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224 results for “count data”
Fig. 5 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 5. Distribution of wetlands depending on species number (axis X) and average amount of birds in them (axis Y).
Paradinium-Oithona annotated images and count data
<p>Human annotated images, machine learning derived count data, and environmental data associated with our paper " Discovery and dynamics of a cryptic marine copepod-parasite interaction" published in Marine Ecology Progress Series. The work is based on imagery generated by the <a href="https://doi.org/10.1002/lom3.10394">Scripps Plankton Camera System</a> deployed at the Scripps Pier in La Jolla, California, USA.</p> <p>This repository contains:</p> <ul> <li><strong>human_annotated_SPC_data.zip </strong>- Regions of Interested collected by the SPC sorted by a human expert into relevant classes: Oithona, Oithona with parasite, and Oithona with eggs. Access to the "other" class is available upon request.</li> <li><strong>corrected_counts_091817.txt</strong> - Count data from the summer of 2015 generated a fine tuned deep neural network with associated human corrected counts.</li> <li><strong>oith_parasite_081420.txt </strong>-<strong> </strong>Count data from March 2015 - April 2016. Data collected after August 2015 is machine generated and should considered a maximum estimate of relative abundance.</li> <li><strong>autoss_a8bc_287c_07d6.csv </strong>- Environmental data measured by the automated shore station deployed at Scripps Pier and maintained the <a href="https://sccoos.org/">Southern California Coastal Ocean Observing System</a>. Complete shore station data can be accessed via <a href="https://erddap.sccoos.org/erddap/tabledap/HABs-ScrippsPier.html?Location_Code,latitude,longitude&distinct()">NOAA ERDDAP</a>.<strong> </strong></li> </ul> <p> </p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
<p><em>Significance</em></p> <p>Near-infrared fluorescence image-guided surgery is often thought of as a spectral imaging problem where the channel count is the critical parameter, but it should also be thought of as a multiscale imaging problem where the field-of-view and spatial resolution are similarly important.</p> <p><em>Aim</em></p> <p>Conventional imaging systems based on division-of-focal-plane architectures suffer from a strict relationship between the channel count on one hand and the field-of-view and spatial resolution on the other, but bioinspired imaging systems that combine stacked photodiode image sensors and long-pass/short-pass filter arrays offer a weaker tradeoff.</p> <p><em>Approach</em></p> <p>In this paper, we explore how the relevant changes to the image sensor and associated image processing routines affect image fidelity during image-guided surgeries for tumor removal in an animal model of breast cancer and nodal mapping in women with breast cancer.</p> <p><em>Results</em></p> <p>We demonstrate that a transition from a conventional imaging system to a bioinspired one, along with optimization of the image processing routines, yields improvements in multiple measures of spectral and textural rendition relevant to surgical decision-making.</p> <p><em>Conclusions</em></p> <p>These results call for a critical examination of the devices and algorithms that underpin image-guided surgery to ensure that surgeons receive high-quality guidance and patients receive high-quality outcomes as these technologies enter clinical practice.</p>
Count data, spatial data, environmental data - Dee Estuary Waders 1970-2020
<p>Combined raw ecological field data, WeBS data, and environmental data used to investigate the spatio-temporal drivers of wader community on the Dee estuary (UK).</p>
Data from: Crows 'count' the number of self-generated vocalizations
<p>Producing a specific number of vocalizations with purpose requires a sophisticated combination of numerical abilities and vocal control. Whether any animal possesses such a capacity to voluntarily control the number of self-generated vocalizations is yet unknown. We demonstrate that crows can flexibly produce a variable number of one to four vocalizations in response to arbitrary cues associated with numerical values. The acoustic features of the first vocalization of a sequence were predictive of the total number of vocalizations, indicating a planning process. Moreover, the acoustic features of vocal units were foretelling of their order in the sequence and could be used to read-out counting errors during vocal production. Together, the crows' vocal enumeration capability could be an evolutionary precursor to symbolic counting found uniquely in humans.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data
<p>In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 14. Symbol 8 regeneration
<p>As we can see, here we were able to recover the lost middle portion of character ‘A’ using our genetic algorithm. If we can apply some noise filtering technique, the result would be far better. In figure 14, 15 there are another two examples.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 11. Initial population
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 10. Block example
<p>First, we have to convert the pixelated small image to the original size image, and then divide these into the blocks as it done in extraction time. Then we have to add or subtract random bits from each of these blocks to equal each block hamming bit to original hamming bit number. And then GA is applied to match these randomness to original image hamming bits in per row and column. In figure 10 there is a 4-block example which regenerates randomly using total block bit count. Now we will try to match their row and column bits of information with the extracted data which is described in later section.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 6. Methodology
<p>Our method will first resize the image using normal image resizing option provided by operating system or standard library and attach the extra 2 array of data which contains no of 1 in original image in each row and column. Also, the total no of 1 in that image will be present too.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 16. Failed Generations
<p>For some images there is a chance to get stuck where fitness function maxed, but we are not near to the original image like in figure 16, both row and column fitness matched. But image lost a key portion from original image, in these cases we should increase the weight of fitness function Fx(X), which will solve the issue.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 2. Image stored in cloud each year (Perret, 2017)
<p>Currently, 4.7 trillion of photos are saved in the cloud (Perret, 2017). And only a few percentage are called to use again. So less used files can be stores in a compression technique which can save more space than time and make the cloud system faster as memory redundancy time will be reduced.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 1. Photo amount by year (Perret, 2017)
<p>Revolution of a portable camera with computer started to produce an exponential rate of media files, and users are sharing these files with everyone. So, using the cloud to store images is becoming a favorite choice for users. But cloud does not only store huge files which are approximately 1.2 trillion in 2017 (Perret, 2017), it also has to transfer these files to a different network to serve users. To reduce load, the cloud system started to use different compression algorithm. These algorithms have a tradeoff between time and space. Most of these have better time complexity than space. But as the cloud has powerful and distributed computing power, it may be better to focus on saving space. As data transfer takes more time than processing same data in the cloud. A perfect use case is a mobile sending the large image to the cloud takes more data transfer time than the compression and decompression process in the cloud. So, in this age of the distributed computer, it is better to reduce size as computation time is less important than network data transfer time.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 9. Resized image and extracted data for a 100*100 size image
<p>For the extraction, our goal is to divide an image into smaller blocks and keep the row and column data for these blocks. But for our experiment we used a single block, which means taking the full image as a single block. For bigger image we should always divide the image in separate blocks and work on them par rally. As in figure 8, after extracting the data we can add the row and column bits information in the resized image or saved in a separate file. For proof of concept we saved it in a text file. And later that file is used to feed GA to make the fitness function, in figure 9 an extraction has been shown. In upper and side textbox containing the information which later is saved in a text file.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 7. Basic Methodology
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 5. Image down sample
<p>Our proposal is to store each column and row bits count in a separate file and used that to reproduce the image using genetic algorithm.If we take 10% of an image size and the row and column image hamming bit count our total size will be approximately below 15% of the actual image size. We proposed a method to reproduce original image from using this 15% information.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 8. Sample Data Extraction for a 20*20 size image
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 4. Image regeneration using GA
<p>In 2008, Roger Johansson was able to regenerate a Mona Lisa image from random sampling (Roger Johansson, 2017). It uses a genetic algorithm to model a population of individuals, each containing a string of DNA which can be visualized in the form of an image (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017).</p> <p>By starting with a population consisting of a randomly generated gene pool, each individual is compared to the reference image (the one on the left), and the individuals can then be ranked by their likeness to it, known as their "fitness", with the best fit being displayed on the output image (the one on the right) (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017). By breeding the fittest individuals from the population, the DNA which produces the most accurate representation of the reference image is selected over successive generations, effectively demonstrating the power of a natural selection process to produce the best candidate for any given environment (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017).</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 3. Data compression and image reconstruction (55:148 Digital Image Processing, 2017)
<p>There are several techniques which are normally divided into two categories lossy and lossless image compressions. In lossy compression, after recovery there are negligible difference present where lossless gives accurate image. Huffman encoding is very well known, which can provide optimal compression and decompression without error (55:148 Digital Image Processing, 2017). The basic idea of Huffman coding is to represent data by number of variable size, where more frequent info being represented by shorter number (55:148 Digital Image Processing, 2017). Currently the Lempel-Ziv (or Lempel-Ziv-Welch, LZW) algorithm for dictionary-based coding has got attention as a better compression algorithm (55:148 Digital Image Processing, 2017).</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 12. After few generation
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
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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.