934 resultados para depth image


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Quickremovalofbiosolidsinaquaculturefacilities,andspeciallyinrecirculatingaquaculturesystems(RAS),isoneofthemostimportantstepinwastemanagement.Sedimentationdynamicsofbiosolidsinanaquaculturetankwilldeterminetheiraccumulationatthebottomofthetank.

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Mitjançant imatges estereoscòpiques es poden detectar la posició respecte de la càmera dels objectes que apareixen en una escena. A partir de les diferències entre les imatges captades pels dos objectius es pot determinar la profunditat dels objectes. Existeixen diversitat de tècniques de visió artificial que permeten calcular la localització dels objectes, habitualment amb l’objectiu de reconstruir l’escena en 3D. Aquestes tècniques necessiten una gran càrrega computacional, ja que utilitzen mètodes de comparació bidimensionals, i per tant, no es poden utilitzar per aplicacions en temps real. En aquest treball proposem un nou mètode d’anàlisi de les imatges estereoscòpiques que ens permeti obtenir la profunditat dels objectes d’una escena amb uns resultats acceptables. Aquest nou mètode es basa en transformar la informació bidimensional de la imatge en una informació unidimensional per tal de poder fer la comparació de les imatges amb un baix cost computacional, i dels resultats de la comparació extreure’n la profunditat dels objectes dins l’escena. Això ha de permetre, per exemple, que aquest mètode es pugui implementar en un dispositiu autònom i li permeti realitzar operacions de guiatge a través d’espais interiors i exteriors.

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El reconeixement dels gestos de la mà (HGR, Hand Gesture Recognition) és actualment un camp important de recerca degut a la varietat de situacions en les quals és necessari comunicar-se mitjançant signes, com pot ser la comunicació entre persones que utilitzen la llengua de signes i les que no. En aquest projecte es presenta un mètode de reconeixement de gestos de la mà a temps real utilitzant el sensor Kinect per Microsoft Xbox, implementat en un entorn Linux (Ubuntu) amb llenguatge de programació Python i utilitzant la llibreria de visió artifical OpenCV per a processar les dades sobre un ordinador portàtil convencional. Gràcies a la capacitat del sensor Kinect de capturar dades de profunditat d’una escena es poden determinar les posicions i trajectòries dels objectes en 3 dimensions, el que implica poder realitzar una anàlisi complerta a temps real d’una imatge o d’una seqüencia d’imatges. El procediment de reconeixement que es planteja es basa en la segmentació de la imatge per poder treballar únicament amb la mà, en la detecció dels contorns, per després obtenir l’envolupant convexa i els defectes convexos, que finalment han de servir per determinar el nombre de dits i concloure en la interpretació del gest; el resultat final és la transcripció del seu significat en una finestra que serveix d’interfície amb l’interlocutor. L’aplicació permet reconèixer els números del 0 al 5, ja que s’analitza únicament una mà, alguns gestos populars i algunes de les lletres de l’alfabet dactilològic de la llengua de signes catalana. El projecte és doncs, la porta d’entrada al camp del reconeixement de gestos i la base d’un futur sistema de reconeixement de la llengua de signes capaç de transcriure tant els signes dinàmics com l’alfabet dactilològic.

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The ongoing development of the digital media has brought a new set of challenges with it. As images containing more than three wavelength bands, often called spectral images, are becoming a more integral part of everyday life, problems in the quality of the RGB reproduction from the spectral images have turned into an important area of research. The notion of image quality is often thought to comprise two distinctive areas – image quality itself and image fidelity, both dealing with similar questions, image quality being the degree of excellence of the image, and image fidelity the measure of the match of the image under study to the original. In this thesis, both image fidelity and image quality are considered, with an emphasis on the influence of color and spectral image features on both. There are very few works dedicated to the quality and fidelity of spectral images. Several novel image fidelity measures were developed in this study, which include kernel similarity measures and 3D-SSIM (structural similarity index). The kernel measures incorporate the polynomial, Gaussian radial basis function (RBF) and sigmoid kernels. The 3D-SSIM is an extension of a traditional gray-scale SSIM measure developed to incorporate spectral data. The novel image quality model presented in this study is based on the assumption that the statistical parameters of the spectra of an image influence the overall appearance. The spectral image quality model comprises three parameters of quality: colorfulness, vividness and naturalness. The quality prediction is done by modeling the preference function expressed in JNDs (just noticeable difference). Both image fidelity measures and the image quality model have proven to be effective in the respective experiments.

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The main objective for this study was to explore certain organization’s product line rebranding process and its impact on product line’s perceived image. The case company is a global paper, packaging and forest products company, business segment paper board. The audience explored is one of the company’s major customers, merchant in Germany. The research was performed as a descriptive case study with a purpose to provide longitudinal insight into the product line image and its eventual alteration as a result of the case company’s rebranding process. Mainly qualitative methods were used for conducting the research. The data for the empirical part was collected with a web-based survey at two different points of time; before the rebranded products entered the market and after they had been available approximately six months. The results of this study reveal that the case company has performed well in its attempt to improve product line’s brand image through rebranding. It was found that between the two brand image measurements the product brand image seems to have improved in all of the areas which according to theoretical framework of this study contribute to formation of brand image; brand associations, marketing communications and interpersonal relationships, not forgetting the original platform that initiated the change; technical quality modifications. In other words it may be concluded that as technical quality was brought to a new level, also assessments about the brand image improved respectively.

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Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.