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29 results for “image filtering”
Automatic plankton image classification - can capsules and filters help coping with data set shift?
<p>This data set is related to the article 'Automatic plankton image classification - can capsules and filters help coping with data set shift?' published in 'Limnology and Oceanography: Methods' by Plonus <em>et al.</em> (2021).</p> <p>The images belong to the trainings set used to train the models in the aforementioned paper (training_) and three different additional data sets which were used to evaluate the performance of the trained models in application mode (fs446_; fs466_; fs534_). The Python-Script 'separate_files.py' can be used to move all the images in different folders for each data set and class respectively.</p>
Training CNNs with Low-Rank Filters for Efficient Image Classification: Trained Models
<p>Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744</p> <p>Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here:</p> <ul> <li>cifarma.csv: Network-in-Network CIFAR10 Models</li> <li>mitma.csv: MIT Places Models</li> <li>googlenetma.csv: GoogLeNet ILSVRC2012 Models</li> <li>vggma.csv: VGG-11 ILSVRC2012 Models</li> </ul> <p> </p> <p> </p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 3. Applying Wiener Filter on noisy image
<p>The Wiener filter is the mean square error-optimal stationary linear filter for images<br> degraded by additive noise and blurring. It removes the additive noise and inverts the blurring<br> simultaneously. The Wiener filtering is a linear estimation of the original image. The approach is<br> based on a stochastic framework.</p>
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Figure 17. Algorithm A9pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>The proposed algorithms refer to two directions: suppressing the noise resulted from acquisition of the fingerprints, the Gaussian noise in filters: A1 to A8 and to “salt and pepper” reduction noise in A9. The set of 9 filters make use of some methods like: &thresholds for segmentation of the digital images and adapted for filters A4, A5 and A6; &quartiles that dive ranked data set in four equal groups and they are applied on the filters: A1, A2, A4, A6, A7, A8, A9; &selections of parameters, like in: A7, A8 and the dimension of the local neighborhood (in our case 10x10 pixels);</p>
Figure 2. Algorithm A1pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A2&uses the principle of quartiles over the entire image in conjunction with applying the quartiles calculated on small areas "locally"(Figure 2.);</p>
Figure 3. Algorithm A3pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A3&is achieved by the extrapolation of the values that are "in the immediate" neighborhood of extremes (0 and 255), made in a "local" manner (Figure 3.);</p>
Figure 1.Algorithm A1pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>In the image processing field, the quartiles are used also for eliminating the outliers (aberrant values) and replacing them with a median value, but in this paper we would like to avoid the median value because often introduces blurring in the resulting image. The set of algorithms uses the quartiles to transform the values under or upper the quartiles Q1, Q3 (in the overall image) and the q1, q3 (in the 10X10 vicinity) in black or white, leaving the rest of pixels in greyscale or introducing other type of processing like increasing or decreasing the values of pixels in the vicinity of the quartiles or simply by locally adjust values according to the vicinity values. A1 & uses quartile (figure 1.) which applies "locally" in small areas, usually 10x10 pixels or estimated using a sample representing approximately 40% of all image data (columns) and transform;</p>
Figure 4. Algorithm A4pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A4& uses quartiles principle applied globally, in conjunction with the thresholds referred to A3 (Figure 4.);</p>
Figure 19. Dynamics of the filtered images with the 9 algorithms and the 2 methods of classification-Efficient Filtering of Noisy Fingerprint Images
<p>The classification (Malik, Gautam, Sahai, Jha & Singh, 2013) and ranking stage can be visualized in the Figure 19, the summary of the filtered images is shown in Table 3 and the pseudocode of the current step can be visualized in Figure 18. The overall results show that the two selection criterion: fuzzy and aggregation indicate that the most efficient algorithm is A6 and according to each criterion there can be made certain decisions to choose the best filters for each situation. Also, the results are influenced by the parameters set to calibrate the filtering, the fuzzy profiles, the weighted sum or the vicinity approach.</p>
Figure 6. Algorithm A6pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A6&the principle of quartiles applied to the entire image, using the quartiles q1 and q3 as thresholds, but the update refers only for the pixel values only if Q1 < q1 or Q3 > q3, where q1 and q3 are the quartiles calculated in the 10x10 pixels area (Figure 6.);</p>
Figure 18. Classification/ranking algorithmpseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>The classification (Malik, Gautam, Sahai, Jha & Singh, 2013) and ranking stage can be visualized in the Figure 19, the summary of the filtered images is shown in Table 3 and the pseudocode of the current step can be visualized in Figure 18. The overall results show that the two selection criterion: fuzzy and aggregation indicate that the most efficient algorithm is A6 and according to each criterion there can be made certain decisions to choose the best filters for each situation. Also, the results are influenced by the parameters set to calibrate the filtering, the fuzzy profiles, the weighted sum or the vicinity approach.</p>
Figure 7. Algorithm A7pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A7&applied to the entire surface using quartiles(Q1 and Q3) in conjunction with an algorithm for the processing of gray tones between Q1 and Q3 is as follows: if in addition is full field the condition that (q2 >Q2), when the initial value of the pixel is adjusted at (initial_value*(1&(q2/q1))), and on the other hand if (q2<Q2) then the value is adjusted to (initial_value*(1+q3/q2 )), please see Figure 7.;</p>
Figure 8. Algorithm A8pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A8 & uses the principles of quartiles per global and additionally adjusts the gray tones between Q1 and Q3 as follows: &if (q2<Q2), the pixel shall be updated with the following value (initial_value *(1.1)); &if (q2 >Q2) when the pixel value is updated with (initial_value *(0.9)), and the purpose is to bring q2(local quartile or median) as much closer as possible to Q2(2 quartile or median overall global), please see Figure 8.;</p>
LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters
<p><strong>LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters</strong></p> <p><strong>Usage</strong></p> <ul> <li>Download the compressed files (13 in total) and uncompress them</li> <li><strong>Please cite reference 1) below in your publications if you make use of the data of this repository</strong></li> </ul> <p><strong>People & Contact</strong></p> <ul> <li><a href="http://wiki.hh.se/caisr/index.php/Fernando_Alonso-Fernandez">Fernando Alonso-Fernandez</a> (contact person).</li> </ul> <p><strong>References</strong></p> <ol> <li>Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J., Alonso-Fernandez, F., "On the Effect of Selfie Beautification Filters on Face Detection and Recognition" <a href="https://github.com/HalmstadUniversityBiometrics/LFW-Beautified/blob/main">https://arxiv.org/abs/2110.08934</a></li> <li>Hedman, P., Skepetzis, V., The Effect of Beautification Filters on Image Recognition: "Are filtered social media images viable Open Source Intelligence?" Master Thesis at Halmstad University, Sweden (Master’s Programme in Network Forensics) <a href="http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799">http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799</a></li> </ol> <p> </p>
Scanning electron microscopy (SEM) images of particulate matter collected on air filters
<p>Airborne PM sampling was conducted within a larger study on the PM composition of different areas in Santa Rosa, La Pampa, Argentina by Prof. Dr. Mendez Mariano. Airborne PM10 samples were collected on commercial 47mm diameter PTFE membrane filters (Image 1-blank) and Nylon filters (Image 2-blank). The PM10 was collected using an electrostatic precipitator coupled with the Easy Dust Generator (EDG). Filters were analysed using a Scanning Electron Microscope (Phenom™ ProX Desktop, Thermofisher). Images were taken using an accelerating voltage of 15 kV. SEM-EDX results are presented in this dataset.</p>
Figure 5. Algorithm A5pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A5&apply thresholds globally across the image (Figure 5.);</p>
Evaluation of Image Filters for their integration with LSQR Computerized Tomography Reconstruction Method
<p>Projection data used for the results, including the A matrix and b projections vector for resolution 512x512 and different number of views. Reference image included. Format for Matlab (.mat files). </p>
Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor
<p>Data corresponding to the figures of the main text of "Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor"</p> <p> </p>
Data from: Depth image super-resolution reconstruction based on a modified joint trilateral filter
Depth image super-resolution (SR) is a technique that utilizes signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, the external database or high-resolution (HR) images are needed for acquiring the priori information to support the SR reconstruction. To overcome the limitation, a depth image SR method which does not need the reference of any external images is proposed. In the paper, a high-quality edge map is firstly constructed using a sparse coding method, which uses a dictionary learned from the original images themselves at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method not only can preserve the sharpness of image edge, but also can avoid the dependence on database. Experimental results show the proposed method is superior to some state-of-the-art depth image SR methods.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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