1 |
Color Persistent Anisotropic Diffusion of ImagesFreddie, Åström, Michael, Felsberg, Reiner, Lenz January 2011 (has links)
Techniques from the theory of partial differential equations are often used to design filter methods that are locally adapted to the image structure. These techniques are usually used in the investigation of gray-value images. The extension to color images is non-trivial, where the choice of an appropriate color space is crucial. The RGB color space is often used although it is known that the space of human color perception is best described in terms of non-euclidean geometry, which is fundamentally different from the structure of the RGB space. Instead of the standard RGB space, we use a simple color transformation based on the theory of finite groups. It is shown that this transformation reduces the color artifacts originating from the diffusion processes on RGB images. The developed algorithm is evaluated on a set of real-world images, and it is shown that our approach exhibits fewer color artifacts compared to state-of-the-art techniques. Also, our approach preserves details in the image for a larger number of iterations. / Original Publication:Åström Freddie, Felsberg Michael and Lenz Reiner, Color Persistent Anisotropic Diffusion of Images, 2011, Image Analysis, SCIA conference, 23-27 May 2011, Ystad Sweden, 262-272.http://dx.doi.org/10.1007/978-3-642-21227-7_25Copyright: Springer
|
2 |
A Color Filter Array Interpolation Method Based on Sampling TheoryGlotzbach, John William 26 August 2004 (has links)
Digital cameras use a single image sensor array with a color filter array (CFA) to measure a color image. Instead of measuring a red, green, and blue value at every pixel, these cameras have a filter built onto each pixel so that
only one portion of the visible spectrum is measured. To generate a full-color image, the camera must estimate the missing two values at every pixel. This process is known as color filter array interpolation.
The Bayer CFA pattern samples the green image on half of the pixels of the imaging sensor on a quincunx grid. The other half of the pixels measure the red and blue images equally on interleaved rectangular sampling grids.
This thesis analyzes this problem with sampling theory. The red and blue images are sampled at half the rate of the green image and therefore have a higher probability of aliasing in the output image. This is apparent when simple interpolation algorithms like bilinear interpolation are used for CFA interpolation.
Two reference algorithms, a projections onto convex sets (POCS) algorithm and an edge-directed algorithm by Adams and Hamilton (AH), are studied. Both algorithms address aliasing in the green image. Because of the high correlation among the red, green, and blue images, information from the red and blue images can be used to better interpolate the green image. The reference algorithms are studied to learn how this information is used. This leads to two new interpolation algorithms for the green image.
The red and blue interpolation algorithm of AH is also studied to determine how the inter-image correlation is used when interpolating these images. This study shows that because the green image is sampled at a higher rate, it retains much of the high-frequency information in the original image. This information is used to estimate aliasing in the red and blue images. We present a general algorithm based on the AH algorithm to interpolate the red and blue images. This algorithm is able to provide results that are on average, better than both reference algorithms, POCS and AH.
|
3 |
Detection of highway warning signs in natural video images using color image processing and neural network techniques on a PCKellmeyer, David January 1992 (has links)
No description available.
|
4 |
Color Image Processing based on Graph TheoryPérez Benito, Cristina 22 July 2019 (has links)
[ES] La visión artificial es uno de los campos en mayor crecimiento en la actualidad que, junto con otras tecnologías como la Biometría o el Big Data, se ha convertido en el foco de interés de numerosas investigaciones y es considerada como una de las tecnologías del futuro. Este amplio campo abarca diversos métodos entre los que se encuentra el procesamiento y análisis de imágenes digitales. El éxito del análisis de imágenes y otras tareas de procesamiento de alto nivel, como pueden ser el reconocimiento de patrones o la visión 3D, dependerá en gran medida de la buena calidad de las imágenes de partida.
Hoy en día existen multitud de factores que dañan las imágenes dificultando la obtención de imágenes de calidad óptima, esto ha convertido el (pre-) procesamiento digital de imágenes en un paso fundamental previo a la aplicación de cualquier otra tarea de procesado. Los factores más comunes son el ruido y las malas condiciones de adquisición: los artefactos provocados por el ruido dificultan la interpretación adecuada de la imagen y la adquisición en condiciones de iluminación o exposición deficientes, como escenas dinámicas, causan pérdida de información de la imagen que puede ser clave para ciertas tareas de procesamiento. Los pasos de (pre-)procesamiento de imágenes conocidos como suavizado y realce se aplican comúnmente para solventar estos problemas: El suavizado tiene por objeto reducir el ruido mientras que el realce se centra en mejorar o recuperar la información imprecisa o dañada. Con estos métodos conseguimos reparar información de los detalles y bordes de la imagen con una nitidez insuficiente o un contenido borroso que impide el (post-)procesamiento óptimo de la imagen.
Existen numerosos métodos que suavizan el ruido de una imagen, sin embargo, en muchos casos el proceso de filtrado provoca emborronamiento en los bordes y detalles de la imagen. De igual manera podemos encontrar una enorme cantidad de técnicas de realce que intentan combatir las pérdidas de información, sin embargo, estas técnicas no contemplan la existencia de ruido en la imagen que procesan: ante una imagen ruidosa, cualquier técnica de realce provocará también un aumento del ruido. Aunque la idea intuitiva para solucionar este último caso será el previo filtrado y posterior realce, este enfoque ha demostrado no ser óptimo: el filtrado podrá eliminar información que, a su vez, podría no ser recuperable en el siguiente paso de realce.
En la presente tesis doctoral se propone un modelo basado en teoría de grafos para el procesamiento de imágenes en color. En este modelo, se construye un grafo para cada píxel de tal manera que sus propiedades permiten caracterizar y clasificar dicho pixel. Como veremos, el modelo propuesto es robusto y capaz de adaptarse a una gran variedad de aplicaciones. En particular, aplicamos el modelo para crear nuevas soluciones a los dos problemas fundamentales del procesamiento de imágenes: suavizado y realce. Se ha estudiado el modelo en profundidad en función del umbral, parámetro clave que asegura la correcta clasificación de los píxeles de la imagen. Además, también se han estudiado las posibles características y posibilidades del modelo que nos han permitido sacarle el máximo partido en cada una de las posibles aplicaciones. Basado en este modelo se ha diseñado un filtro adaptativo capaz de eliminar ruido gaussiano de una imagen sin difuminar los bordes ni perder información de los detalles. Además, también ha permitido desarrollar un método capaz de realzar los bordes y detalles de una imagen al mismo tiempo que se suaviza el ruido presente en la misma. Esta aplicación simultánea consigue combinar dos operaciones opuestas por definición y superar así los inconvenientes presentados por el enfoque en dos etapas. / [CAT] La visió artificial és un dels camps en major creixement en l'actualitat que, junt amb altres tecnlogies com la Biometria o el Big Data, s'ha convertit en el focus d'interés de nombroses investigacions i és considerada com una de les tecnologies del futur. Aquest ampli camp comprén diversos m`etodes entre els quals es troba el processament digital d'imatges i anàlisis d'imatges digitals. L'èxit de l'anàlisis d'imatges i altres tasques de processament d'alt nivell, com poden ser el reconeixement de patrons o la visió 3D, dependrà en gran manera de la bona qualitat de les imatges de partida.
Avui dia existeixen multitud de factors que danyen les imatges dificultant l'obtenció d'imatges de qualitat òptima, açò ha convertit el (pre-) processament digital d'imatges en un pas fonamental previa la l'aplicació de qualsevol altra tasca de processament. Els factors més comuns són el soroll i les males condicions d'adquisició: els artefactes provocats pel soroll dificulten la inter- pretació adequada de la imatge i l'adquisició en condicions d'il·luminació o exposició deficients, com a escenes dinàmiques, causen pèrdua d'informació de la imatge que pot ser clau per a certes tasques de processament. Els passos de (pre-) processament d'imatges coneguts com suavitzat i realç s'apliquen comunament per a resoldre aquests problemes: El suavitzat té com a objecte reduir el soroll mentres que el real se centra a millorar o recuperar la informació imprecisa o danyada. Amb aquests mètodes aconseguim reparar informació dels detalls i bords de la imatge amb una nitidesa insuficient o un contingut borrós que impedeix el (post-)processament òptim de la imatge.
Existeixen nombrosos mètodes que suavitzen el soroll d'una imatge, no obstant això, en molts casos el procés de filtrat provoca emborronamiento en els bords i detalls de la imatge. De la mateixa manera podem trobar una enorme quantitat de tècniques de realç que intenten combatre les pèrdues d'informació, no obstant això, aquestes tècniques no contemplen l'existència de soroll en la imatge que processen: davant d'una image sorollosa, qualsevol tècnica de realç provocarà també un augment del soroll. Encara que la idea intuïtiva per a solucionar aquest últim cas seria el previ filtrat i posterior realç, aquest enfocament ha demostrat no ser òptim: el filtrat podria eliminar informació que, al seu torn, podria no ser recuperable en el seguënt pas de realç.
En la present Tesi doctoral es proposa un model basat en teoria de grafs per al processament d'imatges en color. En aquest model, es construïx un graf per a cada píxel de tal manera que les seues propietats permeten caracteritzar i classificar el píxel en quëstió. Com veurem, el model proposat és robust i capaç d'adaptar-se a una gran varietat d'aplicacions. En particular, apliquem el model per a crear noves solucions als dos problemes fonamentals del processament d'imatges: suavitzat i realç. S'ha estudiat el model en profunditat en funció del llindar, paràmetre clau que assegura la correcta classificació dels píxels de la imatge. A més, també s'han estudiat les possibles característiques i possibilitats del model que ens han permés traure-li el màxim partit en cadascuna de les possibles aplicacions. Basat en aquest model s'ha dissenyat un filtre adaptatiu capaç d'eliminar soroll gaussià d'una imatge sense difuminar els bords ni perdre informació dels detalls. A més, també ha permés desenvolupar un mètode capaç de realçar els bords i detalls d'una imatge al mateix temps que se suavitza el soroll present en la mateixa. Aquesta aplicació simultània aconseguix combinar dues operacions oposades per definició i superar així els inconvenients presentats per l'enfocament en dues etapes. / [EN] Computer vision is one of the fastest growing fields at present which, along with other technologies such as Biometrics or Big Data, has become the focus of interest of many research projects and it is considered one of the technologies of the future. This broad field includes a plethora of digital image processing and analysis tasks. To guarantee the success of image analysis and other high-level processing tasks as 3D imaging or pattern recognition, it is critical to improve the quality of the raw images acquired.
Nowadays all images are affected by different factors that hinder the achievement of optimal image quality, making digital image processing a fundamental step prior to the application of any other practical application. The most common of these factors are noise and poor acquisition conditions: noise artefacts hamper proper image interpretation of the image; and acquisition in
poor lighting or exposure conditions, such as dynamic scenes, causes loss of image information that can be key for certain processing tasks. Image (pre-) processing steps known as smoothing and sharpening are commonly applied to overcome these inconveniences: Smoothing is aimed at reducing noise and sharpening at improving or recovering imprecise or damaged information of image details and edges with insufficient sharpness or blurred content that prevents optimal image (post-)processing.
There are many methods for smoothing the noise in an image, however in many cases the filtering process causes blurring at the edges and details of the image. Besides, there are also many sharpening techniques, which try to combat the loss of information due to blurring of image texture and need to contemplate the existence of noise in the image they process. When dealing with a noisy image, any sharpening technique may amplify the noise. Although the intuitive idea to solve this last case would be the previous filtering and later sharpening, this approach has proved not to be optimal: the filtering could remove information that, in turn, may not be recoverable in the later sharpening step.
In the present PhD dissertation we propose a model based on graph theory for color image processing from a vector approach. In this model, a graph is built for each pixel in such a way that its features allow to characterize and classify the pixel. As we will show, the model we proposed is robust and versatile: potentially able to adapt to a variety of applications. In particular, we apply the model to create new solutions for the two fundamentals problems in image processing: smoothing and sharpening.
To approach high performance image smoothing we use the proposed model to determine if a pixel belongs to a at region or not, taking into account the need to achieve a high-precision classification even in the presence of noise. Thus, we build an adaptive soft-switching filter by employing the pixel classification to combine the outputs from a filter with high smoothing capability and a softer one to smooth edge/detail regions. Further, another application of our model allows to use pixels characterization to successfully perform a simultaneous smoothing and sharpening of color images. In this way, we address one of the classical challenges within the image processing field. We compare all the image processing techniques proposed with other state-of-the-art methods to show that they are competitive both from an objective (numerical) and visual evaluation point of view. / Pérez Benito, C. (2019). Color Image Processing based on Graph Theory [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/123955 / TESIS
|
5 |
Segmentação e quantificação de tecidos em imagens coloridas de úlceras de perna. / Segmentation and quantification of tissues in leg ulcers color imagesPerez, Andres Anobile 31 August 2001 (has links)
Neste trabalho foi desenvolvida uma metodologia de avaliação e monitoramento de pacientes com úlceras de perna baseada nas características dos tecidos internos dessas feridas. Os tecidos internos podem ser classificados como granulado, fibrina e necrosado, e a avaliação da área de cada um desses tecidos fornece para o clínico dados referentes ao estado da úlcera.A metodologia extrai essas informações a partir de imagens digitalizadas das lesões. Para tanto, a área referente à úlcera é segmentada e em seguida a área interna processada por uma rede neural, que tem o propósito de classificar cada ponto para um dos tecidos analisados. Os algoritmos desenvolvidos operam sobre imagens coloridas, já que cada tecido em uma imagem só pode ser identificado por sua cor. Este trabalho propõe ainda uma metodologia de extração de características das lesões através de uma forma não invasiva utilizando, para tanto, algoritmos de visão computacional. / The aim of this work was the development of a monitoring and evaluation methodology of leg ulcers patients based on the features of the inner tissues of these wounds. The internal tissues can be classified as granulation, slough and necrotic, and the evaluation of the area of each one of these tissues can be used by the specialist to help with the patient''s diagnosis. The methodology extracts these information from the wound digitized images. For this, the wound area is segmented and the inner region or the segmented area is processed by a neural network that classifies each point of the analyzed tissues. The developed algorithms operate on color images since each tissue in an image can only be analyzed by its colors. In this work has also proposed a feature extraction methodology of the wounds through a non-invasive way using computer vision algorithms.
|
6 |
Segmentação e quantificação de tecidos em imagens coloridas de úlceras de perna. / Segmentation and quantification of tissues in leg ulcers color imagesAndres Anobile Perez 31 August 2001 (has links)
Neste trabalho foi desenvolvida uma metodologia de avaliação e monitoramento de pacientes com úlceras de perna baseada nas características dos tecidos internos dessas feridas. Os tecidos internos podem ser classificados como granulado, fibrina e necrosado, e a avaliação da área de cada um desses tecidos fornece para o clínico dados referentes ao estado da úlcera.A metodologia extrai essas informações a partir de imagens digitalizadas das lesões. Para tanto, a área referente à úlcera é segmentada e em seguida a área interna processada por uma rede neural, que tem o propósito de classificar cada ponto para um dos tecidos analisados. Os algoritmos desenvolvidos operam sobre imagens coloridas, já que cada tecido em uma imagem só pode ser identificado por sua cor. Este trabalho propõe ainda uma metodologia de extração de características das lesões através de uma forma não invasiva utilizando, para tanto, algoritmos de visão computacional. / The aim of this work was the development of a monitoring and evaluation methodology of leg ulcers patients based on the features of the inner tissues of these wounds. The internal tissues can be classified as granulation, slough and necrotic, and the evaluation of the area of each one of these tissues can be used by the specialist to help with the patient''s diagnosis. The methodology extracts these information from the wound digitized images. For this, the wound area is segmented and the inner region or the segmented area is processed by a neural network that classifies each point of the analyzed tissues. The developed algorithms operate on color images since each tissue in an image can only be analyzed by its colors. In this work has also proposed a feature extraction methodology of the wounds through a non-invasive way using computer vision algorithms.
|
7 |
Three problems in computer vision: design, fabrication and analysis of paper sensors for detecting food contaminants, segmentation of food crystal images, and zero-shot action recognition in video sequences.Qiyue Liang (19349125) 09 August 2024 (has links)
<p dir="ltr">This dissertation delves into three projects within the realms of image processing, computer vision, and machine/deep learning. The primary objective of the first project is the detection of heavy metal particle concentrations using microfluidic paper-based devices. The second project revolves around the analysis of crystals within microscopic images. The third project centers around zero-shot action recognition in video sequences, utilizing a multi-modality deep learning framework that is refined through prompt tuning to enhance its performance.</p>
|
8 |
Extension de l'analyse multi-résolution aux images couleurs par transformées sur graphes / Extension of the multi-resolution analysis for color images by using graph transformsMalek, Mohamed 10 December 2015 (has links)
Dans ce manuscrit, nous avons étudié l’extension de l’analyse multi-résolution aux images couleurs par des transformées sur graphe. Dans ce cadre, nous avons déployé trois stratégies d’analyse différentes. En premier lieu, nous avons défini une transformée basée sur l’utilisation d’un graphe perceptuel dans l’analyse à travers la transformé en ondelettes spectrale sur graphe. L’application en débruitage d’image met en évidence l’utilisation du SVH dans l’analyse des images couleurs. La deuxième stratégie consiste à proposer une nouvelle méthode d’inpainting pour des images couleurs. Pour cela, nous avons proposé un schéma de régularisation à travers les coefficients d’ondelettes de la TOSG, l’estimation de la structure manquante se fait par la construction d’un graphe des patchs couleurs à partir des moyenne non locales. Les résultats obtenus sont très encourageants et mettent en évidence l’importance de la prise en compte du SVH. Dans la troisième stratégie, nous proposons une nouvelleapproche de décomposition d’un signal défini sur un graphe complet. Cette méthode est basée sur l’utilisation des propriétés de la matrice laplacienne associée au graphe complet. Dans le contexte des images couleurs, la prise en compte de la dimension couleur est indispensable pour pouvoir identifier les singularités liées à l’image. Cette dernière offre de nouvelles perspectives pour une étude approfondie de son comportement. / In our work, we studied the extension of the multi-resolution analysis for color images by using transforms on graphs. In this context, we deployed three different strategies of analysis. Our first approach consists of computing the graph of an image using the psychovisual information and analyzing it by using the spectral graph wavelet transform. We thus have defined a wavelet transform based on a graph with perceptual information by using the CIELab color distance. Results in image restoration highlight the interest of the appropriate use of color information. In the second strategy, we propose a novel recovery algorithm for image inpainting represented in the graph domain. Motivated by the efficiency of the wavelet regularization schemes and the success of the nonlocal means methods we construct an algorithm based on the recovery of information in the graph wavelet domain. At each step the damaged structure are estimated by computing the non local graph then we apply the graph wavelet regularization model using the SGWT coefficient. The results are very encouraging and highlight the use of the perceptual informations. In the last strategy, we propose a new approach of decomposition for signals defined on a complete graphs. This method is based on the exploitation of of the laplacian matrix proprieties of the complete graph. In the context of image processing, the use of the color distance is essential to identify the specificities of the color image. This approach opens new perspectives for an in-depth study of its behavior.
|
9 |
Caracterização de imagens de úlceras dermatológicas para indexação e recuperação por conteúdo / Characterization of dermatological ulcers images for indexing and content-based retrievalPereira, Silvio Moreto 01 November 2012 (has links)
Úlceras de pele são causadas devido à deficiência na circulação sanguínea. O diagnóstico é feito pela análise visual das regiões afetadas. A quantificação da distribuição de cores da lesão, por meio de técnicas de processamento de imagens pode auxiliar na caracterização e análise da dinâmica do processo patológico e resposta ao tratamento. O processamento de imagens de úlceras dermatológicas envolve etapas relacionadas a segmentação, caracterização e indexação. Esta análise é importante para classificação, recuperação de imagens similares e acompanhamento da evolução de uma lesão. Este trabalho apresenta um estudo sobre técnicas de segmentação e caracterização de imagens coloridas de úlceras de pele, baseadas nos modelos de cores RGB, HSV, L*a*b* e L*u*v*, utilizando suas componentes na extração de informações de textura e cor. Foram utilizadas técnicas de Aprendizado de Máquina e algoritmos matemáticos para a segmentação e extração de atributos, utilizando uma base de dados com 172 imagens. Nos testes de recuperação, foram utilizadas diferentes métricas de distância para avaliação do desempenho e técnicas de seleção de atributos. Os resultados obtidos evidenciam bom potencial para apoio ao diagnóstico e acompanhamento da evolução do tratamento com valores de até 75% de precisão para as técnicas de recuperação, 0,9 de área embaixo da curva receiver-operating-characteristic na classificação e 0,04 de erro médio quadrático entre a composição de cores da imagem segmentada automaticamente e a segmentada manualmente. Nos testes utilizando seleção de atributos, foi observado uma redução nos valores de precisão de recuperação (60%) e valores similares nos tetes de classificação (0,85). / Skin ulcers are caused due to deficiency in the bloodstream. The diagnosis is made by a visual analysis of the affected area. Quantification of color distribution of the lesion by image processing techniques can aid in the characterization and response to treatment. The image processing steps involves skin ulcers related to segmentation, characterization and indexing. This analysis is important for classification, image retrieval and similar tracking the evolution of an injury. This project presents a study of segmentation techniques and characterization of color images of dermatological skin ulcers, based on the color models RGB, HSV, L*a*b* and L*u*v*, using their components in the extraction of texture and color information. Were used Machine Learning techniques, mathematical algorithms for segmentation and extraction of attributes, using a database containing 172 images in two versions. In recovery tests were used different distance metrics for performance evaluation and techniques of features selection. The results show good potential to support the diagnosis and monitoring of treatment progress with values up to 75% precision in recovery techniques, 0.9 area under the curve receiver-operating-characteristic) in classification, and 0.04 mean square error between the color composition of the automatically segmented image and the manually segmented image. In tests utilizing feature selection was observed a decrease in precision values of image retrieval (60%) and similar values in the classification\'s tests (0.85).
|
10 |
Caracterização de imagens de úlceras dermatológicas para indexação e recuperação por conteúdo / Characterization of dermatological ulcers images for indexing and content-based retrievalSilvio Moreto Pereira 01 November 2012 (has links)
Úlceras de pele são causadas devido à deficiência na circulação sanguínea. O diagnóstico é feito pela análise visual das regiões afetadas. A quantificação da distribuição de cores da lesão, por meio de técnicas de processamento de imagens pode auxiliar na caracterização e análise da dinâmica do processo patológico e resposta ao tratamento. O processamento de imagens de úlceras dermatológicas envolve etapas relacionadas a segmentação, caracterização e indexação. Esta análise é importante para classificação, recuperação de imagens similares e acompanhamento da evolução de uma lesão. Este trabalho apresenta um estudo sobre técnicas de segmentação e caracterização de imagens coloridas de úlceras de pele, baseadas nos modelos de cores RGB, HSV, L*a*b* e L*u*v*, utilizando suas componentes na extração de informações de textura e cor. Foram utilizadas técnicas de Aprendizado de Máquina e algoritmos matemáticos para a segmentação e extração de atributos, utilizando uma base de dados com 172 imagens. Nos testes de recuperação, foram utilizadas diferentes métricas de distância para avaliação do desempenho e técnicas de seleção de atributos. Os resultados obtidos evidenciam bom potencial para apoio ao diagnóstico e acompanhamento da evolução do tratamento com valores de até 75% de precisão para as técnicas de recuperação, 0,9 de área embaixo da curva receiver-operating-characteristic na classificação e 0,04 de erro médio quadrático entre a composição de cores da imagem segmentada automaticamente e a segmentada manualmente. Nos testes utilizando seleção de atributos, foi observado uma redução nos valores de precisão de recuperação (60%) e valores similares nos tetes de classificação (0,85). / Skin ulcers are caused due to deficiency in the bloodstream. The diagnosis is made by a visual analysis of the affected area. Quantification of color distribution of the lesion by image processing techniques can aid in the characterization and response to treatment. The image processing steps involves skin ulcers related to segmentation, characterization and indexing. This analysis is important for classification, image retrieval and similar tracking the evolution of an injury. This project presents a study of segmentation techniques and characterization of color images of dermatological skin ulcers, based on the color models RGB, HSV, L*a*b* and L*u*v*, using their components in the extraction of texture and color information. Were used Machine Learning techniques, mathematical algorithms for segmentation and extraction of attributes, using a database containing 172 images in two versions. In recovery tests were used different distance metrics for performance evaluation and techniques of features selection. The results show good potential to support the diagnosis and monitoring of treatment progress with values up to 75% precision in recovery techniques, 0.9 area under the curve receiver-operating-characteristic) in classification, and 0.04 mean square error between the color composition of the automatically segmented image and the manually segmented image. In tests utilizing feature selection was observed a decrease in precision values of image retrieval (60%) and similar values in the classification\'s tests (0.85).
|
Page generated in 0.1174 seconds