scholarly journals Exemplar-based Video Inpainting for Occluded Objects

2013 ◽  
Vol 81 (13) ◽  
pp. 28-30
Author(s):  
Vaishali U.Gaikwad ◽  
P. V. kulkarni
Author(s):  
Sameh Zarif ◽  
Mina Ibrahim

Reconstructing and repairing of corrupted or missing parts after object removal in digital video is an important trend in artwork restoration. Video inpainting is an active subject in video processing, which deals with the recovery of the corrupted or missing data. Most previous video inpainting approaches consume more time in extensive search to find the best patch to restore the damaged frames. In addition to that, most of them cannot handle the gradual and sudden illumination changes, dynamic background, full object occlusion, and object changes in scale. In this paper, we present a complete video inpainting framework without the extensive search process. The proposed framework consists of a segmentation stage based on low-resolution version and background subtraction. A background inpainting stage is applied to restore the damaged background regions after static or moving object removal based on the gray-level co-occurrence matrix (GLCM). A foreground inpainting stage is based on objects repository. GLCM is used to complete the moving occluded objects during the occlusion. The proposed method reduces the inpainting time from hours to a few seconds and maintains the spatial and temporal consistency. It works well when the background has clutter or fake motion, and it can handle the changes in object size and in posture. Moreover, it is able to handle full occlusion and illumination changes.


2005 ◽  
Author(s):  
Kedar A. Patwardhan ◽  
Guillermo Sapiro ◽  
Marcelo Bertalmio

Perception ◽  
1995 ◽  
Vol 24 (11) ◽  
pp. 1333-1364 ◽  
Author(s):  
Lothar Spillmann ◽  
Birgitta Dresp

The study of illusory brightness and contour phenomena has become an important tool in modern brain research. Gestalt, cognitive, neural, and computational approaches are reviewed and their explanatory powers are discussed in the light of empirical data. Two well-known phenomena of illusory form are dealt with, the Ehrenstein illusion and the Kanizsa triangle. It is argued that the gap between the different levels of explanation, bottom—up versus top—down, creates scientific barriers which have all too often engendered unnecessary debate about who is right and who is wrong. In this review of the literature we favour an integrative approach to the question of how illusory form is derived from stimulus configurations which provide the visual system with seemingly incomplete information. The processes that can explain the emergence of these phenomena range from local feature detection to global strategies of perceptual organisation. These processes may be similar to those that help us restore partially occluded objects in everyday vision. To understand better the Ehrenstein and Kanizsa illusions, it is proposed that different levels of analysis and explanation are not mutually exclusive, but complementary. Theories of illusory contour and form perception must, therefore, take into account the underlying neurophysiological mechanisms and their possible interactions with cognitive and attentional processes.


2006 ◽  
Vol 18 (6) ◽  
pp. 1441-1471 ◽  
Author(s):  
Christian Eckes ◽  
Jochen Triesch ◽  
Christoph von der Malsburg

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.


Perception ◽  
10.1068/p6158 ◽  
2009 ◽  
Vol 38 (2) ◽  
pp. 200-214 ◽  
Author(s):  
Janneke Lommertzen ◽  
Rob van Lier ◽  
Ruud G J Meulenbroek

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