Redeye artifact is the most prevalent problem in the flash photography, especially using compact cameras with built-in flash, which bothers both amateur and professional photographers. Hence, removing the affected redeye pixels has become an important skill. This thesis work presents a completely automatic approach for the purpose of redeye detection and removal and it consists of two modules: detection and correction of the redeye pixels in an individual eye, detection of two red eyes in an individual face.This approach is considered as a combination of some of the previous attempts in the area of redeye removal together with some minor and major modifications and novel ideas. The detection procedure is based on the redness histogram analysis followed by two adaptive methods, general and specific approaches, in order to find a threshold point. The correction procedure is a four step algorithm which does not solely rely on the detected redeye pixels. It also applies some more pixel checking, such as enlarging the search area and neighborhood checking, to improve the reliability of the whole procedure by reducing the image degradation risk. The second module is based on a skin-likelihood detection algorithm. A completely novel approach which is utilizing the Golden Ratio in order to segment the face area into some specific regions is implemented in the second module. The proposed method in this thesis work is applied on more than 40 sample images; by considering some requirements and constrains, the achieved results are satisfactory.

Made available in DSpace on 2015-03-05T14:01:22Z (GMT). No. of bitstreams: 0 Previous issue date: 30 / Hewlett-Packard Brasil Ltda / Atualmente, existem diversos trabalhos com as mais variadas abordagens relativas ao processamento de imagens digitais para detecção de atividade de voz (VAD). As suas aplicações perpassam diferentes áreas, como por exemplo, comandos de voz em veículos e videoconferência. A motivação deste trabalho constitui-se na construção de um algoritmo que contribua para o aperfeiçoamento das técnicas de processamento de imagens aplicadas para a detecção de atividade de voz em vídeos. A problemática envolvida já apresenta uma grande diversidade de abordagens. No entanto, o foco deste trabalho situa-se na busca de alternativas para a melhoria na extração de um modelo de cor de pele e não-pele e, a partir daí, extrair um classificador para identificar a atividade de fala com mais precisão. Algoritmos já existentes de identificação de face e de classificação dos lábios foram utilizados e aprimorados. Através da criação de patches abaixo dos olhos, foi criado um modelo para determinar as características individuais de cor de / Currently, there are several works with many di_erent approaches to image processing for detection of voice activity (VAD). Its applications cross over di_erent areas, such as voice commands in vehicles and videoconferencing. The motivation of this work consists in building an algorithm that contributes to the improvement of techniques image processing applied to detect voice activity on video. The issue already presents a great diversity of approaches. However, the focus of this work lies in _nding alternatives to improve the extraction of a skin and non-skin color model and, from there, extract a classi_er to identify the activity of speech more accurately. Existing algorithms of face detection and classi_cation of the lips were used and improved. Through the creation of patches under the eyes, a model was created to determine the individual characteristics of skin color using the mean and standard deviation of the pixels of the patches and the mouth area. The results are presented based on two approaches.