Showing papers in "Computer Vision and Image Understanding in 2008"

TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.

TL;DR: An extensive evaluation of the unsupervised objective evaluation methods that have been proposed in the literature are presented and the advantages and shortcomings of the underlying design mechanisms in these methods are discussed and analyzed.

TL;DR: This survey covers the historical development and current state of the art in image understanding for iris biometrics and suggests a short list of recommended readings for someone new to the field to quickly grasp the big picture of irisBiometrics.

TL;DR: This paper model the distribution of the texture features using a mixture of Gaussian distributions, allowing the mixture components to be degenerate or nearly-degenerate, and shows that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach.

TL;DR: This paper provides a brief description of each method, highlighting its basic assumptions and mathematical properties, and proposes some numerical benchmarks in order to compare the methods in terms of their efficiency and accuracy in the reconstruction of surfaces corresponding to synthetic, as well as to real images.

TL;DR: The proposed algorithm learns conformity in the traversed paths and hence the inter-camera relationships in the form of multivariate probability density of space-time variables (entry and exit locations, velocities, and transition times) using kernel density estimation.

TL;DR: Experiments and comparative results with multilevel thresholding methods over a synthetic histogram and real images show the efficiency of the proposed method.

TL;DR: A novel method based on principles from linear programming and, in particular, on primal-dual strategies that generalizes prior state-of-the-art methods and can be used for efficiently minimizing NP-hard problems with complex pair-wise potential functions.

TL;DR: This work proposes a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them, using prior knowledge obtained from the scene and accurate camera calibration.

TL;DR: A novel MRF-based model for deformable image matching that is able to match much wider deformations than was considered previously in global optimization framework and applies TRW-S (Sequential Tree-Reweighted Message passing) algorithm to solve the relaxed problem.

TL;DR: This paper designs and implements a novel graph-based min-cut/max-flow algorithm that incorporates topology priors as global constraints and introduces a label attribute for each node to explicitly handle the topology constraints.

TL;DR: The min-marginal energies obtained by the proposed algorithm are exact, as opposed to the ones obtained from other inference algorithms like loopy belief propagation and generalized belief propagation.

TL;DR: The experimental results show that the incremental and adaptive behaviour modelling approach is superior to a conventional batch-mode one in terms of both performance on abnormality detection and computational efficiency.

TL;DR: This paper presents a novel framework for matching video sequences using the spatiotemporal segmentation of videos that uses interest point trajectories to generate video volumes and employs an Earth Mover's Distance based approach for the comparison of volume features.

TL;DR: A weighted fragment based approach that tackles partial occlusion is proposed that is computationally simple enough to be executed in real-time and can be directly extended to a multiple object tracking system.

TL;DR: A novel representation for human actions which encodes the variations in the shape and motion of the performing actor in an unified manner and is robust to viewpoint changes is presented.

TL;DR: This paper implemented a prototype mobile leaf image retrieval system, carried out various experiments for a database with 1,032 leaf images and implemented an adaptive grid-based matching algorithm based on the Nearest Neighbor (NN) search scheme.

TL;DR: Fully automatic methods are presented for the estimation of scene structure and camera motion from an image sequence acquired by a catadioptric system, and many experiments dealing with robustness, accuracy, uncertainty, comparisons between both central and non-central models, and piecewise planar 3D modeling are provided.

TL;DR: A new technique to compute belief propagation messages in time linear with respect to clique size for a large class of potential functions over real-valued variables and develops a form of nonparametric belief representation specifically designed to address issues common to networks with higher-order cliques.

TL;DR: What is seen as current best practices in algorithmic novelty and the increasing importance of validation on particular data sets and problems are reviewed and refinements that may benefit the field of computer vision are suggested.

TL;DR: A new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach is presented, resulting in sophisticated performance in complicated natural scenes.

TL;DR: Two new fusion quality indexes are proposed and implemented through using the phase congruency measurement of the input images to provide a blind evaluation of the image fusion result, i.e. no reference image is needed.

TL;DR: Examples of useful strategies that can be employed to improve the performance of shape matching algorithms are described, which significantly improves shape database retrieval accuracy.

TL;DR: A Selective Coefficient Mask Shift (SCMShift) coding method, implemented over regions of interest (ROIs), is proposed, based on shifting the wavelet coefficients that belong to different subbands, depending on the coefficients relative to the original image.

TL;DR: Experimental results using face images of the UTK-LRHM database demonstrate a significant improvement in recognition rates after assessment and enhancement of degradations.

TL;DR: This paper proposes a novel stereo rectification method for dual-PTZ-camera system, which is essential to greatly increase the efficiency of stereo matching and results show that this approach works well.

TL;DR: A 3-D human-body tracker capable of handling fast and complex motions in real-time is introduced, built upon the Monte-Carlo Bayesian framework, and novel prediction and evaluation methods improving the robustness and efficiency of the tracker are proposed.

TL;DR: An object-based approach based on the F-Measure-a single-valued ROC-like measure which enables a straight-forward mechanism for both optimising and comparing motion detection algorithms.

TL;DR: The fuzzy metric peer group concept is used to build novel switching vector filters andComparisons are provided to show that the proposed approach suppresses impulsive noise, while preserving image details.

TL;DR: This paper addresses the task of human gait and activity analysis from image sequences by learning and recognition of sequential data under a general integrated framework and carries out extensive experiments in three related domains: human activity recognition, abnormal gait analysis, and gait-based human identification.