Vision-based production of personalized video

Kosmopoulos, Dimitrios; Doulamis, Anastasios; Makris, Alexandros; Doulamis, Nikolaos; Chatzis, Sotirios; Middleton, Stuart E.

doi:10.1016/j.image.2008.12.010

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…In [41], a content-based retrieval system was presented with the objective to recall all views (instances) of an object in a large database upon a query exploiting visual similarities. Video content-based retrieval was introduced in [42,43], while a mobile agent was presented in [44]. All the methods, however, need a query to get the retrievals, something which is not relevant in our approach.…”

Section: Content-based Filteringmentioning

confidence: 99%

Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images

et al. 2020

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This paper presents an approach for leveraging the abundance of images posted on social media like Twitter for large scale 3D reconstruction of cultural heritage landmarks. Twitter allows users to post short messages, including photos, describing a plethora of activities or events, e.g., tweets are used by travelers on vacation, capturing images from various cultural heritage assets. As such, a great number of images are available online, able to drive a successful 3D reconstruction process. However, reconstruction of any asset, based on images mined from Twitter, presents several challenges. There are three main steps that have to be considered: (i) tweets’ content identification, (ii) image retrieval and filtering, and (iii) 3D reconstruction. The proposed approach first extracts key events from unstructured tweet messages and then identifies cultural activities and landmarks. The second stage is the application of a content-based filtering method so that only a small but representative portion of cultural images are selected to support fast 3D reconstruction. The proposed methods are experimentally evaluated using real-world data and comparisons verify the effectiveness of the proposed scheme.

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Section: Content-based Filteringmentioning

confidence: 99%

Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images

et al. 2020

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“…This, in sequel, has boosted the amount, the complexity and the diversity of the digital media being captured, generated, processed, analyzed, and stored across heterogeneous and distributed media repositories and cloud infrastructures such as Picasa, and Flickr (Sevillano et al, 2012). This huge amount of multimedia content, which forms the so-called User Generated Content (UGC) (Li et al, 2018), can be exploited toward a better human-to-human interaction but also for a variety of new application domains in the broad fields of tourism, culture, leisure and entertainment (Kosmopoulos et al, 2009;Kim et al, 2014;Vishnevskaya et al, 2015). For instance, as stated by Ntalianis and Doulamis in (Ntalianis and Doulamis, 2016), the rich media content of the social media can be exploited to create personalized summaries of a human life making him/her "digitally perpetual" and leaving his/her mark in the world forever!…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D Reconstruction From Unstructured Videos Combining Video Summarization and Structure From Motion

Doulamis

2018

Front. ICT

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Social media and collection of large volumes of multimedia data such as images, videos and the accompanying text is of prime importance in today's society. This is stimulated by the power of the humans to communicate with one another. A useful paradigm of exploitation of such a huge amount of multimedia volumes is the 3D reconstruction and modeling of sites, historical cultural cities/regions or objects of interest from the short videos captured by simple users mainly for personal or touristic purposes. The main challenge in this research is the unstructured nature of the videos and the fact that they contain much information which is not related with the object the 3D model we ask for but for personal usage such as humans in front of the objects, weather conditions, etc. In this article, we propose an automatic scheme for 3D modeling/reconstruction of objects of interest by collecting pools of short duration videos that have been captured mainly for touristic purposes. Initially a video summarization algorithm is introduced using a discriminant Principal Component Analysis (d-PCA). The goal of this innovative scheme is to extract the frames so that bunches within each video cluster that contains videos of content referring to the same object present the maximum coherency of image data while content across bunches the minimum one. Experimental results on cultural objects indicate the efficiency of the proposed method to 3D reconstruct assets of interest using an unstructured image content information.

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“…Particle filter (PF) has attracted a great deal of attention and found very wide application in the field of visual tracking [1][2][3][4][5]. The key of PF [6] is to represent the posterior probability density function (PDF) of target state distribution by a set of random samples with associated weights, and compute the expectation as the estimation of target state.…”

Section: Introductionmentioning

confidence: 99%