An Examplar Based Video Inpainting using Dictionary Based Method

Inpainting is a skill of rebuilding lost or selected part from the image based on relatedor available information. Reconstruction of missing parts in videos is used extensively nowadays. A method for video inpainting usingexamplar-based inpainting is introduced in the system. The examplar based inpainting samples and copies best matching texture patches using texture synthesis. Matching patches are extracted from the known part of the frames from the video. Input frames are extracted and inpainted using examplar based method. For that dictionary is maintained which consists of legal patches. The input picture isinpainted several times with different parameters. Then it is combined and details are recovered to get the final inpainted video

Livrable D2.2 of the PERSEE project : Analyse/Synthese de Texture

Livrable D2.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D2.2 du projet. Son titre : Analyse/Synthese de Textur

Visualization of mobile mapping data via parallax scrolling

Visualizing big point-clouds, such as those derived from mobile mapping data, is not an easy task. Therefore many approaches have been proposed, based on either reducing the overall amount of data or the amount of data that is currently displayed to the user. Furthermore, an entirely free navigation within such a point-cloud is also not always intuitive using the usual input devices. This work proposes a visualization scheme for massive mobile mapping data inspired by a multiplane camera model also known as parallax scrolling. This technique, albeit entirely two-dimensional, creates a depth illusion by moving a number of overlapping partially transparent image layers at various speeds. The generation of such layered models from mobile mapping data greatly reduces the amount of data up to about 98% depending on the used image resolution. Finally, it is well suited for the panoramic-fashioned visualization of the environment of a moving car

Example based texture synthesis and quantification of texture quality

Textures have been used effectively to create realistic environments for virtual worlds by reproducing the surface appearances. One of the widely-used methods for creating textures is the example based texture synthesis method. In this method of generating a texture of arbitrary size, an input image from the real world is provided. This input image is used for the basis of generating large textures. Various methods based on the underlying pattern of the image have been used to create these textures; however, the problem of finding an algorithm which provides a good output is still an open research issue. Moreover, the process of determining the best of the outputs produced by the existing methods is a subjective one and requires human intervention. No quantification measure exists to do a relative comparison between the outputs. This dissertation addresses both problems using a novel approach. The dissertation also proposes an improved algorithm for image inpainting which yields better results than existing methods. Firstly, this dissertation presents a methodology which uses a HSI (hue, saturation, intensity) color model in conjunction with the hybrid approach to improve the quality of the synthesized texture. Unlike the RGB (red, green, blue) color model, the HSI color model is more intuitive and closer to human perception. The hue, saturation and intensity are better indicators than the three color channels used in the RGB model. They represent the exact way, in which the eye sees color in the real world. Secondly, this dissertation addresses the issue of quantifying the quality of the output textures generated using the various texture synthesis methods. Quantifying the quality of the output generated is an important issue and a novel method using statistical measures and a color autocorrelogram has been proposed. It is a two step method; in the first step a measure of the energy, entropy and similar statistical measures helps determine the consistency of the output texture. In the second step an autocorelogram is used to analyze color images as well and quantify them effectively. Finally, this disseratation prsesents a method for improving image inpainting. In the case of inpainting, small sections of the image missing due to noise or other similar reasons can be reproduced using example based texture synthesis. The region of the image immediately surrounding the missing section is treated as sample input. Inpainting can also be used to alter images by removing large sections of the image and filling the removed section with the image data from the rest of the image. For this, a maximum edge detector method is proposed to determine the correct order of section filling and produces significantly better results

Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications

People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non-uniform illumination. The four main applications of these image inpainting techniques are scratch removal, text removal, object removal and image restoration. However, all the four image inpainting applications cannot be implemented using a single technique. According to the literature, there has been relatively less work done in the field of image inpainting applications. Investigation has been carried out to find the suitability of these three techniques for the four above mentioned image inpainting applications based on two performance metrics