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422 results for “textures”
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Principal stages of image classification system
<p>In computer vision, images or objects are recognised by machine going through two phases<br> shown in the figure 4. First the system is trained with features extracted from sample images in<br> training stage then they are tested on input images in testing stage. The performance of the classifier<br> depends on features extracted from the image. This research work is carried out in three different<br> experiments, first experiment is performed on data set containing the original images of sixteen<br> categories, noisy images are classified in second experiment, and third experiment detects the type<br> of noise affected the image followed by filtering through appropriate filter, then filtered images are<br> classified. The performance of each of the experiment is measured with two approaches. First<br> approach extracts the statistical texture features of the whole image, and the original image of size<br> 128x128 is divided into sixteen blocks of size 32x32 pixels in second approach. Then six statistical<br> texture features discussed in second section are extracted from each of the block producing 96<br> features from each of the images are used for training and testing stage.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 1. Sample Images of sixteen categories
<p>An image is often corrupted by noise in its acquisition or transmission. Noise is any<br> undesired information that degrades the image and appears in images from a variety of sources.</p> <p>Basically, there are three standard noise models [17], which model the types of noise<br> encountered in most images; they are additive noise, multiplicative noise and impulse noise. In this<br> work we have considered the occurrence of additive noise. An image function is given by f (x, y)<br> where (x, y) is spatial coordinate and f is intensity at point(x, y). Let f (x, y) be the original image,<br> g(x, y) be the noisy version and η(x, y) be the noise function, which returns random values coming<br> from an arbitrary distribution.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 7. Performance in experiment 3 for both approaches
<p>Classification of noisy images starts with detection of type of noise followed by appropriate<br> filtering operation. Then similar approaches are followed for feature extraction as discussed in<br> above experiments.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Feed Forward Back propagation Neural Network
<p>This BPNN provides a computationally efficient<br> method for changing the weights in feed forward network, with differentiable activation function<br> units, to learn a training set of input-output data. Being a gradient descent method it minimizes the<br> total squared error of the output computed by the net. The aim is to train the network to achieve a<br> balance between the ability to respond correctly to the input patterns that are used for training and<br> the ability to provide good response to the input that are similar. A typical back propagation<br> network of input layer, one hidden layer and output layer is shown in figure 4.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 6. Performance in experiment 2 for both approaches
<p>Second experiment is performed on noisy data set with 50 images each class for training of<br> feed forward neural network, and 100 images each class are used for testing phase. This experiment<br> is also carried out with two approaches as discussed in first experiment.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise. This is because the<br> texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the<br> second data set improves the result. The table also shows that feature extraction using blocking of<br> the image enhance the average classification rate in all the case.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 5. Performance in experiment 1 for both approaches
<p>The first experiment is carried out on data set containing 16 class and 50 images of each<br> class for training and 100 images of each class for testing. Initially the original images are resized to<br> 128X128 pixels and six texture features are extracted from 800 training images in the first<br> approach. It produces a feature matrix of size 6X800. Similarly 6X1600 feature matrix is produced<br> for testing stage. Then a neural classifier is designed with ten hidden layers and 16 output layers.<br> Feature matrix of training images are used to train the neural network. After that they are tested on feature matrix of test image. In second approach, the original images are divided into 16 blocks of<br> size 32X32 pixel each block. So it produces 96 features (16 blocksX6 features) for each image and<br> feature matrix of 96X800 for training image and 96X1600 feature matrix for testing samples.</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>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 2. Original image affected by Gaussian noise
<p>In this research work we have detected Gaussian noise (fig 2) pattern in our images based on<br> methodology discussed in [18]. Where statistical moments features are extracted from the noise<br> patterns for noise class detection. This experiment detects the Gaussian noise patterns from images.<br> This leads to applying of wiener filter on noisy images, which gives the best noise removal.</p>
Haptic Saliency Model for Rigid Textured Surfaces
<p>When touching an object, we focus more on some of its parts rather than touching the whole object’s surface, i.e. some parts are more salient than others. Here we investigated how different physical properties of rigid, plastic, relieved textures determine haptic exploratory behavior. We produced haptic stimuli whose textures were locally defined by random distributions of four independent features: amplitude, spatial frequency, orientation and isotropy. Participants explored two stimuli one after the other and in order to promote exploration we asked them to judge their similarity. We used a linear regression model to relate the features and their gradients to the exploratory behavior (spatial distribution of touch duration). The model predicts human behavior significantly better than chance, suggesting that exploratory movements are to some extent driven by the low level features we investigated. Remarkably, the contribution of each predictor changed as a function of the spatial scale in which it was defined, showing that haptic exploration preferences are spatially tuned, i.e. specific features are most salient at different spatial scales.</p> <p>Metzger, A., Toscani, M., Valsecchi, M. & Drewing, K. (2018) Haptic saliency model for rigid textured surfaces. In Prattichizzo, D., Shinoda, H., Tan, H. Z., Ruffaldi, E. & Frisoli, A. (Eds.), Haptics: Science, Technology, and Applications, 11th International Conference, EuroHaptics 2018, Pisa, Italy, June 13-16, 2018, Proceedings, Part I (pp. 389–400). Springer International Publishing, Cham.</p> <p> </p> <p>Data of the experiment is stored in a zip file, containing all data relative to the publication. The 'movement' folder containes participnts' movement data. The 'stimuli' folder containes the 2D and 3D models of the stimuli. </p> <p>Explanaition and coding of the data is provided in the file VARIABLE_CODES.txt.</p>
Fig. 22. Character 0, dorsal skin texture. A in PHYLOGENETIC SYSTEMATICS OF DART-POISON FROGS AND THEIR RELATIVES (AMPHIBIA: ATHESPHATANURA: DENDROBATIDAE)
Fig. 22. Character 0, dorsal skin texture. A: State 0, smooth (galactonotus, AMNH live exhibit). B: State 1, posteriorly tubercular (fraterdanieli, MHNUC 364). C: State 2, granular (macero, AMNH 129473). D: State 3, spiculate (Dendrophryniscus minutus, AMNH 93856).
Fig. 2 in Dietary ecology of the extinct cave bear: Evidence of omnivory as inferred from dental microwear textures
Fig. 2. Bivariate plot of complexity (Asfc) and anisotropy (epLsar) of extant ursids and Ursus spelaeus.
Fig. 1 in Dietary ecology of the extinct cave bear: Evidence of omnivory as inferred from dental microwear textures
Fig. 1. Meshed axonometrics of digital elevation models showing microwear features. Examples include Ursus americanus (A), black bear (SBMNH 1381, modern specimen from California); Ursus arctos (B), brown bear (LACM 31256, modern specimen from Alaska), and Ursus spelaeus (C), cave bear (AMNH 11100, Pleistocene fossil specimen from Germany).
Fig. 2 in A dental microwear texture analysis of the Mio-Pliocene hyaenids from Langebaanweg, South Africa
Fig. 2. Photosimulations of fossil hyaena microwear surfaces generated from point clouds. A. Hyaenictitherium namaquensis (Stromer, 1931), SAM−PQL 12848. B. Hyaenictis hendeyi (Werdelin, Turner, and Solounias, 1994), SAM−PQL 20990. C. Ikelohyaena abronia (Hendey, 1974), SAM−PQL 22202L. D. Chasmaporthetes australis (Hendey, 1974), SAM−PQL 22204. Each represents a field of view of 276 µm × 204 µm.
Fig. 3 in A dental microwear texture analysis of the Mio-Pliocene hyaenids from Langebaanweg, South Africa
Fig. 3. Bivariate plot of fossil and extant feliform anisotropy and complexity. The lines on the graphs connect specimens with minimum and maximum values for each taxon, and indicate the ranges of variation for these attributes. The data for the extant species are from Schubert et al. (2010).
Fig. 1 in A dental microwear texture analysis of the Mio-Pliocene hyaenids from Langebaanweg, South Africa
Fig. 1. Biochronology of species discussed in the text (based upon Werdelin and Solounias 1991; Turner et al. 2008). Asterisks refer to the genera analysed in this study. MN, Mammal Neogene Zone.
FIGURE 14 in Virtual palaeontology: the effects of mineral composition and texture of fossil shell and hosting rock on the quality of X-ray microtomography (XMT) outcomes using Palaeozoic brachiopods
FIGURE 14. Three-dimensional rotational model (video) of Timaniella harkeri (GSC26406). For video see palaeo-electronica.org/content/2017/1891-xmt-on-brachiopod-fossils.
FIGURE 12 in Virtual palaeontology: the effects of mineral composition and texture of fossil shell and hosting rock on the quality of X-ray microtomography (XMT) outcomes using Palaeozoic brachiopods
FIGURE 12. XMT result of Tyloplecta nankingensis (Q-2). 1-7, serial slices in the transverse plane (from dorsal to ventral). 8-14, serial slices in the coronal plane (from posterior to anterior). 15-18, serial slices in the sagittal plane (from lateral to middle). 19-21, lateral (19), ventral (20) and dorsal (21) views of the reconstructed 3-D model (external shell). 22, ventral view of the 3-D model in transparent mode. All the slice images were obtained under false-color lookup tables (Color 1 option in DataViewer). Abbreviations: cp, cardinal process; ap, adductor platform; ms, median septum; mc, muscle scar.
FIGURE 13 in Virtual palaeontology: the effects of mineral composition and texture of fossil shell and hosting rock on the quality of X-ray microtomography (XMT) outcomes using Palaeozoic brachiopods
FIGURE 13. Three-dimensional reconstruction model of internal shell structures of Timaniella harkeri (GSC26406). 1-6, dorsal and anterior views of the whole shell interior through posteriorly continuous rotation. 7-12, dorsal and anterior views of shell interior without spiralia through posteriorly continuous rotation.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.
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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.
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