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Figure 1. Experimental protocol-Single Trial Classification of Evoked EEG Signals Due to RGB Colors
<p>Various methods exist to enhance and pre-process EEG signals by removing different artifacts like eye movement and blinking, Electrooculography (EOG) or Electromyography (EMG). The complexity of EEG signal's representation makes it difficult to define the circle that encloses most of the data points of their total. The problem with these methods is that they work at frequency domain or time domain, but not both, which causes a loss of important data during the processing stage. The researches show that the combination of frequency and time domain information can provide more completed features that improve the classification performance of EEG signals. Empirical Mode Decomposition (EMD) has recently been developed by N. (Huang Huang et al., 1998) as an adaptive time-frequency data analysis method. It has proven to be quite versatile in a broad range of applications for extracting signals from data generated in noisy nonlinear and non- stationary processes. Wavelet Transform (WT) is also an analysis method that uses the time- frequency domain. However, EMD acts essentially as a filter bank, resembling those involved in wavelet decompositions.</p>
Figure 2-Single Trial Classification of Evoked EEG Signals Due to RGB
<p>The auto regressive (AR) model has high ability to represent the EEG signal characteristics and form reliable feature sets (Hatamikia, Maghooli, & Nasrabadi, 2014). The AR function computes the coefficients of an AR model of order p on each channel individually. Each sample is obtained from the summation of previous weighted samples. The model order is determined by the number of weights, which are called AR coefficients. The AR model of order p for a zero-mean time series can be written as shown in equation (5). (5) Where p and denotes a zero-mean process with variance i …,p for each channel separately, and concatenate the results to form a feature vector for a window. Because each channel is treated individually, the spatio-temporal information that exists in the time series cannot be directly estimated, which is considered a limitation of this function. There is a built in function in Matlab that can be used directly by passing each IMF separately with the order of the AR model as shown in equation (6).</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 11. Accuracy of different method for unseen faces
<p>Table 3 shows the mode detection accuracy of the proposed method and its combination with two other methods (uniform LBP and circular LBP) for different people. The overall accuracy of the proposed procedure is calculated as this way one video is chosen as input, and after mode detection the three aforementioned steps are applied on this video. The obtained feature vectors are given to the neural network and the corresponding labels to each frame are regarded as output. Afterwards, the overall accuracy is calculated from the confusion matrix. However, it should be noted that the expression detection criteria are the observation of a certain number of subsequent similar labels and in the case of observing a limited or sparse number of different labels the final label would not change. Figure 10 and 11 show result of different methods for seen and unseen data respectively. Table 4 shows results for seen data with proposed method and uniform LBP.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-
<p>Table 3 shows the mode detection accuracy of the proposed method and its combination with two other methods (uniform LBP and circular LBP) for different people. The overall accuracy of the proposed procedure is calculated as this way one video is chosen as input, and after mode detection the three aforementioned steps are applied on this video. The obtained feature vectors are given to the neural network and the corresponding labels to each frame are regarded as output. Afterwards, the overall accuracy is calculated from the confusion matrix. However, it should be noted that the expression detection criteria are the observation of a certain number of subsequent similar labels and in the case of observing a limited or sparse number of different labels the final label would not change. Figure 10 and 11 show result of different methods for seen and unseen data respectively. Table 4 shows results for seen data with proposed method and uniform LBP.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 9. Results of our facial motion capture system(a,b,c,d)
<p>In test procedures, single video feature vectors consisting of different expressions are given to the neural network and the network produces the corresponding labels for each frame as output. If there is a mode in a video which is not available in the data base, the nearest available mode's label to this mode is produced. For example, in test3 and test6 videos, the surprise expression (that have been showed with number 7) is recognized as open mouth expression. At the end, considering the certain numbers of subsequent similar labels (at least 10 frames, because the minimum number of one modes' frames is related to “rising the eyebrow” mode that takes 10 frames), the expressions are detected, and a 3D show of these expressions are represented. For instance, in test8 videos that have been obtained from unseen face, the “smiling” and “open mouth” expressions are well recognized, but expressions related to rising the eyebrows are not detected properly and all the corresponding frames to this expression are regarded as normal expression. Figure 9 shows example of generated 3D models.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 6. Proposed feed-forward neural network classifier
<p>After the feature extraction stage, neural network is used for classifying the modes. In this study, the utilized expressions are normal, smiling, open mouth, rising the eyebrows, anger and pursing modes. In fact, they are some selective modes for face movements. It should be noted that the modes can be increased but in this case we work with these six modes. This paper used three layers feed-forward neural network (Figure 6). The proposed neural network includes 800 nodes for the input layer (400 nodes for U matrix and 400 nodes for V matrix), 100 nodes for the hidden layer and 6-nodes for output layer. From the collected data 70% are used for training, 15% for validation and the last 15% are used to evaluate the neural network.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 5. Examples of the circular LBP (Huang et al., 2011)
<p>One limitation of the basic LBP operator is that its small 3x3 neighborhood cannot capture dominant features with large scale structures. To deal with the texture at different scales the operator was later generalized to use neighborhoods of different sizes. A local neighborhood is defined as a set of sampling points evenly spaced on a circle which is centered at the pixel to be labeled. The sampling points that do not fall within the pixels are interpolated using bilinear interpolation, thus allowing for any radius and any number of sampling points in the neighborhood. Figure 5 shows some examples of the extended LBP operator where the notation (P, R) denotes a neighborhood of P sampling points on a circle of radius of R.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB- Figure 4. An example of the uniform LBP operator (Huang et al., 2011)
<p>The original LBP operator labels the pixels of an image by means of decimal numbers called Local Binary Patterns or LBP codes, which encode the local structure around each pixel. It proceeds thus as illustrated in figure 4: Each pixel is compared with its eight neighbors in a 3x3 neighborhood by subtracting the center pixel value. The resulting strictly negative values are encoded with 0 and the others with 1. A binary number is obtained by concatenating all these binary codes in a clockwise direction starting from the top-left one and its corresponding decimal value is used for labeling. The derived binary numbers are referred to as Local Binary Patterns or LBP codes.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database
<p> Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.</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>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 1. Peak of facial expression in database
<p>In this study, data are obtained from the Kinect camera that benefits from colorful images and depth data. Kinect can record colorful and depth data simultaneously at 30 frames per second. The data are collected from the person who initially pose in front of the camera with normal face mode and then the various modes are represented. It should be noted that data are obtained at different distances from the Kinect camera and in different lighting conditions. Figure 1 shows various facial modes in our database.</p>
DS6.SSSA-02. Human_Walking_Dataset_at_SSSA. Dataset for characterizing the walking behavior of subjects and identification of changes in the motion patterns, based on RGB-D cameras.
<p>This dataset is used for characterizing the wakling behavior of subjects. It is based on RGB-D camerasand obtained through data collection experiments at the premises of the Percro Labotory, TeCIP Intitute, Scuola Superiore Sant'Anna (Pisa, Italy). Data are collected for the gait patterns of 9 healthy participants.</p>
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>
RGB-Based Behavior Cloning Dataset for Surgical Robotics: 99,522 Episodes of Optimal Demonstrations
<h3><strong>Dataset Description</strong>:</h3> <p>This dataset contains 99,522 episodes of RGB-based state-action-reward expert demonstrations collected from a reaching task within a surgical robotics simulation environment, LapGym (Scheikl et al.). The data was generated using the LapGym ReachEnv, where a robotic grasper is tasked with reaching a specific point in 3D space. Each episode consists of a series of RGB images (64x64 pixels), corresponding actions, rewards, and terminal flags, designed for training behavior cloning and offline RL algorithms.</p> <p>This dataset was created for the paper "Assessing Behavior Cloning with RGB Inputs in Surgical Robotics Through Dataset Ablation". The expert demonstrations were collected using an optimal agent, where actions were computed based on the known locations of the grasper and the point of interest.</p> <p>The specific settings for the ReachEnv environment used to collect the dataset are as follows:</p> <ul> <li><strong>Environment</strong>: <code>ReachEnv</code></li> <li><strong>Observation Type</strong>: <code>RGB</code></li> <li><strong>Render Mode</strong>: <code>HUMAN</code></li> <li><strong>Action Type</strong>: <code>CONTINUOUS</code></li> <li><strong>Distance to Target Threshold</strong>: <code>0.01</code></li> <li><strong>Image Shape</strong>: <code>(64, 64)</code></li> <li><strong>Frame Skip</strong>: <code>1</code></li> <li><strong>Time Step</strong>: <code>0.1</code></li> <li><strong>Reward Amounts</strong>: <ul> <li><strong>Distance to Target</strong>: <code>0.0</code></li> <li><strong>Delta Distance to Target</strong>: <code>0.0</code></li> <li><strong>Successful Task</strong>: <code>100.0</code></li> <li><strong>Time Step Cost</strong>: <code>0.0</code></li> <li><strong>Workspace Violation</strong>: <code>0.0</code></li> </ul> </li> <li><strong>Sphere Radius</strong>: <code>0.020</code></li> </ul> <p>Key features of the dataset include:</p> <ul> <li><strong>RGB Inputs</strong>: Each episode includes 64x64 RGB frames representing the environment's visual state.</li> <li><strong>Optimal Demonstrations</strong>: All actions represent optimal behavior for completing the reach task.</li> <li><strong>Sparse Rewards</strong>: Rewards are only provided upon successful task completion, offering a challenging learning scenario.</li> <li><strong>Varied Episode Lengths</strong>: Episodes vary in length, depending on how quickly the task is completed.</li> </ul> <h3><strong>Applications</strong>:</h3> <p>This dataset is designed for research in:</p> <ul> <li>Behavior cloning with RGB image inputs.</li> <li>Data efficiency and sample efficiency in imitation learning.</li> <li>Offline reinforcement learning with visual inputs.</li> </ul> <h3><strong>Structure</strong>:</h3> <ul> <li><strong>Observations</strong>: Images stored as 64x64 RGB pixel arrays.</li> <li><strong>Actions</strong>: Continuous actions corresponding to the robotic grasper’s movements.</li> <li><strong>Rewards</strong>: Sparse rewards indicating task success.</li> <li><strong>Terminals</strong>: Terminal flags for task completion.</li> </ul> <h3><strong>How to Use</strong>:</h3> <p>This dataset can be used to train and evaluate offline models for robotic control tasks in conjunction with LapGym, particularly in the domain of surgical robotics. It is especially suited for behavior cloning experiments, offline reinforcement learning, and studies on data efficiency.</p> <h3><strong>Citation</strong>:</h3> <p>Please cite this dataset in any publications as:<br><em>Acs and Zhong (2024). RGB-Based Behavior Cloning Dataset for Surgical Robotics: 99,522 Episodes of Optimal Demonstrations. </em></p>
UAV RGB imagery dataset captured at nadir and oblique angles over pistachio trees in Spain, including images, GCPs, 3D point cloud and orthomosaic.
<p>The dataset comprises 248 images taken in two flights on 29 July 2021 over a pistachio orchard in Spain. In addition, GCPs (ground control points) were collected to improve the photogrammetric process accuracy. The photos were taken using a UAV DJI Phantom Advance quadcopter equipped with a DJI FC6310 RGB 20-megapixel camera. The first flight mission was planned to take nadir images (-90º gimbal pitch degree), whereas the second flight was scheduled to take oblique images (-60º gimbal pitch degree), both at 55 metres above the ground. In addition, the images were used to generate a 3D point cloud, DEM and orthomosaic, which were included in the dataset.This dataset is useful for precision agriculture researchers interested in photogrammetric reconstruction.</p>
Data used in "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition"
<p>Datasets from the paper "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition", By D. T. Blewett, T. X. Choi, Y.-C. Zheng, and E. A. Cloutis, to be published in the journal <em>Earth and Space Science</em>.</p> <p>Reflectance spectra for <em>Apollo</em> lunar samples 14003, 15601, 70011, 14310, and 65015 were published by Wagner et al. (1987), <em>Icarus 69</em>, 14–28. The spectra were digitized by Amanda Hendrix and Faith Vilas (see Hendrix and Vilas (2006), <em>Astron. J. 132</em>, 1396–1404). I took the spectra that Hendrix and Vilas supplied to me and resampled them to RELAB wavelengths. The spectra are in a tab-delimited text file.</p> <p>The responsivities of the <em>Chang'E-3</em> PCAM RGB channels were published by X. Ren et al. (2014), <em>Res. Astron. Astrophys. 14</em>, 1557–1566. We digitized the RGB curves from Fig. 2 of the Ren paper. The curves are in tab-delimited text files.</p> <p> </p>
IODP Expedition 350 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
IODP Expedition 376 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
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.