Ilias Grinias

Video segmentation using fast marching and region growing algorithms

AbstractThe algorithm presented in this paper is comprised of three main stages: (1) classification of the image sequence and, in the case of a moving camera, parametric motion estimation, (2) change detection having as reference a fixed frame, an appropriately selected frame or a displaced frame, and (3) object localization using local colour features. The image sequence classification is based on statistical tests on the frame difference. The change detection module uses a two-label fast marching algorithm. Finally, the object localization uses a region growing algorithm based on the colour similarity. Video object segmentation results are shown using the COST data set.

A semi-automatic seeded region growing algorithm for video object localization and tracking

This paper describes a semi-automatic method for moving object segmentation and tracking. This method is suitable when a few objects have to be tracked, while the camera moves and fixates on them. The user delineates approximately the initial locations in a selected frame and specifies the depth ordering of the objects to be tracked. First, motion-based segmentation is obtained through an initial application of a region growing algorithm. The partition map is sequentially tracked from frame to frame using motion compensation and location prediction. The segmentation map is obtained by the region growing algorithm. Translational motion is assumed for the moving objects, and local intensity or color average may be used as additional features. A post-processing procedure regularizes the object boundaries over time.

Robust pan, tilt and zoom estimation

We propose a method for estimating the motion parameters for a rotating camera with a possibly changing focal length. This method is suitable for analysing sport event videos. The estimation process consists of three stages: (1) robust estimation of the 2D translational components; (2) 2D block-based motion estimation; (3) robust estimation of a parametric motion model. For more reliability, confidence measures for the 2D motion vectors are introduced. The reliability of the method is illustrated on some difficult real image sequences.

MPEG-4 authoring tool using moving object segmentation and tracking in video shots

An Authoring tool for the MPEG-4 multimedia standard integrated with image sequence analysis algorithms is described. MPEG-4 offers numerous capabilities and is expected to be the future standard for multimedia applications. However, the implementation of these capabilities requires a complex authoring process, employing many different competencies from image sequence analysis and encoding of audio/visual/BIFS to the implementation of different delivery scenarios: local access on CD/DVD-ROM, Internet, or broadcast. However powerful the technologies underlying multimedia computing are, the success of these systems depends on their ease of authoring. In this paper, a novel Authoring tool fully exploiting the object-based coding and 3D synthetic functionalities of the MPEG-4 standard is described. It is based upon an open and modular architecture able to progress with MPEG-4 versions and it is easily adaptable to newly emerging better and higher-level authoring and image sequence analysis features.

Flooding and MRF-based Algorithms for Interactive Segmentation

We propose a method for interactive colour image segmentation. The goal is to detect an object from the background, when some markers on object(s) and the background are given. As features only probability distributions of the data are used. At first, all the labelled seeds are independently propagated for obtaining homogeneous connected components for each of them. Then the image is divided in blocks, which are classified according to their probabilistic distance from the classified regions. A topographic surface for each class is obtained, using Bayesian dissimilarities and a min-max criterion. Two algorithms are proposed: a regularized classification based on the topographic surface and incorporating an MRF model, and a priority multi-label flooding algorithm. Segmentation results on the LHI data set are presented.

Bayesian Region Growing and MRF-based Minimization for Texture and Colour Segmentation

We propose a generic, unsupervised feature classification and image segmentation framework, where only the number of classes is assumed as known. Image segmentation is treated as an optimization problem. The framework involves block-based unsupervised clustering using k-means, followed by region growing in spatial domain. High confidence statistical criteria are used to compute a map of initial labelled pixels. A new region growing algorithm is introduced, which is named Independent Flooding Algorithm and computes a height per label for each one of the unlabeled pixels, using Bayesian dissimilarity criteria. Finally, a MRF model is used to incorporate the local pixel interactions of label heights and a graph cuts algorithm performs the final labelling by minimizing the underlying energy. Segmentation results using texture, intensity and color features are presented.

MINMAX Video Summarization under Equality Principle

In this paper we present a video summarization scheme. First, shot detection is performed and then we extract the key frames under an equality requirement on subshots. We propose a key frames selection algorithm (Iso-Content MINMAX), which is very flexible on any choice of content descriptors, and is based on MINMAX optimization formulation. The equality principle provides to the selected key frames the useful property to be equivalent on video content summarization.