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.

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.

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.

Trimap-free natural image matting problem is an important computer vision task in which we extract foreground objects from given images without extra trimap input. Compared with trimap-based matting algorithms, trimap-free algorithms are easier to make false detection when the foreground object is not well defined. To solve the problem, we design a novel structure (SegMatting) to handle foreground segmentation and alpha matte prediction simultaneously, which is able to produce high-quality mattes based on RGB inputs alone. This entangled structure enables information exchange between the binary segmentation task and the alpha matte prediction task interactively, and we further design a hybrid loss to adaptively balance two tasks during the multitask learning process. Additionally, we adopt a salient object detection dataset to pretrain our network so that we could obtain a more accurate foreground segment before our training process. Experiments indicate that the proposed SegMatting qualitatively and quantitatively outperforms most previous trimap-free models with a significant margin, while remains competitive among trimap-based methods. / Thesis / Master of Science in Electrical and Computer Engineering (MSECE)

La segmentation de personnes dans les images et les vidéos est une problématique actuellement au coeur de nombreux travaux. Nous nous intéressons à la segmentation de personnes debout. Pour cela, nous avons mis au point deux méthodes originales : La première est une continuation d'une méthode de détection efficace. On réalise une pré-segmentation en associant aux segments de contour de l'image une valeur de vraisemblance en tant qu'élément d'une silhouette humaine par une combinaison d'histogrammes de gradients orientés (HOG) et de machines à vecteurs de support (SVM) prises à l'échelle des ces segments. Une recherche d'arbre optimal dans un graphe intégrant les données de la pré-segmentation permet de reconstruire la silhouette de la personne. Enfin, une utilisation itérative de ce processus permet d'en améliorer la performance. La seconde méthode prend en compte l'interaction de l'utilisateur pour une image. Une coupe de graphe est guidée par un gabarit non binaire représentant une silhouette humaine. Nous proposons également un gabarit par parties pour s'adapter à la posture de la personne. Nous avons enfin transposé cette méthode à la segmentation de vidéos et la réalisation automatique de trimaps.

The thesis deals with an image processing problem called image matting. The problem involves detection of a foreground and background in an image with minimal user interaction using trimaps. Foreground detection is used in image composition. The goal of the thesis is to apply already known algorithms, in this case A Global sampling matting, in an Android application. The most important result is an intuitive application that can be used for making creative viral photos. Agile methodology is applied throughout the whole application development cycle. From the very beginning, the application is publicly available as a minimum viable product on Google play. The work’s contribution is in optimization of the mentioned algorithm for use in mobile devices and parallelization on a GPU, together with a publicly available application.