The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper addresses the heterogeneous data registration problem, which is one of the key features for any scene reconstruction and representation, especially for the underwater environment. In this study, we propose a registration method built around a 2D-to-3D feature-based approach that registers high-resolution side-scan sonar images with bathymetric data (topographic 3D point cloud) obtained...
Mapping of underwater environments is a critical task for a range of activities from monitoring coral reef habitats to surveying submerged archaeological sites. In recent years, optical reconstruction methods developed for terrestrial (in air) applications have increasingly been applied to the underwater environment by the marine science community. However, assumptions such as the brightness constancy...
Artificial Neural Networks are a widely used computing system implemented for a wide variety of tasks and problems. A common application of such networks is classification problems. However, a significant amount of this research focuses on one and two-dimensional information, such as vectorized data and images. There is limited research performed on three-dimensional media such as video clips. This...
The subsequence-matching operation applied to motion capture data searches in long motion sequences to locate their parts that are similar to a query example. An effective and efficient implementation of such operation is valuable to increase reusability and findability of expensively recorded data in the past. This demonstration paper builds on recent advances in the field of motion-data processing...
In this paper, we deal with the most challenging task of recovering the 3D human pose from just a single monocular image, that may be a synthetic image or a real internet image. The retrieval and reconstruction of the articulated 3D pose, both are prerequisites for the analysis of the people in images/videos. We address both tasks together and propose an efficient framework for search & retrieval...
Current motion-capture technologies produce continuous streams of 3D human joint trajectories. One of the challenges is to automatically annotate such streams of complex spatio-temporal data in real time. In this paper, we propose an efficient approach to label motion stream data in real time with a limited usage of main memory. Based on a set of user-defined motion profiles, each of them specified...
Convolutional Neural Network (CNN) has received remarkable achievements in hyperspectral image (HSI) classification. However, how to effectively implement spatial context that has been demonstrated to be crucial for classification of HSI is still an open issue. Current CNNs for hyperspectral classification are restricted into a small scale due to small-scale input and limited training samples. Therefore,...
Using spatio-temporal features is popular for action recognition. However, existing methods embed these local features into a global representation. Orders and correlations among local motions of each action are missing. This can make it difficult to distinguish closely related actions. This paper proposes a solution to address this challenge by encoding correlations of movements. Space-time interest...
Face recognition remains a challenge today as recognition performance is strongly affected by variability such as illumination, expressions and poses. In this work we apply Convolutional Neural Networks (CNNs) on the challenging task of both 2D and 3D face recognition. We constructed two CNN models, namely CNN-1 (two convolutional layers) and CNN-2 (one convolutional layer) for testing on 2D and 3D...
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 paper presents a new calibration method for lenslet-based plenoptic cameras. While most existing approaches require the computation of sub-aperture images or depth maps which quality depends on some calibration parameters, the proposed process uses the raw image directly. We detect micro-images containing checkerboard corners and use a pattern registration method to estimate their positions with...
Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction...
Accurate early lung cancer detection is essential towards precision oncology and would effectively improve the patients' survival rate. In this work, we explore the lung cancer early detection capacity by learning from deep spatial lung features. A 3D CNN network architecture is constructed with segmented CT lung volumes as training and testing samples. The new model extracts and projects 3D features...
The constant development of scientific and technological forces has led to further exploration and research in various fields. While exploring the real world, scientists have extended their exploration into the virtual world. The emergence of AR technology makes the real world and the virtual world together. The virtual-actual combination has become a new subject. With the growing power of smart phones,...
In order to understand the underwater environment, it is essential to use sensing methodologies able to perceive the three dimensional (3D) information of the explored site. Sonar sensors are commonly employed in underwater exploration. This paper presents a novel methodology able to retrieve 3D information of underwater objects. The proposed solution employs an acoustic camera, which represents the...
Recognition of human actions is an intelligent way for human-machine communication and Radial basis function (RBF) models are among the most powerful machines on this task. One prerequisite of using this traditional model is that the movement data must be translated into a vector space via the feature extraction process. Recent development of the convolutional neural networks (CNNs) has been shown...
This paper proposes a geometric feature-based method to solve the Simultaneous Localization and Mapping (SLAM) problem in an unknown structured environment using a short range and low Field of View (FoV) measurement unit such as Kinect sensor. A RANdom SAmple Consensus (RANSAC) based algorithm is used for feature detection, and a grid-based point cloud segmentation method has been introduced to improve...
This paper introduces a new video understanding dataset which can be utilised for the related problems of event recognition, localisation and description in video. Our dataset consists of dense, well structured event annotations in untrimmed video of tennis matches. We also include highly detailed commentary style descriptions, which are heavily dependent on both the occurrence as well as the sequence...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.