DIGITAL INPAINTING ALGORITHMS AND EVALUATION

Digital inpainting is the technique of filling in the missing regions of an image or a video using information from surrounding area. This technique has found widespread use in applications such as restoration, error recovery, multimedia editing, and video privacy protection. This dissertation addresses three significant challenges associated with the existing and emerging inpainting algorithms and applications. The three key areas of impact are 1) Structure completion for image inpainting algorithms, 2) Fast and efficient object based video inpainting framework and 3) Perceptual evaluation of large area image inpainting algorithms. One of the main approach of existing image inpainting algorithms in completing the missing information is to follow a two stage process. A structure completion step, to complete the boundaries of regions in the hole area, followed by texture completion process using advanced texture synthesis methods. While the texture synthesis stage is important, it can be argued that structure completion aspect is a vital component in improving the perceptual image inpainting quality. To this end, we introduce a global structure completion algorithm for completion of missing boundaries using symmetry as the key feature. While existing methods for symmetry completion require a-priori information, our method takes a non-parametric approach by utilizing the invariant nature of curvature to complete missing boundaries. Turning our attention from image to video inpainting, we readily observe that existing video inpainting techniques have evolved as an extension of image inpainting techniques. As a result, they suffer from various shortcoming including, among others, inability to handle large missing spatio-temporal regions, significantly slow execution time making it impractical for interactive use and presence of temporal and spatial artifacts. To address these major challenges, we propose a fundamentally different method based on object based framework for improving the performance of video inpainting algorithms. We introduce a modular inpainting scheme in which we first segment the video into constituent objects by using acquired background models followed by inpainting of static background regions and dynamic foreground regions. For static background region inpainting, we use a simple background replacement and occasional image inpainting. To inpaint dynamic moving foreground regions, we introduce a novel sliding-window based dissimilarity measure in a dynamic programming framework. This technique can effectively inpaint large regions of occlusions, inpaint objects that are completely missing for several frames, change in size and pose and has minimal blurring and motion artifacts. Finally we direct our focus on experimental studies related to perceptual quality evaluation of large area image inpainting algorithms. The perceptual quality of large area inpainting technique is inherently a subjective process and yet no previous research has been carried out by taking the subjective nature of the Human Visual System (HVS). We perform subjective experiments using eye-tracking device involving 24 subjects to analyze the effect of inpainting on human gaze. We experimentally show that the presence of inpainting artifacts directly impacts the gaze of an unbiased observer and this in effect has a direct bearing on the subjective rating of the observer. Specifically, we show that the gaze energy in the hole regions of an inpainted image show marked deviations from normal behavior when the inpainting artifacts are readily apparent

Image Based View Synthesis

This dissertation deals with the image-based approach to synthesize a virtual scene using sparse images or a video sequence without the use of 3D models. In our scenario, a real dynamic or static scene is captured by a set of un-calibrated images from different viewpoints. After automatically recovering the geometric transformations between these images, a series of photo-realistic virtual views can be rendered and a virtual environment covered by these several static cameras can be synthesized. This image-based approach has applications in object recognition, object transfer, video synthesis and video compression. In this dissertation, I have contributed to several sub-problems related to image based view synthesis. Before image-based view synthesis can be performed, images need to be segmented into individual objects. Assuming that a scene can approximately be described by multiple planar regions, I have developed a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, correctly detect the occlusion pixels over multiple consecutive frames, and accurately segment the scene into several motion layers. First, a number of seed regions using correspondences in two frames are determined, and the seed regions are expanded and outliers are rejected employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, the occlusion order constraints on multiple frames are explored, which guarantee that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then the correct layer segmentation is obtained by using a graph cuts algorithm, and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust. Recovering the geometrical transformations among images of a scene is a prerequisite step for image-based view synthesis. I have developed a wide baseline matching algorithm to identify the correspondences between two un-calibrated images, and to further determine the geometric relationship between images, such as epipolar geometry or projective transformation. In our approach, a set of salient features, edge-corners, are detected to provide robust and consistent matching primitives. Then, based on the Singular Value Decomposition (SVD) of an affine matrix, we effectively quantize the search space into two independent subspaces for rotation angle and scaling factor, and then we use a two-stage affine matching algorithm to obtain robust matches between these two frames. The experimental results on a number of wide baseline images strongly demonstrate that our matching method outperforms the state-of-art algorithms even under the significant camera motion, illumination variation, occlusion, and self-similarity. Given the wide baseline matches among images I have developed a novel method for Dynamic view morphing. Dynamic view morphing deals with the scenes containing moving objects in presence of camera motion. The objects can be rigid or non-rigid, each of them can move in any orientation or direction. The proposed method can generate a series of continuous and physically accurate intermediate views from only two reference images without any knowledge about 3D. The procedure consists of three steps: segmentation, morphing and post-warping. Given a boundary connection constraint, the source and target scenes are segmented into several layers for morphing. Based on the decomposition of affine transformation between corresponding points, we uniquely determine a physically correct path for post-warping by the least distortion method. I have successfully generalized the dynamic scene synthesis problem from the simple scene with only rotation to the dynamic scene containing non-rigid objects. My method can handle dynamic rigid or non-rigid objects, including complicated objects such as humans. Finally, I have also developed a novel algorithm for tri-view morphing. This is an efficient image-based method to navigate a scene based on only three wide-baseline un-calibrated images without the explicit use of a 3D model. After automatically recovering corresponding points between each pair of images using our wide baseline matching method, an accurate trifocal plane is extracted from the trifocal tensor implied in these three images. Next, employing a trinocular-stereo algorithm and barycentric blending technique, we generate an arbitrary novel view to navigate the scene in a 2D space. Furthermore, after self-calibration of the cameras, a 3D model can also be correctly augmented into this virtual environment synthesized by the tri-view morphing algorithm. We have applied our view morphing framework to several interesting applications: 4D video synthesis, automatic target recognition, multi-view morphing

Video inpainting for non-repetitive motion

Master'sMASTER OF SCIENC

Multiple View Geometry For Video Analysis And Post-production

Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data

Novel Video Completion Approaches and Their Applications

Video completion refers to automatically restoring damaged or removed objects in a video sequence, with applications ranging from sophisticated video removal of undesired static or dynamic objects to correction of missing or corrupted video frames in old movies and synthesis of new video frames to add, modify, or generate a new visual story. The video completion problem can be solved using texture synthesis and/or data interpolation to fill-in the holes of the sequence inward. This thesis makes a distinction between still image completion and video completion. The latter requires visually pleasing consistency by taking into account the temporal information. Based on their applied concepts, video completion techniques are categorized as inpainting and texture synthesis. We present a bandlet transform-based technique for each of these categories of video completion techniques. The proposed inpainting-based technique is a 3D volume regularization scheme that takes advantage of bandlet bases for exploiting the anisotropic regularities to reconstruct a damaged video. The proposed exemplar-based approach, on the other hand, performs video completion using a precise patch fusion in the bandlet domain instead of patch replacement. The video completion task is extended to two important applications in video restoration. First, we develop an automatic video text detection and removal that benefits from the proposed inpainting scheme and a novel video text detector. Second, we propose a novel video super-resolution technique that employs the inpainting algorithm spatially in conjunction with an effective structure tensor, generated using bandlet geometry. The experimental results show a good performance of the proposed video inpainting method and demonstrate the effectiveness of bandlets in video completion tasks. The proposed video text detector and the video super resolution scheme also show a high performance in comparison with existing methods

Enforcing Realism and Temporal Consistency for Large-Scale Video Inpainting

Today, people are consuming more videos than ever before. At the same time, video manipulation has rapidly been gaining traction due to the influence of viral videos, as well as the convenience of editing software. Although video manipulation has legitimate entertainment purposes, it can also be incredibly destructive. In order to understand the positive and negative consequences of media manipulation---as well as to maintain the integrity of mass media---it is important to investigate the capabilities of video manipulation techniques. In this dissertation, we focus on the manipulation task of video inpainting, where the goal is to automatically fill in missing parts of a masked video with semantically relevant content. Inpainting results should possess high visual quality with respect to reconstruction performance, realism, and temporal consistency, i.e., they should faithfully recreate missing contents in a way that resembles the real world and exhibits minimal flickering artifacts. Two major challenges have impeded progress toward improving visual quality: semantic ambiguity and diagnostic evaluation. Semantic ambiguity exists for any masked video due to several plausible explanations of the events in the observed scene; however, prior methods have struggled with ambiguity due to their limited temporal contexts. As for diagnostic evaluation, prior work has overemphasized aggregate analysis on large datasets and underemphasized fine-grained analysis on modern inpainting failure modes; as a result, the expected behaviors of models under specific scenarios have remained poorly understood. Our work improves on both models and evaluation techniques for video inpainting, thereby providing deeper insight into how an inpainting model's design impacts the visual quality of its outputs. To advance state-of-the-art in video inpainting, we propose two novel solutions that improve visual quality by expanding the available temporal context. Our first approach, bi-TAI, intelligently integrates information from multiple frames before and after the desired sequence. It produces more realistic results than prior work, which could only consume limited contextual information. Our second approach, HyperCon, suppresses flickering artifacts from frame-wise processing by identifying and propagating consistencies found in high frame-rate space; we successfully apply it to tasks as disparate as video inpainting and style transfer. Aside from methodological improvements, we also propose two novel evaluation tools to diagnose failure modes of modern video inpainting methods. Our first such contribution is the Moving Symbols dataset, which we use to characterize the sensitivity of a state-of-the-art video prediction model to controllable appearance and motion parameters. Our second contribution is the DEVIL benchmark, which provides a dataset and a comprehensive evaluation scheme to quantify how several semantic properties of the input video and mask affect video inpainting quality. Through models that exploit temporal context---as well as evaluation paradigms that reveal fine-grained failure modes of modern inpainting methods at scale---our contributions enforce better visual quality for video inpainting on a larger scale than prior work. We enable the production of more convincing manipulated videos for data processing and social media needs; we also establish replicable fine-grained analysis techniques to cultivate future progress in the field.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169785/1/szetor_1.pd

Approches de remplissage automatique de trous à l'intérieur d'images et de séquences vidéo

À notre époque, les images et les séquences vidéo destinées au cinéma ou à la télévision sont fréquemment altérées durant l’étape de postproduction afin d’effectuer le remplissage de régions indésirables. Par exemple, les graffitis à caractères haineux présents dans une image sont supprimés. Pour produire un résultat de qualité, il est important que le remplissage ait une apparence réaliste et qu’il présente des signes d’usure. Les méthodes actuelles traitant de ce problème ne sont pas adaptées puisqu’elles utilisent des paramètres peu intuitifs et qu’elles traitent généralement d’un seul effet de détérioration. Le remplissage peut aussi se faire sur une séquence vidéo dans laquelle la perche de son a malencontreusement été filmée. Le remplissage de régions manquantes dans une séquence vidéo pose des défis additionnels, comme la cohérence spatio-temporelle et la grande quantité d’information à traiter, et les approches actuelles sont inadaptées. En effet, la plupart des méthodes, dont celles basées sur les champs aléatoires de Markov, ne peuvent traiter directement la haute résolution dans un délai raisonnable. De plus, les méthodes actuelles sont limitées par le type de mouvement de caméra, la taille des régions indésirables et la variation de l’intensité lumineuse. Un objectif de cette thèse est de développer un système de remplissage qui permet la génération d’effets de détérioration basé sur une image échantillon contenant un exemple de l’effet voulu. Pour y arriver, une approche de synthèse de textures par remplissage de trous qui ne comporte aucun paramètre complexe à manipuler par l’artiste et qui permet de reproduire de nouveaux effets similaires est introduite. Un deuxième objectif est l’élaboration d’un système de remplissage de régions manquantes de séquences vidéo de haute définition. Un algorithme de synthèse de textures par remplissage de trous est adapté en tirant profit du principe de la cohérence et d’une recherche locale. De plus, le dernier volet de la thèse présente une approche de remplissage basée sur le suivi de caractéristiques invariantes permettant de compléter de très grandes régions manquantes provenant de séquences vidéo filmées avec des mouvements de caméra non-triviaux. Les résultats obtenus à partir des différentes contributions du projet de recherche montrent un réalisme accru lors du remplissage de régions manquantes d’images et de séquences vidéo. Les différentes méthodes sont faciles d’utilisation et intuitives puisqu’elles ne possèdent aucun paramètre complexe à spécifier par l’artiste. De plus, elles s’intègrent bien dans le processus itératif de création de ce dernier. Finalement, les petits temps de calculs rendent faciles leur intégration dans le pipeline de production des studios

Motion Layer Based Object Removal In Videos

This paper proposes a novel method to generate plausible video sequences after removing relatively large objects from the original videos. In order to maintain temporal coherence among the frames, a motion layer segmentation method is applied. Then, a set of synthesized layers are generated by applying motion compensation and region completion algorithm. Finally, a new video, in which the selected object is removed, is plausibly rendered given the synthesized layers and the motion parameters. A number of example videos are shown in the results to demonstrate the effectiveness of our method