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2,139 results for “recognition”
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection
<p>The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 3. The methodology flowchart
<p>The methodology of our experimental method is described in Figure 3 below.</p> <p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features.</p>
Axiom voice recognition dataset
<p>The AXIOM Voice Dataset has the main purpose of gathering audio recordings from Italian natural language speakers. This voice data collection intended to obtain audio reconding sample for the training and testing of VIMAR algorithm implemented for the Smart Home scenario for the Axiom board. The final goal was to developing an efficient voice recognition system using machine learning algorithms. A team of UX researchers of the University of Siena collected data for five months and tested the voice recognition system on the AXIOM board [1]. The data acquisition process involved natural Italian speakers who provided their written consent to participate in the research project. The participants were selected in order to maintain a cluster with different characteristics in gender, age, region of origin and background. </p>
Dataset used in "Free context smartphone based application for motor activity levels recognition"
<p>This is the data set used in the paper "Free context smartphone based application for motor activity levels recognition", 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, 2016, pp1-4.</p> <p>The data refer to three subjects (i.e. subject1, subject2 and subject3). For each subject a folder is created. The folder contains data used for training and for test in all the conditions addressed by the reference paper.</p> <p>Activities are labeled by the last character of the filename as follows: 1-2 resting; 3-6 walking; 7-8 running; 9-12 climbing stairs</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 2. Facial expression recognition in proposed method
<p>In this stage, a video is prepared using the color data captured from Kinect camera. The face region in each frame is obtained from the video using Viola-Jones algorithm (Figure 2). Because of different distance from the Kinect camera, the obtained images from the face must be re-sized, in order to have the same size. At the end, the colored images are converted to gray-scaled images.</p>
Fruit Recognition dataset
<p>The database used in this study is comprising of 44406 fruit images, which we collected in a period of 6 months. The images where made with in our lab’s environment under different scenarios which we mention below. We captured all the images on a clear background with resolution of 320×258 pixels. We used HD Logitech web camera to took the pictures. During collecting this database, we created all kind of challenges, which, we have to face in real-world recognition scenarios in supermarket and fruit shops such as light, shadow, sunshine, pose variation, to make our model robust for, it might be necessary to cope with illumination variation, camera capturing artifacts, specular reflection shading and shadows. We tested our model’s robustness in all scenarios and it perform quit well.</p>
Oficio de Hipotecas de Girona. A dataset of Spanish notarial deeds (18th Century) for Handwritten Text Recognition and Layout Analysis of historical documents.
<p>This dataset is a subset of 596 documents from the <em>Registre d'Hipoteques de Girona</em> of 1769 collection, guarded by the <a href="http://xac.gencat.cat/ca/llista_arxius_comarcals/girones/"><em>Arxiu Històric de Girona</em></a>. This collection, is composed by hundreds of thousands of notarial deeds from the XVIII-XIX century (1768-1862). Sales, redemption of censuses, inheritance and matrimonial chapters are among the most common documentary typologies in the collection.</p> <p>This dataset is composed of more than 23700 text lines written by a single hand, covering more that 50 different topics (documentary typologies) and a vocabulary of more than 2400 different words. The documents are transcribed using the so-called diplomatic criteria. Additionally, transcripts were tagged with <br> extra enriching/complementary information (e.g. expansion of the abbreviations, hyphen marks, etc.). Along with the transcripts the layout of the document is detected and recorded. Pages have been labeled using six different layout regions.</p> <p>The images along with their respective ground-truth was compiled in PAGE compliant XML format<br> by the <a href="http://www2.udg.edu/tabid/11296/Default.aspx"><em>Centre de Recerca d'Història Rural</em></a> and the HTR group of the <a href="https://www.prhlt.upv.es">Pattern Recognition and Human Language Technologies Research Center</a>.</p>
Changes in neuronal representations of phonemes in the ascending auditory system and their role speech recognition
<p>This dataset comprises neural responses to a set of speech sounds from several brain regions. Auditory nerve data was simulated using a computational model of the auditory nerve. Also included are multi-unit extracellular recordings or responses to the same stimuli in the inferior colliculus and auditory cortex of anaethetised guinea pigs.</p>
Fig. 6 in Enlarging the monotypic Monocarpieae (Annonaceae, Malmeoideae): recognition of a second genus from Vietnam informed by morphology and molecular phylogenetics
Fig. 6. – Leoheo domatiophorus Chaowasku, D.T. Ngo & H.T. Le, showing habit with inflorescences and flowers. [HUAF collectors 2009-03-19-ND,CMUB] [Drawing: A. Damthongdee]
Fig. 5 in Enlarging the monotypic Monocarpieae (Annonaceae, Malmeoideae): recognition of a second genus from Vietnam informed by morphology and molecular phylogenetics
Fig. 5. – Reproductive organs of Leoheo domatiophorus Chaowasku, D.T. Ngo & H.T. Le: A. Flower with petals and stamens removed; B. Flower with petals, stamens, and carpels removed, back view, showing outer side of sepals; C. Same as (B), but on another side, showing a volcano-shaped torus and inner side of sepals; D. Inner side of an outer petal; E. Outer side of an outer petal; F. Inner side of an inner petal; G. Outer side of an inner petal; H. Stamen, abaxial side; I. Stamen, adaxial side; J. Carpels, showing enlarged and irregularly lobed stigmas; K. Fruit, showing longitudinal ridges on monocarp surface; L. Seed, lateral view, showing a raphe; M. Seed, lateral view, showing a pitteand slightly rugose surface; N. Cross section of a seed, showing spiniform endosperm ruminations. [A–J: HUAF collectors 2009-03-19-ND, CMUB; K: Chaowasku 131, CMUB; L–N: Chaowasku 165, CMUB] [Drawing: A. Damthongdee]
Fig. 3 in Enlarging the monotypic Monocarpieae (Annonaceae, Malmeoideae): recognition of a second genus from Vietnam informed by morphology and molecular phylogenetics
Fig. 3. – Inflorescence position of Leoheo Chaowasku (A) and Monocarpia Miq. (B). A. Axillary inflorescences/infructescences of Leoheo domatiophorus Chaowasku, D.T. Ngo & H.T. Le; B. Terminal inflorescence of Monocarpia kalimantanensis Kessler. [A: HUAF collectors 2009-03-19-ND, CMUB; B: Sidiyasa et al. 3469, L] [Photos: A: D.T. Ngo; B: Arbainsyah]
Fig. 4 in Enlarging the monotypic Monocarpieae (Annonaceae, Malmeoideae): recognition of a second genus from Vietnam informed by morphology and molecular phylogenetics
Fig. 4. – Lower leaf surface of Leoheo Chaowasku (A) and Monocarpia Miq. (B). A. Leoheo domatiophorus Chaowasku, D.T. Ngo & H.T. Le, with a hairy domatium; B. Monocarpia maingayi (Hook. f. & Thomson) I.M. Turner, without domatia. [A: Chaowasku 131, CMUB; B: Promchua 18, CMUB]
Fig. 2. – A in Enlarging the monotypic Monocarpieae (Annonaceae, Malmeoideae): recognition of a second genus from Vietnam informed by morphology and molecular phylogenetics
Fig. 2. – A. Leaf of Monocarpia kalimantanensis Kessler, showing conspicuous intramarginal veins; B. Fruit of Monocarpia maingayi (Hook. f. & Thomson) I.M. Turner, showing monocarps without longitudinal ridges; C– H: Leoheo domatiophorus Chaowasku, D.T. Ngo & H.T. Le; C. Leaf without intramarginal veins; D. Fruit, showing monocarps with longitudinal ridges; E. Flowering branches; F. Dissected flower and young fruit; G. Dissected flower, showing detached stamens and stigmas; H. Flower, showing enlarged and irregularly lobed stigmas. [A: Sidiyasa et al. 3469, L; B: Gardner & Sidisunthorn ST0541a, L; C–D: Chaowasku 131, CMUB; E–H: HUAF collectors 2009-03-19-ND, CMUB] [Photos: A: Arbainsyah; B: S. Gardner & P. Sidisunthorn; C–H: D.T. Ngo]
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
<p>While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.</p> <p>If you use this dataset in your research, please cite the following publication:</p> <p>T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in <em>2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020</em>. IEEE, 2020.</p> <pre><code>@InProceedings{Grenzdoerffer2020ycbm, title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation}, author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim}, booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020}, year = {2020}, publisher = {{IEEE}} }</code></pre> <p>This paper is also available on arXiv: <a href="https://arxiv.org/abs/2004.11657">https://arxiv.org/abs/2004.11657</a></p> <p> </p> <p>To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):</p> <pre><code class="language-bash"># IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs: # ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type # nvdu requires Python 3.5 or 3.6 sudo add-apt-repository -y ppa:deadsnakes/ppa # to get python3.6 on Ubuntu Xenial sudo apt-get update sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip # create a new virtual environment virtualenv -p python3.6 venv_nvdu cd venv_nvdu/ source bin/activate # clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu' # download and transform the meshes # (alternatively, unzip the meshes contained in the dataset # to <path to venv_nvdu>/lib/python3.6/site-packages/nvdu/data/ycb/aligned_cm) nvdu_ycb -s # run nvdu_viz to visualize the dataset cd <a subdirectory of the YCB-M dataset with some frames> nvdu_viz --name_filters '*.jpg' </code></pre> <p>For further details, see README.md.</p>
Qualitative Interview Data: Users and therapists perceptions of myoelectric multi-function upper limb prostheses with direct and pattern recognition control
<p>The data uploaded here were collected in 2016/2017 through semi-structured interviews with prosthesis users and hand therapists. Participants were mainly asked about satisfaction with their prosthetic device and about activities which they perform with the prosthesis. Interviews were conducted in Dutch and German language.</p> <p>All interview data are made publicly available, except for data of prosthesis users who were experienced with pattern recognition control (n=4). Due to the small number of these participants, their interview data is only available upon reasonable request to not compromise participant privacy. </p> <p> </p>
Emotion recognition: social appraisal and offset task, 8-12 yo.
<p>Data from social appraisal task and offset task in 57 8-12 yo. school children, with pedagogy school type information.</p>
Fig. 4 in On the distinctive call of a threatened phenotype of Allobates femoralis (Anura: Aromobatidae) and its recognition by allopatric conspecific males
Fig. 4. (a) Differences in latencY to first orientation towards loudspeakers of male Allobates femoralis (Boulenger, 1884) tested at RFAD with plaYbacks of acoustic stimuli built from recordings of natural calls. (b) Differences in latencY to focal males approach within 30 cm of loudspeakers in the same experiments. Values in top-right corner of (b) and (c) correspond to Kruskall-Wallis Test statistics and p-values, assuming Chi-square distribution with two degrees of freedom.
Fig. 3 in On the distinctive call of a threatened phenotype of Allobates femoralis (Anura: Aromobatidae) and its recognition by allopatric conspecific males
Fig. 3. (a) Waveform (upper graph) and spectrogram (lower graph) of a 14 s bout of advertisement calls of Allobates femoralis (Boulenger, 1884) recorded near Altamira, State of Pará, Brazil. First three calls are considered warm-up calls, formed by four notes. Remaining calls are formed by six notes. (b) Detailed view of waveform and spectrogram of a single call formed by six notes, originating from the same call bout. Roman numerals correspond to the designation of silent intervals between notes; arabic numerals correspond to the designation of notes (see Table 1 for a description of parameters of notes and silent intervals). Air temperature at the time of recording was 29.0°C.
Fig. 2 in On the distinctive call of a threatened phenotype of Allobates femoralis (Anura: Aromobatidae) and its recognition by allopatric conspecific males
Fig. 2. Sample spectrograms of stimuli used in the playback experiments conducted at Reserva Ducke (RFAD), in Manaus, Brazil, from December 2011 to April 2012. The original advertisement calls of A. femoralis males used for the stimuli were recorded in (a) RFAD, Manaus, State of Amazonas, Brazil in June 2008, by L. K. Erdtmann; (b) Belterra, State of Pará, Brazil, in January 2007, by P. I. Simões; (c) Altamira, State of Pará, Brazil, in March 2009, by A.P. Lima. Air temperature at the time of recording was 24.7 °C, 24.7 °C, 28.6 °C, respectivelY. Advertisement calls were analYZed in Raven 1.2 using Blackmann window, 80% overlapping and a fast Fourier transform with frequency resolution of 80 Hz and 2048 points.
ScienceDex guides
Understand access before you commit
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.