966 resultados para Image texture


Relevância:

30.00% 30.00%

Publicador:

Resumo:

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

In the last decade, research in Computer Vision has developed several algorithms to help botanists and non-experts to classify plants based on images of their leaves. LeafSnap is a mobile application that uses a multiscale curvature model of the leaf margin to classify leaf images into species. It has achieved high levels of accuracy on 184 tree species from Northeast US. We extend the research that led to the development of LeafSnap along two lines. First, LeafSnap’s underlying algorithms are applied to a set of 66 tree species from Costa Rica. Then, texture is used as an additional criterion to measure the level of improvement achieved in the automatic identification of Costa Rica tree species. A 25.6% improvement was achieved for a Costa Rican clean image dataset and 42.5% for a Costa Rican noisy image dataset. In both cases, our results show this increment as statistically significant. Further statistical analysis of visual noise impact, best algorithm combinations per species, and best value of k , the minimal cardinality of the set of candidate species that the tested algorithms render as best matches is also presented in this research

Relevância:

30.00% 30.00%

Publicador:

Resumo:

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Beach sands from the Rosa Marina locality (Adriatic coast, southern Italy) were analysed mainly microscopically in order to trace the source areas of their lithoclastic and bioclastic components. The main cropping out sedimentary units were also studied with the objective to identify the potential source areas of lithoclasts. This allowed to establish how the various rock units contribute to the formation of beach sands. The analysis of the bioclastic components allows to estimate the actual role of organisms regarding the supply of this material to the beach. Identification of taxa that are present in the beach sands as shell fragments or other remains was carried out at the genus or family level. Ecologi- cal investigation of the same beach and the recognition of sub-environments (mainly distinguished on the basis of the nature of the substrate and of the water depth) was the key topic that allowed to establish the actual source areas of bioclasts in the Rosa Marina beach sands. The sedimentological analysis (including a physical study of the beach and the calculation of some statistical parameters concerning the grain-size curves) shows that the Rosa Marina beach is nowadays subject to erosion.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Recent advances in mobile phone cameras have poised them to take over compact hand-held cameras as the consumer’s preferred camera option. Along with advances in the number of pixels, motion blur removal, face-tracking, and noise reduction algorithms have significant roles in the internal processing of the devices. An undesired effect of severe noise reduction is the loss of texture (i.e. low-contrast fine details) of the original scene. Current established methods for resolution measurement fail to accurately portray the texture loss incurred in a camera system. The development of an accurate objective method to identify the texture preservation or texture reproduction capability of a camera device is important in this regard. The ‘Dead Leaves’ target has been used extensively as a method to measure the modulation transfer function (MTF) of cameras that employ highly non-linear noise-reduction methods. This stochastic model consists of a series of overlapping circles with radii r distributed as r−3, and having uniformly distributed gray level, which gives an accurate model of occlusion in a natural setting and hence mimics a natural scene. This target can be used to model the texture transfer through a camera system when a natural scene is captured. In the first part of our study we identify various factors that affect the MTF measured using the ‘Dead Leaves’ chart. These include variations in illumination, distance, exposure time and ISO sensitivity among others. We discuss the main differences of this method with the existing resolution measurement techniques and identify the advantages. In the second part of this study, we propose an improvement to the current texture MTF measurement algorithm. High frequency residual noise in the processed image contains the same frequency content as fine texture detail, and is sometimes reported as such, thereby leading to inaccurate results. A wavelet thresholding based denoising technique is utilized for modeling the noise present in the final captured image. This updated noise model is then used for calculating an accurate texture MTF. We present comparative results for both algorithms under various image capture conditions.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Résumé : La texture dispose d’un bon potentiel discriminant qui complète celui des paramètres radiométriques dans le processus de classification d’image. L’indice Compact Texture Unit (CTU) multibande, récemment mis au point par Safia et He (2014), permet d’extraire la texture sur plusieurs bandes à la fois, donc de tirer parti d’un surcroît d’informations ignorées jusqu’ici dans les analyses texturales traditionnelles : l’interdépendance entre les bandes. Toutefois, ce nouvel outil n’a pas encore été testé sur des images multisources, usage qui peut se révéler d’un grand intérêt quand on considère par exemple toute la richesse texturale que le radar peut apporter en supplément à l’optique, par combinaison de données. Cette étude permet donc de compléter la validation initiée par Safia (2014) en appliquant le CTU sur un couple d’images optique-radar. L’analyse texturale de ce jeu de données a permis de générer une image en « texture couleur ». Ces bandes texturales créées sont à nouveau combinées avec les bandes initiales de l’optique, avant d’être intégrées dans un processus de classification de l’occupation du sol sous eCognition. Le même procédé de classification (mais sans CTU) est appliqué respectivement sur : la donnée Optique, puis le Radar, et enfin la combinaison Optique-Radar. Par ailleurs le CTU généré sur l’Optique uniquement (monosource) est comparé à celui dérivant du couple Optique-Radar (multisources). L’analyse du pouvoir séparateur de ces différentes bandes à partir d’histogrammes, ainsi que l’outil matrice de confusion, permet de confronter la performance de ces différents cas de figure et paramètres utilisés. Ces éléments de comparaison présentent le CTU, et notamment le CTU multisources, comme le critère le plus discriminant ; sa présence rajoute de la variabilité dans l’image permettant ainsi une segmentation plus nette, une classification à la fois plus détaillée et plus performante. En effet, la précision passe de 0.5 avec l’image Optique à 0.74 pour l’image CTU, alors que la confusion diminue en passant de 0.30 (dans l’Optique) à 0.02 (dans le CTU).