Automatic objects removal for scene completion

Abstract

With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as 3D scene reconstruction and other big data applications. However, this is not an easy task due to the fact the retrieved photos are neither aligned nor calibrated. Furthermore, with the occlusion of unexpected foreground objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structurebased image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to the natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing: 3D scene reconstruction and location recognition.

Background subtraction under sudden illumination change

“…The simplicity of approach, however, should allow for real time operation, which is a part of our future work. In addition, our BMB can further be improved in 1 http://vis.uky.edu/nsf-privacy/anonymity-in-video-surveillance/ two ways, firstly, algorithms such as [22] can allow us to make BMB even when images without foreground are not available and secondly BMB is currently static and we intend to make it adaptive. It would naturally help to solve moved object problem.…”

Efficient Poisson Image Editing

“…It is the process of combining two images by copying the gradients of the pixels from the source image into the target image. It is used in many applications such as seamless cloning [14][15][16][17][18][19][20][21][22], seamless video editing [23][24], seamless stitching [25][26][27], shadow removal [28], inpainting [29][30][31], gradient domain painting [32,33], scene completion [34,35], and image smoothing [36][37][38]. Some of gradient domain methods compose images by solving the Poisson equation in the specified region.…”

An interprojection sensor fusion approach to estimate blocked projection signal in synchronized moving grid-based CBCT system

“…5,7 The interpolation approach has the advantage of reducing imaging dose by half. Many inpainting techniques, including texture synthesis, partial differential equation, and exemplar based techniques as well as their hybrids [9][10][11][12] can be used for interpolation. However, these approaches usually cannot recover sufficient information in the blocked regions, especially when wider grids are used.…”