. We developed a monocular galvano-mirror-based stereo tracking system that can switch between 500 different views in a second, and it functions as a catadioptric active stereo with left and right pan-tilt tracking cameras that can virtually capture 8-bit color 512 × 512 images each operating at 250 fps to mechanically track a fast-moving object with a sufficient parallax for accurate 3D measurement. Several tracking experiments for moving objects in 3D space are described to demonstrate the performance of our monocular stereo tracking system.

Multi-object tracking faces persistent challenges from occlusions and truncations in monocular vision systems. While stereo vision provides depth information, existing approaches require computationally expensive dense matching or 3D reconstruction. This paper presents a real-time 2.5D stereo multi-object tracking framework combining lightweight stereo matching with resilient tracker management. The stereo matching module employs Direct Linear Transform-based triangulation using only bounding box coordinates, eliminating costly feature extraction while maintaining robust correspondence through geometric constraints. A dual-tracker architecture maintains independent trackers in both views, enabling re-identification when objects become occluded in one view but remain visible in the other. Experimental validation on a refrigerator monitoring dataset demonstrates that StereoSORT achieves a multiple object tracking accuracy (MOTA) of 0.932 and an identification F1 score (IDF1) of 0.823, substantially outperforming monocular trackers, including OC-SORT (IDF1: 0.765) and ByteTrack (IDF1: 0.609). The system achieves a 50.1 mm median depth error, comparable to commercial sensors, while maintaining 70 FPS on standard hardware. These results validate that geometric constraints alone enable robust stereo tracking without appearance features, offering a practical solution for resource-constrained environments where computational efficiency and tracking reliability are equally critical.

Deep learning-based stereo matching methods have made remarkable progress in recent years. However, it is still a challenging task to achieve high accuracy in real time. In response to this challenge, we propose a Spatial Attention-Guided Upsampling network (SAGU-Net) for accurate and real-time stereo matching. First, a Spatial Attention-Guided Cost Volume Upsampling (SAG-CVU) module is proposed for upsampling the low-resolution cost volume, which calculates each upsampled matching cost as the sum of neighboring coarse costs under the guidance of spatial attention. Different from the recently popular coarse-to-fine (CTF) strategy that prefers upsampling the coarse disparity map, the low-resolution cost volume is upsampled by the SAG-CVU module which allows more raw information to propagate to subsequent procedures and can alleviate the problem of losing high-frequency information. To ensure fast running speed, a medium-resolution disparity map is directly regressed from the upsampled cost volume and then upsampled to full resolution with a Spatial Attention-Guided Disparity Map Upsampling (SAG-DMU) module. Unlike most CTF-based methods which usually build and aggregate narrow cost volumes iteratively until a full-resolution disparity map is obtained, the SAG-DMU module helps the proposed network avoid the iterative procedure to ensure fast running speed. In addition, we propose a simple yet effective gradient loss function that plays the role of a discontinuity-preserving regularizer, which further improves the overall accuracy, especially at depth discontinuities. These design choices lead to the proposed SAGU-Net which can obtain accurate results in real time. Extensive experimental results demonstrate that SAGU-Net and its variants outperform not only state-of-the-art real-time networks but also many accuracy-oriented models on multiple datasets.

A photometric stereo needs three images taken under three different light directions lit one by one, while a color photometric stereo needs only one image taken under three different lights lit at the same time with different light directions and different colors. As a result, a color photometric stereo can obtain the surface normal of a dynamically moving object from a single image. However, the conventional color photometric stereo cannot estimate a multicolored object due to the colored illumination. This paper uses an example-based photometric stereo to solve the problem of the color photometric stereo. The example-based photometric stereo searches the surface normal from the database of the images of known shapes. Color photometric stereos suffer from mathematical difficulty, and they add many assumptions and constraints; however, the example-based photometric stereo is free from such mathematical problems. The process of our method is pixelwise; thus, the estimated surface normal is not oversmoothed, unlike existing methods that use smoothness constraints. To demonstrate the effectiveness of this study, a measurement device that can realize the multispectral photometric stereo method with sixteen colors is employed instead of the classic color photometric stereo method with three colors.

Rain is a common natural phenomenon. Taking images in the rain however often results in degraded quality of images, thus compromises the performance of many computer vision systems. Most existing de-rain algorithms use only one single input image and aim to recover a clean image. Few work has exploited stereo images. Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignore semantic information. In this paper, we present a Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information. Specifically, we develop a Semantic-Aware Deraining Module (SADM) which solves both tasks of semantic segmentation and deraining of scenes, and a Semantic-Fusion Network (SFNet) and a View-Fusion Network (VFNet) which fuse semantic information and multi-view information respectively. In addition, we also introduce an Enhanced Paired Rain Removal Network (EPRRNet) which exploits semantic prior to remove rain streaks from stereo images. We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image. Finally, a parallel stereo deraining network fuses semantic and multi-view information to restore finer results. We also propose new stereo based rainy datasets for benchmarking. Experiments on both monocular and the newly proposed stereo rainy datasets demonstrate that the proposed method achieves the state-of-the-art performance. https://github.com/HDCVLab/Stereo-Image-Deraining.

Men are vastly underrepresented in early childhood education and care, particularly in childcare work. To uncover stereotypes that motivate or hinder support for men in childcare in society, we employed a representative sample ( N  = 280) from Czechia, which has one of the lowest percentages of men working in childcare in the EU. We identified and contrasted descriptive, prescriptive, and proscriptive stereotypes about men, women, or childcare workers without a specified gender. Next, we examined the link between convergence of descriptive and prescriptive stereotypes about men in childcare and support for men working in childcare. In both open responses and trait ratings, men working in childcare were less often perceived or expected to be warm than women working in childcare. In the trait ratings, men working in childcare were less often expected to be moral and competent than women working in childcare. Yet, the overall stereotypical profiles of men converged with childcare workers with no gender information. Greater convergence between descriptive and prescriptive stereotypes about men working in childcare was associated with higher support for them. These findings highlight the specific role that normative beliefs play in support for men in childcare in the larger social environment.

In this paper, we propose a multi-view stereo matching method, EnSoft3D (Enhanced Soft 3D Reconstruction) to obtain dense and high-quality depth images. Multi-view stereo is one of the high-interest research areas and has wide applications. Motivated by the Soft3D reconstruction method, we introduce a new multi-view stereo matching scheme. The original Soft3D method is introduced for novel view synthesis, while occlusion-aware depth is also reconstructed by integrating the matching costs of the Plane Sweep Stereo (PSS) and soft visibility volumes. However, the Soft3D method has an inherent limitation because the erroneous PSS matching costs are not updated. To overcome this limitation, the proposed scheme introduces an update process of the PSS matching costs. From the object surface consensus volume, an inverse consensus kernel is derived, and the PSS matching costs are iteratively updated using the kernel. The proposed EnSoft3D method reconstructs a highly accurate 3D depth image because both the multi-view matching cost and soft visibility are updated simultaneously. The performance of the proposed method is evaluated by using structured and unstructured benchmark datasets. Disparity error is measured to verify 3D reconstruction accuracy, and both PSNR and SSIM are measured to verify the simultaneous enhancement of view synthesis.