Showing papers in "Computer Vision and Image Understanding in 2010"

TL;DR: This work addresses the problem of incorporating different types of contextual information for robust object categorization in computer vision by considering the most common levels of extraction of context and the different levels of contextual interactions.

TL;DR: An overview of the TRECVid shot boundary detection task, a high-level overview ofThe most significant of the approaches taken, and a comparison of performances are presented, focussing on one year (2005) as an example.

TL;DR: The segmented and annotated IAPR TC-12 benchmark is introduced; an extended resource for the evaluation of AIA methods as well as the analysis of their impact on multimedia information retrieval.

TL;DR: A new intensity-based calibration model is proposed that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data and therefore significantly reduces the number of necessary reference images.

TL;DR: This paper presents a real-time vision-based system to assist a person with dementia wash their hands, which combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision-theoretic framework for computing policies of action.

TL;DR: A Census-based stereo matching algorithm that handles difficult areas for stereo matching, such as areas with low texture, very well in comparison to state-of-the-art real-time methods and can successfully eliminate false positives to provide reliable 3D data.

TL;DR: This paper investigates automated detection and identification of malaria parasites in images of Giemsa-stained thin blood film specimens by proposing a complete framework to extract these stained structures, determine whether they are parasites, and identify the infecting species and life-cycle stages.

TL;DR: This paper suggests a simple method to use multiple reference histograms for producing a single histogram that is more appropriate for tracking the target and proposes an extension to the Mean Shift tracker where the convex hull of these histograms is used as the target model.

TL;DR: An integrated method for post-processing of range data which removes outliers, smoothes the depth values and enhances the lateral resolution in order to achieve visually pleasing 3D models from low-cost depth sensors with additional (registered) color images is presented.

TL;DR: The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus theNumber of operations needed in the test phase, is reduced.

TL;DR: The development of an automatic breast tissue classification methodology is described, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification.

TL;DR: The aim of the paper is to show the advantages of using a efficient modeling of the processing occurring at retina level and in the V1 visual cortex in order to develop efficient and fast bio-inspired modules for low level image processing.

TL;DR: A novel stereo matching algorithm that is designed for high efficiency when realized in hardware and designed for the deployment in Field Programmable Gate Arrays and Application Specific Integrated Circuits (ASICs) is proposed.

TL;DR: This paper presents a method for estimating six degrees of freedom camera motions from central catadioptric images in man-made environments by decoupling the rotation and the translation and shows that the line-based approach allows to estimate the absolute attitude at each frame, without error accumulation.

TL;DR: This work presents an approximate solution to the problem of visually finding an object in a mostly unknown space with a mobile robot and investigates its performance and properties to conclude that this approach is sufficient to solve this problem and has additional desirable empirical characteristics.

TL;DR: An active perception system, consisting of a camera mounted on a pan-tilt unit and a 360^o RFID detection system, both embedded on a mobile robot, and a multi-sensor-based control strategy based on the tracker outputs and on the RFID data is designed.

TL;DR: The proposed approach outperforms existing works such as scale invariant feature transform (SIFT), or the speeded-up robust features (SURF), and is robust to some changes in illumination, viewpoint, color distribution, image quality, and object deformation.

TL;DR: The results demonstrate that the CA is capable of being trained to perform many different tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms.

TL;DR: Experimental results show that the proposed fuzzy color histogram-based shot-boundary detection algorithm effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot- boundary detection algorithms.

TL;DR: This is the first study to document degraded iris biometrics performance with non-cosmetic contact lenses.

TL;DR: This paper compares four approaches to achieve a compact codebook vocabulary while retaining categorization performance and investigates the trade-off between codebook compactness and categorizationperformance.

TL;DR: A three module system based on both 2D and 3D cues that gives rise to a promising system to detect pedestrians in urban scenarios using Real AdaBoost, Haar wavelets and edge orientation histograms.

TL;DR: It is demonstrated that concept detection in web video is feasible, and that - when testing on YouTube videos - the YouTube-based detector outperforms the ones trained on standard training sets.

TL;DR: A novel probabilistic approach to data association, that takes into account that features can also move between cameras under robot motion, is presented, that circumvents the combinatorial data association problem by using an incremental expectation maximization algorithm.

TL;DR: This paper presents an approach for view-invariant gesture recognition based on 3D data captured by a SwissRanger SR4000 camera, which is similar to the recognition rate when training and testing on gestures from the same viewpoint, hence the approach is indeed view- Invariant.

TL;DR: The proposed method has been shown to perform very well with both noisy and noise-free images from multimodal datasets, outperforming conventional methods in terms of fusion quality and noise reduction in the fused output.

TL;DR: A modified four-source PS algorithm is presented which enhances the surface normal estimates by assigning a likelihood measure for each pixel being in a shadowed region, determined by the discrepancies between measured pixel brightnesses and expected values.

TL;DR: Experimental results demonstrate that the developed tracker is capable of handling several challenging situations, where the labels of objects are correctly identified and maintained over time, despite the complex interactions among the tracked objects that lead to several layers of occlusions.

TL;DR: Under the general assumption of stationary foreground appearance, it is shown that robust object tracking is possible by adaptively adjusting the locations of these blocks, which are modeled as a small number of rectangular blocks whose positions within the tracking window are adaptively determined.

TL;DR: This paper presents a light-weight and efficient background modeling and foreground detection algorithm that is highly robust against lighting variations and non-static backgrounds including scenes with swaying trees, water fountains and rain.