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Improved Sampling-based Alpha Matting in Images and VideoHao, Chengcheng 18 October 2012 (has links)
Foreground extraction technology plays an important role in image and video processing tasks. It has been widely used in various industries. To better describe the overlap relationship between foreground and background, alpha channel is introduced. It reveals the opacity property of foreground objects. Thus, fully extracting a foreground object requires determining the alpha values for pixels, also known as extracting an alpha matte. In this thesis, we propose an improved sampling-based alpha matting algorithm, which is capable of generating high quality matting results. By analyzing the weakness of previous approaches, we optimize the sampling process and consider the cost of each sample pair to avoid missing any good samples. The good performance is demonstrated even for complex images. On the other hand, extracting foreground objects from video sequences is a more challenging task since it has higher demands on accuracy and efficiency. Previous approaches usually require a significant amount of user input and the results still suffer from inaccuracy. In this thesis, we successfully extend our algorithm to video sequences and let it run in an automatic fashion. Adaptive trimap, which is vital for matting, can be automatically generated and properly propagated in this system. Our method not only reduces the user interference but also guarantees the matting quality.
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Improved Sampling-based Alpha Matting in Images and VideoHao, Chengcheng 18 October 2012 (has links)
Foreground extraction technology plays an important role in image and video processing tasks. It has been widely used in various industries. To better describe the overlap relationship between foreground and background, alpha channel is introduced. It reveals the opacity property of foreground objects. Thus, fully extracting a foreground object requires determining the alpha values for pixels, also known as extracting an alpha matte. In this thesis, we propose an improved sampling-based alpha matting algorithm, which is capable of generating high quality matting results. By analyzing the weakness of previous approaches, we optimize the sampling process and consider the cost of each sample pair to avoid missing any good samples. The good performance is demonstrated even for complex images. On the other hand, extracting foreground objects from video sequences is a more challenging task since it has higher demands on accuracy and efficiency. Previous approaches usually require a significant amount of user input and the results still suffer from inaccuracy. In this thesis, we successfully extend our algorithm to video sequences and let it run in an automatic fashion. Adaptive trimap, which is vital for matting, can be automatically generated and properly propagated in this system. Our method not only reduces the user interference but also guarantees the matting quality.
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Improved Sampling-based Alpha Matting in Images and VideoHao, Chengcheng January 2012 (has links)
Foreground extraction technology plays an important role in image and video processing tasks. It has been widely used in various industries. To better describe the overlap relationship between foreground and background, alpha channel is introduced. It reveals the opacity property of foreground objects. Thus, fully extracting a foreground object requires determining the alpha values for pixels, also known as extracting an alpha matte. In this thesis, we propose an improved sampling-based alpha matting algorithm, which is capable of generating high quality matting results. By analyzing the weakness of previous approaches, we optimize the sampling process and consider the cost of each sample pair to avoid missing any good samples. The good performance is demonstrated even for complex images. On the other hand, extracting foreground objects from video sequences is a more challenging task since it has higher demands on accuracy and efficiency. Previous approaches usually require a significant amount of user input and the results still suffer from inaccuracy. In this thesis, we successfully extend our algorithm to video sequences and let it run in an automatic fashion. Adaptive trimap, which is vital for matting, can be automatically generated and properly propagated in this system. Our method not only reduces the user interference but also guarantees the matting quality.
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Alpha Matting via Residual Convolutional Grid NetworkZhang, Huizhen 23 July 2019 (has links)
Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and image synthesis. The conventional approaches for alpha matting perform unsatisfactorily when they encounter complicated background and foreground. It is also difficult for them to extract alpha matte accurately when the foreground objects are transparent, semi-transparent, perforated or hairy. Fortunately, the rapid development of deep learning techniques brings new possibilities for solving alpha matting problems.
In this thesis, we propose a residual convolutional grid network for alpha matting, which is based on the convolutional neural networks (CNNs) and can learn the alpha matte directly from the original image and its trimap. Our grid network consists of horizontal residual convolutional computation blocks and vertical upsampling/downsampling convolutional computation blocks. By choosing different paths to pass information by itself, our network can not only retain the rich details of the image but also extract high-level abstract semantic information of the image. The experimental results demonstrate that our method can solve the matting problems that plague conventional matting methods for decades and outperform all the other state-of-the-art matting methods in quality and visual evaluation. The only matting method performs a little better than ours is the current best matting method. However, that matting method requires three times amount of trainable parameters compared with ours. Hence, our matting method is the best considering the computation complexity, memory usage, and matting performance.
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Chroma Keying Based on Stereo ImagesChu, Mengdie January 2017 (has links)
This thesis proposes a novel chroma keying method based on stereo images, which can be applied to post-process the alpha matte generated by any existing matting approach. Given a pair of stereo images, a new matting Laplacian matrix is constructed based on the affinities between matching pixels and their neighbors from two frames. Based on the new matting Laplacian matrix, a new cost function is also formulated to estimate alpha values of the reference image through the propagation between stereo images.
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AMMNet: an Attention-based Multi-scale Matting NetworkNiu, Chenxiao January 2019 (has links)
Matting, which aims to separate the foreground object from the background of an image, is an important problem in computer vision. Most existing methods rely on auxiliary information such as trimaps or scibbles to alleviate the difficulty arising from the underdetermined nature of the matting problem. However, such methods tend to be sensitive to the quality of auxiliary information, and are unsuitable for real-time deployment. In this paper, we propose a novel Attention-based Multi-scale Matting Network (AMMNet), which can estimate the alpha matte from a given RGB image without resorting to any auxiliary information. The proposed AMMNet consists of three (sub-)networks: 1) a multi-scale neural network designed to provide the semantic information of the foreground object, 2) a Unet-like network for attention mask generation, and 3) a Convolutional Neural Network (CNN) customized to integrate high- and low-level features extracted by the first two (sub-)networks. The AMMNet is generic in nature and can be trained end-to-end in a straightforward manner. The experimental results indicate that the performance of AMMNet is competitive against the state-of-the-art matting methods, which either require additional side information or are tailored to images with a specific type of content (e.g., portrait). / Thesis / Master of Applied Science (MASc)
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SEGMENTAÇÃO DE VÍDEOS PARA STORYTELLING INTERATIVO BASEADO EM VÍDEO / VIDEO SEGMENTATION FOR VIDEO-BASED INTERACTIVE STORYTELLINGSchetinger, Victor Chitolina 28 February 2013 (has links)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Video-based interactive storytelling has as its main goal the generation of interactive narratives,
allowing users to control the course of the story and using cinematographic compositions
to dramatize it. For this process to be possible, there is a need for large amounts of cinematographic
content in the form of filmed scenes. This content, on the other hand, has to be properly
pre-processed in order to be usable. This work proposes an approach for video segmentation
aimed for video-based interactive storytelling, using alpha matting to extract color and transparency
of video elements to be later recomposed for dramatization purposes. The developed
solution uses background subtraction and energy minimization techniques to automatically generate
trimaps. / O storytelling interativo baseado em vídeo tem como objetivo a geração de narrativas,
permitindo o controle de um usuário sobre o rumo da estória e utilizando composições cinematográficas
para dramatizá-la. Para que este processo seja possível, existe a necessidade de uma
grande quantidade de conteúdo cinematográfico na forma de filmagens. Este conteúdo, por sua
vez, precisa ser adequadamente pré-processado para permitir sua utilização adequada. Nesse
trabalho, uma abordagem para segmentação de vídeos para storytelling interativo baseado em
vídeo é proposta, utilizando alpha matting para extrair informações de cor e transparência de
elementos de vídeos para serem reutilizados em processos de dramatização. A solução desenvolvida
utiliza técnicas de subtração de fundo e minimização de energia para gerar trimaps de
forma automática.
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Color Range Determination and Alpha Matting for Color ImagesLuo, Zhenyi 02 November 2011 (has links)
This thesis proposes a new chroma keying method that can automatically
detect background, foreground, and unknown regions. For background color
detection, we use K-means clustering in color space to calculate the limited
number of clusters of background colors. We use spatial information to clean
the background regions and minimize the unknown regions. Our method only
needs minimum inputs from user.
For unknown regions, we implement the alpha matte based on Wang's robust
matting algorithm, which is considered one of the best algorithms in the
literature, if not the best. Wang's algorithm is based on modified random walk.
We proposed a better color selection method, which improves matting results
in the experiments. In the thesis, a detailed implementation of robust
matting is provided.
The experimental results demonstrate that our proposed method can handle
images with one background color, images with gridded background, and images
with difficult regions such as complex hair stripes and semi-transparent
clothes.
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Color Range Determination and Alpha Matting for Color ImagesLuo, Zhenyi 02 November 2011 (has links)
This thesis proposes a new chroma keying method that can automatically
detect background, foreground, and unknown regions. For background color
detection, we use K-means clustering in color space to calculate the limited
number of clusters of background colors. We use spatial information to clean
the background regions and minimize the unknown regions. Our method only
needs minimum inputs from user.
For unknown regions, we implement the alpha matte based on Wang's robust
matting algorithm, which is considered one of the best algorithms in the
literature, if not the best. Wang's algorithm is based on modified random walk.
We proposed a better color selection method, which improves matting results
in the experiments. In the thesis, a detailed implementation of robust
matting is provided.
The experimental results demonstrate that our proposed method can handle
images with one background color, images with gridded background, and images
with difficult regions such as complex hair stripes and semi-transparent
clothes.
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Color Range Determination and Alpha Matting for Color ImagesLuo, Zhenyi 02 November 2011 (has links)
This thesis proposes a new chroma keying method that can automatically
detect background, foreground, and unknown regions. For background color
detection, we use K-means clustering in color space to calculate the limited
number of clusters of background colors. We use spatial information to clean
the background regions and minimize the unknown regions. Our method only
needs minimum inputs from user.
For unknown regions, we implement the alpha matte based on Wang's robust
matting algorithm, which is considered one of the best algorithms in the
literature, if not the best. Wang's algorithm is based on modified random walk.
We proposed a better color selection method, which improves matting results
in the experiments. In the thesis, a detailed implementation of robust
matting is provided.
The experimental results demonstrate that our proposed method can handle
images with one background color, images with gridded background, and images
with difficult regions such as complex hair stripes and semi-transparent
clothes.
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