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11 results for “Object Localization”
Histograms of local features for the object dataset
<p>Pictures used in the experiments were acquired as frames from an interaction with an iCub robot, through an RGBD sensor (red, green, and blue camera coupled with a depth sensor). The acquisition was performed and is described in more details by Lyubova and Filliat. Each image contains one visual object and possibly the hand of the operator.</p> <p><br /> Images contains objects from a subset of 10 objects each appearing in more than a thousand frames.During the acquisition, the objects are moved and rotated. Hence they are presented from distinct points of view and they may be partially cluttered by the hand of the operator.</p> <p> </p> <p>For more information on the format please look at https://github.com/omangin/multimodal/blob/master/db/models/objects.py.</p> <p> </p>
Data from: SimPLE: A visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
<p>Existing robotic systems have a clear tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single task, lacking precise generalization, i.e., the ability to solve many tasks without compromising on precision. This paper explores solutions for precise and general pick-and-place. In precise pick-and-place, i.e. kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement, which can facilitate further manipulation. We propose simPLE (simulation to Pick Localize and PLacE) as a solution to precise pick-and-place. simPLE learns to pick, regrasp and place objects precisely, given only the object CAD model and no prior experience. We develop three main components: task-aware grasping, visuotactile perception, and regrasp planning. Task-aware grasping computes affordances of grasps that are stable, observable, and favorable to placing. The visuotactile perception model relies on matching real observations against a set of simulated ones through supervised learning. Finally, we compute the desired robot motion by solving a shortest path problem on a graph of hand-to-hand regrasps. On a dual-arm robot equipped with visuotactile sensing, we demonstrate pick-and-place of 15 diverse objects with simPLE. The objects span a wide range of shapes and simPLE achieves successful placements into structured arrangements with 1mm clearance over 90% of the time for 6 objects, and over 80% of the time for 11 objects.</p>
Data from: SimPLE: A visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
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Raw Experimental Data for work presented in 'Precise Localization of Multiple Noncooperative Objects in a Disordered Cavity by Wave Front Shaping'
<p>This is the raw experimental data for the work presented in 'Precise Localization of Multiple Noncooperative Objects in a Disordered Cavity by Wave Front Shaping', to be published in Physical Review Letters.</p> <p> </p> <p>https://journals.aps.org/prl/accepted/b307fY31A5a13c5579fc4252303f8b2e5e024fc00</p> <p> </p> <p>See the README file for an explanation of the data.</p> <p> </p> <p> </p>
Implementation and Results of a Randomized Local Search on the 2D Rectangular Bin Packing Problem with Item Rotation using Different Objective Functions
<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) applied to the two-dimensional bin packing problem without orientation (where items can be rotated by 90 degrees). As benchmark dataset, we use the <code>beng</code>, <code>A</code>, and <code>class</code> instances from <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> as well as the four non-trivial Almost Squares in Almost Squares (<a href="https://math.vu.nl/~sbhulai/publications/data_analytics2016b.pdf"><code>Asqas</code></a>) instances</p> <p>These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Rui Zhao, Tianyu Liang, Zhize Wu, Daan van den Berg, Matthias Thürer, and Thomas Weise. 2024. Randomized Local Search on the 2D Rectangular Bin Packing Problem with Item Rotation. In <em>Genetic and Evolutionary Computation Conference (GECCO'24 Companion),</em> July 14–18, 2024, Melbourne, VIC, Australia. ACM, New York, NY, USA, 4 pages. doi:<a href="https://doi.org/10.1145/3638530.3654139">10.1145/3638530.3654139</a>.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> version 0.8.34 and <a href="https://thomasweise.github.io/moptipy">moptipy</a> version 0.9.98, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>data</code> is the directory with the results and their evaluation.</li> <li><code>data/results</code> is the directory with the log files generated by the experiment. In this folder, there are two sub-folders, <code>ibf1</code> and <code>ibf2</code>. We tested two different encodings, but found that the second one (<code>ibf2</code>) is too slow to do meaningful experiments. Thus, the experiments with it were abandoned and only one objective function was tested. We include <code>ibf2</code> for the sake of completeness, whereas <code>ibf1</code> was used in our paper. Either way, both <code>ibfX</code> folders contain one directory for each objective function applied to them. In each such directory, there is one folder (for the single algorithm applied) and this folder, in return, contains one folder per benchmark instance. The benchmark instance folders contain the three log files of the three runs that we applied to each instance/algorithm/objective combination.<br>Each log file contains information of one run, i.e., one execution of one algorithm on one problem instance. All improving moves of a run as well as the final solution are stored in the log file.</li> <li><code>data/evaluator</code> is the folder containing Python scripts that were used to evaluate these results. Two scripts are provided: <code>evaluator_short.py</code> was used for generating the tables used in the final paper version. <code>evaluator_full.py</code> provides larger tables, which could not be included in the final paper due to space reasons.</li> <li>Folder <code>evaluation_full</code> was generated using <code>evaluator_full.py</code> and contains tables and figures and a result summary in CSV format.</li> <li>Folder <code>evaluation_short</code> was generated using <code>evaluator_short.py</code> and contains both the tables used in the paper as well as a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> and other benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Rui ZHAO (赵睿) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:zr1329142665@163.com">zr1329142665@163.com</a>.</p>
Class-aware Sounding Objects Localization via Audiovisual Correspondence
<p>Videos for "Class-aware Sounding Objects Localization via Audiovisual Correspondence"</p>
Class-aware Sounding Objects Localization via Audiovisual Correspondence_mp4
<p>Class-aware Sounding Objects Localization via Audiovisual Correspondence</p>
Data from: Object recognition and localization from 3D point clouds by maximum-likelihood estimation
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Local Compression Seroma DIminution Objective (CLODIS)
ClinicalTrials.gov study NCT03598712. IPD Sharing: NO. Countries: 1. Publications: 0.
Study To Assess Local Tolerability of Crisaborole 2% Ointment and Vehicle Ointment In Healthy Participants Using Subject-reported Assessments and Objective Measurements
ClinicalTrials.gov study NCT03760042. IPD Sharing: NO. Countries: 1. Publications: 0.
Deep Learning for Real Time 3D Multi-Object Detection, Localization, and Tracking : Application to Smart Mobility
<p>In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOV3 [1] which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 [2,3] for monocular vision and MADNEt [4] for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time condition. Object tracking is necessary in order to mitigate the risks of collisions. Unlike, traditional tracking approaches which requires target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT [5] approach for 3D object tracking. We introduce an extended Kalman filter [6] to better estimate the position of objects. Extensive experiments carried out on KITTI dataset [7] prove that our proposal outperforms state-of-the-art approches. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.