508 research outputs found
Highlighting objects of interest in an image by integrating saliency and depth
Stereo images have been captured primarily for 3D reconstruction in the past.
However, the depth information acquired from stereo can also be used along with
saliency to highlight certain objects in a scene. This approach can be used to
make still images more interesting to look at, and highlight objects of
interest in the scene. We introduce this novel direction in this paper, and
discuss the theoretical framework behind the approach. Even though we use depth
from stereo in this work, our approach is applicable to depth data acquired
from any sensor modality. Experimental results on both indoor and outdoor
scenes demonstrate the benefits of our algorithm
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
A Global Nearest-Neighbour Depth Estimation-based Automatic 2D-to-3D Image and Video Conversion
The proposed work is to present a new method based on the radically different approach of learning the 2D-to-3D conversion from examples. It is based on lobally estimating the entire depth map of a query image directly from a repository of 3D images (image depth pairs or stereo pairs) using a nearest-neighbour regression type idea
A Global Nearest-Neighbour Depth Estimation-based Automatic 2D-to-3D Image and Video Conversion
The proposed work is to present a new method based on the radically different approach of learning the 2D-to-3D conversion from examples. It is based on lobally estimating the entire depth map of a query image directly from a repository of 3D images (image depth pairs or stereo pairs) using a nearest-neighbour regression type idea
Foot Detection Method for Footwear Augmented Reality Applications
Liitreaalsus on populaarsust koguv platvorm rĂ”ivaste ning aksessuaaride kasutamise visualiseerimiseks. Ideaalis vĂ”imaldab see kasutajatel proovida erinevaid riideid, jalatseid ja aksessuaare, kasutades ainult ĂŒht kaamerat ning sobivat rakendust, mis vĂ”imaldab kuvada erinevaid valikuid.\n\rJalatsite liitreaalsuses on palju erinevaid lahendusi, et pakkuda kasutajatele liitreaalsuse kogemust. Need lahendused kasutavad erinevaid meetodeid, nagu fikseeritud kaamera, muutumatu taust ja markerid jalgadel tuvastuse hĂ”lbustamiseks. Nende meetodite hulgas pole ĂŒkski kindlalt parem, lihtsam vĂ”i kiirem. Lisaks puudub tihtipeale avalikkusel ligipÀÀs arendatud rakendustele.\n\rKĂ€esolev magistritöö proovis leida universaalset lahendust, mis sobiks kasutamiseks kĂ”igi tulevaste jalatsite liitreaalsuse rakendustega.Augmented reality is gaining popularity as a technique for visualizing apparel usage. Ide-ally it allows users virtually to try out different clothes, shoes, and accessories, with only a camera and suitable application which encompasses different apparel choices.\n\rFocusing on augmented reality for footwear, there is a multitude of different solutions on how to offer the reality augmentation experience to the end users. These solutions employ different methods to deliver the end result, such as using fixed camera and constant back-ground or requiring markers on feet for detection. Among the variety of techniques used to approach the footwear reality augmentation, there is no single best, simplest, or fastest solution. The solutionsâ sources arenât usually even publicly available. \n\rThis thesis tries to come up with a solution for the footwear reality augmentation problem, which can be used as a base for any proceeding footwear augmented reality projects. This intentionally universal approach will be created by researching possible combinations of potential methods that can ensure a solutions regarding footwear reality augmentation. \n\rIn general, the idea behind this thesis work is to conduct a literature review about different techniques and come up with the best and robust algorithm or combination of methods that can be used for footwear augmented reality.\n\rA researched, documented, implemented and publicized solution would allow any upcom-ing footwear augmented reality related project to start working from an established base, therefore reducing time waste on already solved issues and possibly improving the quality of the end result.\n\rThe solution presented in this thesis is developed with focus on augmented reality applica-tions. The method is neither specific to any platform nor does it have heavy location re-quirements. The result is a foot detection algorithm, capable of working on commonly available hardware, which is beneficial for augmented reality application
Computer Assisted Relief Generation - a Survey
In this paper we present an overview of the achievements accomplished to date in the field of computer aided relief
generation. We delineate the problem, classify the different solutions, analyze similarities, investigate the evelopment and review the approaches according to their particular relative strengths and weaknesses. In consequence this survey is likewise addressed to researchers and artists through providing valuable insights into the theory behind the different concepts in this field and augmenting the options available among the methods presented with regard to practical application
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