936 research outputs found
High-ISO long-exposure image denoising based on quantitative blob characterization
Blob detection and image denoising are fundamental, sometimes related tasks in computer vision. In this paper, we present a computational method to quantitatively measure blob characteristics using normalized unilateral second-order Gaussian kernels. This method suppresses non-blob structures while yielding a quantitative measurement of the position, prominence and scale of blobs, which can facilitate the tasks of blob reconstruction and blob reduction. Subsequently, we propose a denoising scheme to address high-ISO long-exposure noise, which sometimes spatially shows a blob appearance, employing a blob reduction procedure as a cheap preprocessing for conventional denoising methods. We apply the proposed denoising methods to real-world noisy images as well as standard images that are corrupted by real noise. The experimental results demonstrate the superiority of the proposed methods over state-of-the-art denoising methods
Particle Detection Algorithms for Complex Plasmas
In complex plasmas, the behavior of freely floating micrometer sized
particles is studied. The particles can be directly visualized and recorded by
digital video cameras. To analyze the dynamics of single particles, reliable
algorithms are required to accurately determine their positions to sub-pixel
accuracy from the recorded images. Typically, straightforward algorithms are
used for this task. Here, we combine the algorithms with common techniques for
image processing. We study several algorithms and pre- and post-processing
methods, and we investigate the impact of the choice of threshold parameters,
including an automatic threshold detection. The results quantitatively show
that each algorithm and method has its own advantage, often depending on the
problem at hand. This knowledge is applicable not only to complex plasmas, but
useful for any kind of comparable image-based particle tracking, e.g. in the
field of colloids or granular matter
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
Thermographic Laplacian-pyramid filtering to enhance delamination detection in concrete structure
Despite decades of efforts using thermography to detect delamination in
concrete decks, challenges still exist in removing environmental noise from
thermal images. The performance of conventional temperature-contrast approaches
can be significantly limited by environment-induced non-uniform temperature
distribution across imaging spaces. Time-series based methodologies were found
robust to spatial temperature non-uniformity but require the extended period to
collect data. A new empirical image filtering method is introduced in this
paper to enhance the delamination detection using blob detection method that
originated from computer vision. The proposed method employs a Laplacian of
Gaussian filter to achieve multi-scale detection of abnormal thermal patterns
by delaminated areas. Results were compared with the state-of-the-art methods
and benchmarked with time-series methods in the case of handling the
non-uniform heat distribution issue. To further evaluate the performance of the
method numerical simulations using transient heat transfer models were used to
generate the 'theoretical' noise-free thermal images for comparison.
Significant performance improvement was found compared to the conventional
methods in both indoor and outdoor tests. This methodology proved to be capable
to detect multi-size delamination using a single thermal image. It is robust to
the non-uniform temperature distribution. The limitations were discussed to
refine the applicability of the proposed procedure
Generalization of form in visual pattern classification.
Human observers were trained to criterion in classifying compound Gabor signals with sym- metry relationships, and were then tested with each of 18 blob-only versions of the learning set. General- ization to dark-only and light-only blob versions of the learning signals, as well as to dark-and-light blob versions was found to be excellent, thus implying virtually perfect generalization of the ability to classify mirror-image signals. The hypothesis that the learning signals are internally represented in terms of a 'blob code' with explicit labelling of contrast polarities was tested by predicting observed generalization behaviour in terms of various types of signal representations (pixelwise, Laplacian pyramid, curvature pyramid, ON/OFF, local maxima of Laplacian and curvature operators) and a minimum-distance rule. Most representations could explain generalization for dark-only and light-only blob patterns but not for the high-thresholded versions thereof. This led to the proposal of a structure-oriented blob-code. Whether such a code could be used in conjunction with simple classifiers or should be transformed into a propo- sitional scheme of representation operated upon by a rule-based classification process remains an open question
Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camera
Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camer
Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camera
Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camer
Mammographic Ellispe Modelling for Risk Estimation
AbstractIt has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present the classification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection technique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammograms based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast density using computer vision techniques to improve classification of mammographic images as low/high risk
- …