7,622 research outputs found
Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy
In this paper we present a simple and robust method for self-correction of
camera distortion using single images of scenes which contain straight lines.
Since the most common distortion can be modelled as radial distortion, we
illustrate the method using the Harris radial distortion model, but the method
is applicable to any distortion model. The method is based on transforming the
edgels of the distorted image to a 1-D angular Hough space, and optimizing the
distortion correction parameters which minimize the entropy of the
corresponding normalized histogram. Properly corrected imagery will have fewer
curved lines, and therefore less spread in Hough space. Since the method does
not rely on any image structure beyond the existence of edgels sharing some
common orientations and does not use edge fitting, it is applicable to a wide
variety of image types. For instance, it can be applied equally well to images
of texture with weak but dominant orientations, or images with strong vanishing
points. Finally, the method is performed on both synthetic and real data
revealing that it is particularly robust to noise.Comment: 9 pages, 5 figures Corrected errors in equation 1
Recommended from our members
A new phantom and gradient isocenter estimation for magnetic resonance imaging distortion correction
The purpose of this study was to develop and implement a numerical software based method that can accurately correct the distortion of MR images generated by 3T MRI scanner. To accomplish this, a new phantom has been designed from scratch to capture the distortions inside 3T MRI scanner. An algorithm has been developed, based on the unique geometric feature of the new phantom, to estimate the location of gradient isocenter of the magnetic field inside 3T MRI scanner for the first time
Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach
The main aim of this work is the development of a vision-based road detection
system fast enough to cope with the difficult real-time constraints imposed by
moving vehicle applications. The hardware platform, a special-purpose massively
parallel system, has been chosen to minimize system production and operational
costs. This paper presents a novel approach to expectation-driven low-level
image segmentation, which can be mapped naturally onto mesh-connected massively
parallel SIMD architectures capable of handling hierarchical data structures.
The input image is assumed to contain a distorted version of a given template;
a multiresolution stretching process is used to reshape the original template
in accordance with the acquired image content, minimizing a potential function.
The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file
Simple fish-eye calibration method with accuracy evaluation
In this paper, a simple fish-eye radial distortion calibration procedure is described. This method avoids costly minimisation and optimisation algorithms, and is based on trivial concentricity of three extracted points. The results show that this simplicity is at the expense of increased deviation of results (and thus increased error). However, this deviation can be reduced significantly by the use of simple averaging, such that it is only marginally greater than the current state-of-the-art
A PDE Method to Segment Image Linear Objects with Application to Lens Distortion Removal
In this paper, we propose a partial differential equation based method to segment image objects, which have a given parametric shape based on energy functional. The energy functional is composed of a term that detects object boundaries and a term that constrains the contour to find a shape compatible with the parametric shape. While the shape constraints guiding the PDE may be determined from object's shape statistical models, we demonstrate the proposed approach on the extraction of objects with explicit shape parameterization, such as linear image segments. Several experiments are reported on synthetic and real images to evaluate our approach. We also demonstrate the successful application of the proposed method to the problem of removing camera lens distortion, which can be significant in medium to wide-angle lenses
Part-to-whole Registration of Histology and MRI using Shape Elements
Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing
- …