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Calibrating hand-eye geometry is often based on explicit feature correspondences. This article presents an alternative method that uses the apparent flow induced by the motion of the camera to achieve self-calibration. To make the method more robust against noise, the strategy is to use the orientation of the normal flow field which is more noise-immune, to recover first the direction component of...
Navigating safely in outdoor environments is a challenging activity for vision-impaired people. This paper is a step towards developing an assistive navigation system for the blind. We propose a robust method for detecting the pedestrian marked lanes at traffic junctions. The proposed method includes two stages: regions of interest (ROI) extraction and lane marker verification. The ROI extraction...
In this paper, we propose a new descriptor which is computed by comparing invariant cross color channels of pairs of points in the local patch. To efficiently obtain the sampled pairs of points, a galaxy sampling pattern is proposed. As shown in the experiments, our descriptor using invariant cross color channels and the galaxy sampling can achieve the best performance in most cases with slight computation...
Deformable registration of multi-modality medical image remains a challenging research topic. The incorporation of prior information on the expected joint distribution has shown to noticeably improve registration accuracy and robustness. However, direct application of the learned joint histogram makes the algorithm sensitive to the difference between the training data and the test image. This paper...
The main issue in current algorithms for the detection of single-frame defects like dust, dirt and blotches in archived film is the significant number of false alarms due to motion compensation errors and film grain. This typically leads to disturbing artifacts occurring in the subsequent defect removal process. We propose a novel algorithm for the detection of single-frame defects which addresses...
This paper proposes a prioritized matching approach for finding corresponding points in multiple calibrated images for multi-view stereo reconstruction. The approach takes a sparse set of seed matches between pairs of views as input and then propagates the seeds to neighboring regions by using a prioritized matching method which expands the most promising seeds first. The output of the method is a...
Multi-camera systems such as linear camera arrays are commonly used to capture content for multi-baseline stereo estimation, view generation for auto-stereoscopic displays, or similar tasks. However, even after a careful mechanical alignment, residual vertical disparities and horizontal disparity offsets impair further processing steps. In consequence, the multicamera content needs to be rectified...
Local Binary Patterns (LBPs) and Covariance Matrices (CovMs) are two popular kinds of texture descriptors. However, local correlation brought by LBPs and global correlation brought by CovMs could not be directly combined to achieve enhanced discriminative power. This paper develops a powerful descriptor, named COV-LBP. Firstly, we propose a variant of LBPs on Euclidean space, named the LBP Difference...
Many problems in computer vision and robotics rely on automatically determining point correspondences from two images. Due to issues such as illumination variations, uncontrolled acquisition conditions and noise, this is a challenging problem. This work presents a method that combines visual and shape information to perform point correspondences which is invariant to rotation and scaling transformations...
Foreground detection is the first step in video surveillance system to detect moving objects. Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this paper,...
This paper presents a robust fuzzy clustering algorithm which can perform clustering without pre-assigning the number of clusters and is not sensitive to the initialization of cluster centers. This is achieved by iteratively splitting and merging operations under the guidance of mistake measurements. In every step of the iteration, we first split the cluster containing data points belonging to different...
This work introduces a new representation for Motion Capture data (MoCap) that is invariant under rigid transformation and robust for classification and annotation of MoCap data. This representation relies on distance matrices that fully characterize the class of identical postures up to the body position or orientation. This high dimensional feature descriptor is tailored using PCA and incorporated...
During the last two decades, a series of subspace methods have succeeded in achieving a satisfactory performance for face recognition tasks, but have always failed when partial occlusions occur. This paper combines the subspace techniques with probabilistic models, and aims at achieving invariance to occlusions. The concept underlying the proposed method is that two faces with the same identity, even...
We introduce an adaptation framework based on scale space filtering for making thinning algorithms robust against noise in sketch images. The framework takes a sketch image as input, produces a set of Gaussian blurred images of the input sketch and uses a thinning algorithm to produce thinned versions of the blurred images. The algorithm's output is then the thinned image with the best performance...
Images with text are frequently used on Internet for different purposes. Automatic recognition of text from web images plays an important role on extraction and retrieval of web information. However, the web images are usually in low resolution with artifacts and special effects, which makes word recognition a challenge task even after the text has been localized. In this paper, we propose a robust...
We present a new split-type algorithm for the minimization of a p-harmonic energy with added data fidelity term. The half-quadratic splitting reduces the original problem to two straightforward problems, that can be minimized efficiently. The minimizers to the two sub-problems can typically be computed pointwise and are easily implemented on massively parallel processors. Furthermore the splitting...
Least square fitting of quadratic surfaces is a fundamental problem in pattern recognition, computer vision, graphics, and medical imaging analysis. This paper investigated in approaches to ellipsoid-specific fitting. In 2D case, Fitzgibbon's ellipse-specific fitting approach outperforms others since it is extremely robust, efficient, and easy to implement. This paper attempts to extend it from 2D...
We propose a novel unified approach for homography estimation from two or more correspondences of local elliptical features. The method finds a homography defined by first-order Taylor expansions at two (or more) points. The approximations are affine transformations that are constrained by the ellipse-to-ellipse correspondences. Unlike methods based on projective invariants of conics, the proposed...
Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem...
Ordinal measures are robust image descriptors for encoding discriminative features of iris images. However, there are many tunable parameters in ordinal filters which can generate an over-complete feature pool. This paper proposes a novel feature selection method based on linear programming, which can learn a compact and effective ordinal feature set for iris recognition. Firstly, large margin principle...
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