16,912 research outputs found
Markov mezĆk a kĂ©pmodellezĂ©sben, alkalmazĂĄsuk az automatikus kĂ©pszegmentĂĄlĂĄs terĂŒletĂ©n = Markovian Image Models: Applications in Unsupervised Image Segmentation
1) KifejlesztettĂŒnk egy olyan szĂn Ă©s textĂșra alapĂș szegmentĂĄlĂł MRF algoritmust, amely alkalmas egy kĂ©p automatikus szegmentĂĄlĂĄsĂĄt elvĂ©gezni. Az eredmĂ©nyeinket az Image and Vision Computing folyĂłiratban publikĂĄltuk. 2) KifejlesztettĂŒnk egy Reversible Jump Markov Chain Monte Carlo technikĂĄn alapulĂł automatikus kĂ©pszegmentĂĄlĂł eljĂĄrĂĄst, melyet sikeresen alkalmaztunk szĂnes kĂ©pek teljesen automatikus szegmentĂĄlĂĄsĂĄra. Az eredmĂ©nyeinket a BMVC 2004 konferenciĂĄn Ă©s az Image and Vision Computing folyĂłiratban publikĂĄltuk. 3) A modell többrĂ©tegƱ tovĂĄbbfejlesztĂ©sĂ©t alkalmaztuk video objektumok szĂn Ă©s mozgĂĄs alapĂș szegmentĂĄlĂĄsĂĄra, melynek eredmĂ©nyeit a HACIPPR 2005 illetve az ACCV 2006 nemzetközi konferenciĂĄkon publikĂĄltuk. SzintĂ©n ehhez az alapproblĂ©mĂĄhoz kapcsolĂłdik HorvĂĄth PĂ©ter hallgatĂłmmal az optic flow szamĂtĂĄsĂĄval illetve szĂn, textĂșra Ă©s mozgĂĄs alapĂș GVF aktĂv kontĂșrral kapcsoltos munkĂĄink. TDK dolgozata elsĆ helyezĂ©st Ă©rt el a 2004-es helyi versenyen, az eredmĂ©nyeinket pedig a KEPAF 2004 konferenciĂĄn publikĂĄltuk. 4) HorvĂĄth PĂ©ter PhD hallgatĂłmmal illetve az franciaorszĂĄgi INRIA Ariana csoportjĂĄval, kidolgoztunk egy olyan kĂ©pszegmentĂĄlĂł eljĂĄrĂĄst, amely a szegmentĂĄlandĂł objektum alakjĂĄt is figyelembe veszi. Az eredmĂ©nyeinket az ICPR 2006 illetve az ICCVGIP 2006 konferenciĂĄn foglaltuk össze. A modell elĆzmĂ©nyekĂ©nt kidolgoztunk tovĂĄbbĂĄ egy alakzat-momemntumokon alapulĂł aktĂv kontĂșr modellt, amelyet a HACIPPR 2005 konferenciĂĄn publikĂĄltunk. | 1) We have proposed a monogrid MRF model which is able to combine color and texture features in order to improve the quality of segmentation results. We have also solved the estimation of model parameters. This work has been published in the Image and Vision Computing journal. 2) We have proposed an RJMCMC sampling method which is able to identify multi-dimensional Gaussian mixtures. Using this technique, we have developed a fully automatic color image segmentation algorithm. Our results have been published at BMVC 2004 international conference and in the Image and Vision Computing journal. 3) A new multilayer MRF model has been proposed which is able to segment an image based on multiple cues (such as color, texture, or motion). This work has been published at HACIPPR 2005 and ACCV 2006 international conferences. The work on optic flow computation and color-, texture-, and motion-based GVF active contours doen with my student, Mr. Peter Horvath, won a first price at the local Student Research Competition in 2004. Results have been presented at KEPAF 2004 conference. 4) A new shape prior, called 'gas of circles' has been introduced using active contour models. This work is done in collaboration with the Ariana group of INRIA, France and my PhD student, Mr. Peter Horvath. Results are published at the ICPR 2006 and ICCVGIP 2006 conferences. A preliminary study on active contour models using shape-moments has also been done, these results are published at HACIPPR 2005
GPU-based Image Analysis on Mobile Devices
With the rapid advances in mobile technology many mobile devices are capable
of capturing high quality images and video with their embedded camera. This
paper investigates techniques for real-time processing of the resulting images,
particularly on-device utilizing a graphical processing unit. Issues and
limitations of image processing on mobile devices are discussed, and the
performance of graphical processing units on a range of devices measured
through a programmable shader implementation of Canny edge detection.Comment: Proceedings of Image and Vision Computing New Zealand 201
Fusing image representations for classification using support vector machines
In order to improve classification accuracy different image representations
are usually combined. This can be done by using two different fusing schemes.
In feature level fusion schemes, image representations are combined before the
classification process. In classifier fusion, the decisions taken separately
based on individual representations are fused to make a decision. In this paper
the main methods derived for both strategies are evaluated. Our experimental
results show that classifier fusion performs better. Specifically Bayes belief
integration is the best performing strategy for image classification task.Comment: Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th
International Conference, Wellington : Nouvelle-Z\'elande (2009
Cryptanalysis of image encryption with compound chaotic sequence
Recently, an image encryption algorithm based on compound chaotic sequence was proposed [Tong et al., Image and Vision Computing 26 (2008) 843]. In this paper, we analyze the security weaknesses of the proposal. We give chosen-plaintext and known-plaintext attacks that yield the secret parameters of the algoritm. Our simulation results show that the computational complexity of the attacks is quite low.Publisher's VersionAuthor's Post-prin
ROBUSfT: Robust Real-Time Shape-from-Template, a C++ Library
Tracking the 3D shape of a deforming object using only monocular 2D vision is
a challenging problem. This is because one should (i) infer the 3D shape from a
2D image, which is a severely underconstrained problem, and (ii) implement the
whole solution pipeline in real-time. The pipeline typically requires feature
detection and matching, mismatch filtering, 3D shape inference and feature
tracking algorithms. We propose ROBUSfT, a conventional pipeline based on a
template containing the object's rest shape, texturemap and deformation law.
ROBUSfT is ready-to-use, wide-baseline, capable of handling large deformations,
fast up to 30 fps, free of training, and robust against partial occlusions and
discontinuity in video frames. It outperforms the state-of-the-art methods in
challenging datasets. ROBUSfT is implemented as a publicly available C++
library and we provide a tutorial on how to use it in
https://github.com/mrshetab/ROBUSfTComment: This is the arXiv version of an article published in Image and Vision
Computing. Please cite the accepted version: M. Shetab-Bushehri, M. Aranda,
E. Ozgur, Y. Mezouar and Adrien Bartoli "ROBUSfT: Robust Real-Time
Shape-from-Template, a C++ Library," in Image and Vision Computing, doi:
10.1016/j.imavis.2023.10486
Learning to detect video events from zero or very few video examples
In this work we deal with the problem of high-level event detection in video.
Specifically, we study the challenging problems of i) learning to detect video
events from solely a textual description of the event, without using any
positive video examples, and ii) additionally exploiting very few positive
training samples together with a small number of ``related'' videos. For
learning only from an event's textual description, we first identify a general
learning framework and then study the impact of different design choices for
various stages of this framework. For additionally learning from example
videos, when true positive training samples are scarce, we employ an extension
of the Support Vector Machine that allows us to exploit ``related'' event
videos by automatically introducing different weights for subsets of the videos
in the overall training set. Experimental evaluations performed on the
large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness
of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for
publicatio
A Tool for Integer Homology Computation: Lambda-At Model
In this paper, we formalize the notion of lambda-AT-model (where is
a non-null integer) for a given chain complex, which allows the computation of
homological information in the integer domain avoiding using the Smith Normal
Form of the boundary matrices. We present an algorithm for computing such a
model, obtaining Betti numbers, the prime numbers p involved in the invariant
factors of the torsion subgroup of homology, the amount of invariant factors
that are a power of p and a set of representative cycles of generators of
homology mod p, for each p. Moreover, we establish the minimum valid lambda for
such a construction, what cuts down the computational costs related to the
torsion subgroup. The tools described here are useful to determine topological
information of nD structured objects such as simplicial, cubical or simploidal
complexes and are applicable to extract such an information from digital
pictures.Comment: Journal Image and Vision Computing, Volume 27 Issue 7, June, 200
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Visual multimedia have become an inseparable part of our digital social
lives, and they often capture moments tied with deep affections. Automated
visual sentiment analysis tools can provide a means of extracting the rich
feelings and latent dispositions embedded in these media. In this work, we
explore how Convolutional Neural Networks (CNNs), a now de facto computational
machine learning tool particularly in the area of Computer Vision, can be
specifically applied to the task of visual sentiment prediction. We accomplish
this through fine-tuning experiments using a state-of-the-art CNN and via
rigorous architecture analysis, we present several modifications that lead to
accuracy improvements over prior art on a dataset of images from a popular
social media platform. We additionally present visualizations of local patterns
that the network learned to associate with image sentiment for insight into how
visual positivity (or negativity) is perceived by the model.Comment: Accepted for publication in Image and Vision Computing. Models and
source code available at https://github.com/imatge-upc/sentiment-201
Undue influence: Mitigating range-intensity coupling in AMCW âflashâ lidar using scene texture
We present a new algorithm for mitigating range-intensity coupling caused by scattered light in full-field amplitude modulated continuous wave lidar systems using scene texture. Full-field Lidar works using the time-of-flight principle to measure the range to thousands of points in a scene simultaneously. Mixed pixel are erroneous range measurements caused by pixels integrating light from more than one object at a time. Conventional optics suffer from internal reflections and light scattering which can result in every pixel being mixed with scattered light. This causes erroneous range measurements and range-intensity coupling. By measuring how range changes with intensity over local regions it is possible to determine the phase and intensity of the scattered light without the complex calibration inherent in deconvolution based restoration. The new method is shown to produce a substantial improvement in range image quality. An additional range from texture method is demonstrated which is resistant to scattered light. Variations of the algorithms are tested with and without segmentation - the variant without segmentation is faster, but causes erroneous ranges around the edges of objects which are not present in the segmented algorithm
- âŠ