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Analyzing fish and fish schools behavior can help in studying fish-fish interaction, analyzing characteristics of fish species, studying prey avoidance maneuvers of fish schools, etc. Such analysis requires the estimation of each fish's 3D location, 3D pose, and 3D shape over time. Moreover if we are interested in studying the interaction of fish by injecting visual / acoustic stimuli artificially...
Segmentation is a fundamental problem in image processing. In biomedical applications, for example cell analysis, it is important to recognize cells with certain shape characteristics. Although recent advances in microscopy and in experimental methods have made the culturing and imaging of cells in 3D environments possible, there is a great need for advanced image processing methods capable of handling...
3D point cloud classification is an important task in applications for many areas such as robotics, urban planning and augmented reality. 3D sensors measure a high amount of points in the 3D scene objects' surface at a high collect rate, so robust techniques are needed to process all input data and also deal with some imprecision. A common solution for these tasks is the use of robust features extraction...
3D objects classification and retrieval are a growing research topic with many application in different areas, such as robotics, virtual/augmented reality, medicine and physics. In robotics, 3D objects can serve as robust landmarks for pose estimation and localization, so robust 3D object recognition methods are very important tools in this field. Co-occurrence matrices were used for texture classification...
The 3D object recognition is an important research field in robotics. The real world object classification, retrieval, localization and tracking from images (2D or 3D data) are very useful application of computer vision in many areas, for instance, robotics, industry, education, augmented reality and medicine. Locate and recognize 3D objects in the environment can serve to create robust landmarks...
This study explores the non-parametric estimation of a shape boundary from noisy points in 2D when the sensor characteristics are known. As the underlying shape information is not known, the offered algorithm estimates points on the shape boundary by using the statistics of the subsets of point cloud data. The novel approach proposed in this paper is able to find corner points in a local geometry...
The measurement of residual thyroid tissue after thyroidectomy is crucial for the precise quantification of thyroid cancer treatment. Accurate residual thyroid tissue segmentation from CT images is challenging due to the indistinct tissue boundary. We propose a vote-in & vote-out region propagation model for residual thyroid tissue segmentation which incorporates global and local constraints and...
Pairwise surface rigid registration aims to find the rigid transformation that best register two surfaces represented by point clouds. This work presents a comparison between seven algorithms, with different strategies to tackle rigid registration tasks. We focus on the frame-to-frame problem by using both point clouds and a RGB-D video stream in the experimental results. The former, is considered...
The rapid increase in available protein structure datasets requires new techniques for fast, yet, effective analysis of protein 3D structures. In this work, we propose a structure-based signature for protein families, suitable for rapid analysis of multidomain domain protein structures. Our method is alignment-free, using protein strings as the basic representation. A key novelty is the two-stage...
A near-light perspective shape from shading (SfS) technique applied to endoscopy for 3D visualizations of the gastrointestinal tract regions is presented. By utilizing an extensible reflectance model, we study a robust Huber regularization function based variational SfS model. A balancing parameter is used for weighting the irradiance ad smoothness/regularization terms. Experimental results on different...
By adopting different reconstruction techniques, 3D sensors generate different point clouds of various scales for the same object. This paper presents a new algorithm to align point clouds of different scales. First, shape descriptors based on different neighboring sizes are calculated on each point. Secondly, for each neighboring size, principle components analysis is applied on all descriptors to...
This paper proposes a new pylon detection technique from point cloud data. Two masks are created from the non-ground points that mainly represent trees and power line components. The first mask is the power line mask Mₘ, which contains the power line components and trees and where successive pylons are found connected with wires. The second mask is the pylon mask Mₚ, where successive pylons are found...
Semantic parsing of large-scale 3D point clouds is an important research topic in computer vision and remote sensing fields. Most existing approaches utilize hand-crafted features for each modality independently and combine them in a heuristic manner. They often fail to consider the consistency and complementary information among features adequately, which makes them difficult to capture high-level...
We propose a robust hand pose estimation method by learning hand articulations from depth features and auxiliary modality features. As an additional modality to depth data, we present a function of geometric properties on the surface of the hand described by heat diffusion. The proposed heat distribution descriptor is robust to identify the keypoints on the surface as it incorporates both the local...
Rigid structure-from-motion (RSfM) and non-rigid structure-from-motion (NRSfM) have long been treated in the literature as separate (different) problems. Inspired by a previous work which solved directly for 3D scene structure by factoring the relative camera poses out, we revisit the principle of “maximizing rigidity” in structure-from-motion literature, and develop a unified theory which is applicable...
3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods...
Based on the realization that more than 100 Tokyo children have died after falling from balconies during the last five years, this paper proposes a new system for detecting potentially dangerous situations using red green blue-depth (RGB-D) cameras, and thus aims to help prevent children from falling from balconies. The system, which is based on the results of our investigation into how residents...
Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments. Learning pose-invariant features is one solution, but needs expensively labeled large-scale data and carefully designed feature learning algorithms. In this work, we focus on frontalizing faces in the wild under various head poses, including extreme...
Mixed reality (MR) technologies can virtually change the appearance of real objects in real time without changing the material attributes of the objects and the associated haptic stimuli. In this study, we use MR-based visuo—haptic to investigate the mechanisms by which vision and haptics interact. In contrast to MR, diminished reality (DR) can virtually erase a real object from our sight. Because...
We present a novel, low-cost haptic feedback device for spatial design tasks that provides proprioceptive and tactile feedback. It uses the Manus VR datagloves and a custom VR CAD environment. Here, tactile feedback is provided to the index finger through a vibrating motor, which helps users in identifying points on a grid. This grid allows for alignment during the creation and manipulation of geometric...
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