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609 results for “brain imaging”
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>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 4. Mouse trajectory – images taken from http://iographica.com 4 hours in Photoshop (left) vs. 4 hours in Eclipse (right)
<p>Studies from different fields (Arroyo & Wei, 2006), (Mäkiaho & Poranen, 2012), (Lockton, Harrison, Cain, Stanton, & Jennings, 2013), (Seelye, et al., 2015), (Hehman, Stolier, & Freeman, 2014) have shown that mouse trajectory and clicks are environment (application, device) and user specific, especially if mapped over a long working session (Figure 4). </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 8. Simulation of Coiflet 1 (PyWavelets discussion group, 2008), analyzing as one-dimensional images
<p>Using only the wavelet coefficients was inadequate for classification. For example, for the pie chart, we obtained large wavelet coefficients located in the low-frequency domain; however, if we changed a circle in the pie chart to other shapes, such as a radar chart, the wavelet transformation gave results that were similar to those of the original pie chart. The Hough transformation can solve this problem since it detects the shapes of objects</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in face orientation recognition. In this paper, we propose an image mapping technique for face analysis in smart camera networks with a feature extraction and data from the facial feature. We estimate the face orientation angles in all camera views, based on the matched imaged data. Our objective is to obtain a set of facial structures which can work as landmarks for tracking and recognition of facial expressions. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </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>
New Ideas for Brain Modelling 4-Figure 1. Example mapping of cells presented as an image.
<p>The author has used this structure before in Greer (2016) and it is a type of entropy classifier. It attempts to reduce the error overall and is not so concerned with minimising individual associations. The paper Greer (2017) describes a classifier that is conjecturally more visual in nature than other types and it also uses a complete linking method. Instead of several levels of feature refactoring, it is a 1-level impression only. With the image classifier, each cell stores a count of every other cell it gets associated with, when averaging this can determine what cells are most similar to the pixel in question. Figure is an example of the clustering technique. If the top LHS grid is the first image to be mapped, then for cell A1, the other black cells are recorded as shown, with a count of 1. The count would then be incremented each time a cell is recorded again, for example, after the second image, cell A3 would lose a count. The idea of linking everything this way has now been used 3 times.</p>
Validation of the registration of intraoperative optical image of exposed brain with preoperative MRI volumes (T1 volumes with injection of Gadolinium).
<p>This dataset contains the results of the registration of intraoperative optical images of exposed brain with pre-operative MRI volumes (T1 volumes with injection of Gadolinium). The file contains the validation metric (Euclidean distance) calculated with a landmark-based validation approach for 9 patients.</p>
Two-photon fluorescence microscopy image stacks of human brain sections (grey and white matter)
<p>Two-photon fluorescence microscopy (TPFM) image stacks of human brain sections including grey matter (N<sub>g</sub>=10) and white matter (N<sub>w</sub>=10), considered in the validation of the 3D fiber orientation analysis pipeline proposed in: "<em>Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy</em>". <br> Human brain tissue was preliminarily treated for TPFM following the label-free MAGIC preparation technique, presented in (Costantini et al., <em>Scientific Reports</em> 2021).</p> <p>The PSF of the TPFM system has a FWHM of (0.692, 0.692, 2.612) μm along the x, y, and z axes, respectively, whereas the adopted voxel size is 0.88 μm x 0.88 μm x 1 μm.</p>
4D time-lapse images of brain and trunk development in the larvacean Oikopleura dioica
<p>The larvacean, <em>Oikopleura</em> <em>dioica</em> is a planktonic chordate, which is an emerging model organism with a short life cycle of 5 days and belongs to tunicates (urochordates). Organ formation in the trunk proceeds in seven hours from hatching of tailbud larvae at three hours after fertilization (hpf) to completion of organ formation in fully functional juveniles that start feeding at 10 hpf and are just miniature of adult form. Development of <em>O. dioica</em> has been described (Nishida, H., 2008 Development of the appendicularian <em>Oikopleura</em> <em>dioica</em>: culture, genome, and cell lineages. Dev. Growth Differ. 50, S239–S256.). The dataset contains 4D (3D+time) time-lapse images that were acquired during larval development using differential interference contrast optics, and wide-field fluorescent microscope, which visualize cell membrane and nuclei of the entire trunk region. In some cases, animal or vegetal hemisphere blastomeres are labelled to trace the descendants. The dataset would be generally utilized as basic morphological data during the larval development and for tracing cell lineages at a single cell level. This data set is related to the manuscript "Formation of the brain by stem cell divisions of large neuroblasts in <em>Oikopleura</em> <em>dioica</em>, a simple chordate".</p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
<p>The idea that sensory stimulation to the embryo (in utero or in ovo) may be crucial for brain development is widespread. Unfortunately, up to now evidence was only indirect because mapping of embryonic brain activity in vivo is challenging. Here we applied for the first time Manganese Enhanced Magnetic Resonance Imaging (MEMRI), a functional imaging method, to the eggs of domestic chicks. We revealed both spontaneous and light-induced brain asymmetry by comparing embryonic brain activity in vivo of eggs that were stimulated by light or maintained in the darkness. Our protocol paves the way to investigation of the effects of a variety of sensory stimulations on brain activity in embryo.</p>
4D time-lapse images of brain and trunk development in the larvacean Oikopleura dioica
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Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
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Mobile Brain-Body Imaging (MoBI) dual-tasking datasets (response inhibition while walking): Young adults
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Mobile Brain-Body Imaging (MoBI) dual-tasking datasets (response inhibition while walking): Increased cognitive load
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Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students
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Mobile Brain-Body Imaging (MoBI) dual-tasking datasets (response inhibition while walking): Older adults
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BIDS Data for "An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging"
<p>Base data package for the “An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging” article, formatted corresponding to the Brain Imaging Data Structure.</p>
Data from: Brain imaging evidence for why we are numbed by numbers
We as humans do not value lives consistently. While we are willing to act for one victim, we often become numb as the number of victims increases. The empathic ability to adopt others' perspectives is essential for motivating help. However, the perspective-taking ability in our brains seems limited. Using functional MRI, we demonstrated that the core empathy network including the medial prefrontal cortex (mPFC) was more engaged for events happening to a single person than those happening to many people, no matter whether the events were emotionally neutral or negative. In particular, the perspective-taking-related mPFC showed greater and more extended activations for events about one person than those about many people. The mPFC may be the neural marker of why we feel indifferent to the suffering of large numbers of people in humanitarian disasters.
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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)
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.