4,287 research outputs found
Automated Detection of Coronal Loops using a Wavelet Transform Modulus Maxima Method
We propose and test a wavelet transform modulus maxima method for the au-
tomated detection and extraction of coronal loops in extreme ultraviolet images
of the solar corona. This method decomposes an image into a number of size
scales and tracks enhanced power along each ridge corresponding to a coronal
loop at each scale. We compare the results across scales and suggest the
optimum set of parameters to maximise completeness while minimising detection
of noise. For a test coronal image, we compare the global statistics (e.g.,
number of loops at each length) to previous automated coronal-loop detection
algorithms
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
This paper addresses the visualisation of image classification models, learnt
using deep Convolutional Networks (ConvNets). We consider two visualisation
techniques, based on computing the gradient of the class score with respect to
the input image. The first one generates an image, which maximises the class
score [Erhan et al., 2009], thus visualising the notion of the class, captured
by a ConvNet. The second technique computes a class saliency map, specific to a
given image and class. We show that such maps can be employed for weakly
supervised object segmentation using classification ConvNets. Finally, we
establish the connection between the gradient-based ConvNet visualisation
methods and deconvolutional networks [Zeiler et al., 2013]
Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications
Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performanc
Robust semi-automated path extraction for visualising stenosis of the coronary arteries
Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets
Text Localization in Video Using Multiscale Weber's Local Descriptor
In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the local region information using WLD with different radius and
neighborhood relationship of pixel values and hence obtained intensity enhanced
key frames at multiple scales. These multiscale WLD key frames are merged
together and then the horizontal gradients are computed using morphological
operations. The obtained results are then binarized and the false positives are
eliminated based on geometrical properties. Finally, we employ connected
component analysis and morphological dilation operation to determine the text
regions that aids in text localization. The experimental results obtained on
publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset
illustrate that the proposed method can accurately detect and localize texts of
various sizes, fonts and colors in videos.Comment: IEEE SPICES, 201
Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy
Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes
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