Super resolution (SR) refers to generation of a High Resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single frame or multi-frame that contains a collection of several images acquired from slightly different views of the same observation area. In this study, we propose a novel application of tri-stereo Remote Sensing (RS) satellite images to the super resolution problem. Since the tri-stereo RS images of the same observation area are acquired from three different viewing angles along the flight path of the satellite, these RS images are properly suited to a SR application. We first estimate registration between the chosen reference LR image and other LR images to calculate the sub pixel shifts among the LR images. Then, the warping, blurring and down sampling matrix operators are created as sparse matrices to avoid high memory and computational requirements, which would otherwise make the RS-SR solution impractical. Finally, the overall system matrix, which is constructed based on the obtained operator matrices is used to obtain the estimate HR image in one step in each iteration of the SR algorithm. Both the Laplacian and total variation regularizers are incorporated separately into our algorithm and the results are presented to demonstrate an improved quantitative performance against the standard interpolation method as well as improved qualitative results due expert evaluations.
This study performs 3D to 2D rigid registration of segmented pre-operative CTA coronary arteries with a single segmented intra-operative X-ray Angio frame in both frequency and spatial domains for real-time Angiography interventions by C-arm fluoroscopy. Most of the work on rigid registration in literature required a close initial-
ization of poses and/or positions because of the abundance of local minima and high complexity that searching algorithms face. This study avoids such setbacks by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. First, template DRRs as candidate poses of 3D vessels of segmented CTA are produced by rotating the camera (image intensifier) around the DICOM angle values with a wide range as in C-arm setup. We have compared the 3D poses of template DRRs with the real X-ray after equalizing the scales (due to disparities in focal length distances) in 3 domains, namely Fourier magnitude, Fourier phase and Fourier polar. The best pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that these methods are robust against noise and occlusion which was also validated by our results. Translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of our objective function without local minima due to distance maps. Final results were evaluated in 2D projection space rather than with actual values in 3D due to lack of ground truth, ill-posedness of the problem which we intend to address in future.
Although Virtual Histology (VH) is the in-vivo gold standard for atherosclerosis plaque characterization in IVUS
images, it suffers from a poor longitudinal resolution due to ECG-gating. In this paper, we propose an image-based approach to overcome this limitation. Since each tissue have different echogenic characteristics, they show
in IVUS images different local frequency components. By using Redundant Wavelet Packet Transform (RWPT),
IVUS images are decomposed in multiple sub-band images. To encode the textural statistics of each resulting
image, run-length features are extracted from the neighborhood centered on each pixel. To provide the best
discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant
Bases (LDB) algorithm in combination with Fisher's criterion. A structure of weighted multi-class SVM permits the classification of the extracted feature vectors into three tissue classes, namely fibro-fatty, necrotic core and dense calcified tissues. Results shows the superiority of our approach with an overall accuracy of 72% in comparison to methods based on Local Binary Pattern and Co-occurrence, which respectively give accuracy rates of 70% and 71%.
We present a content-based image retrieval system for plant identification which is intended for providing users with a
simple method to locate information about their house plants. A plant image consists of a collection of overlapping leaves
and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, texture
and shape features for this problem, as well as introducing some new ones. The features are extracted from the general
plant region that is segmented from the background using the max-flow min-cut technique. Results on a database of 132
different plant images show promise (in about 72% of the queries, the correct plant image is retrieved among the top-15
results).
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our
method is motivated by the observation that neighboring or coupling objects in images generate configurations
and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs
coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate
kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape
distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on
such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm
based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted
objects in a number of applications. In particular for medical image analysis, we use our method to
extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging
segmentation problem. We also apply our technique to the problem of handwritten character segmentation.
Finally, we use our method to segment cars in urban scenes.
Medical imaging is essential in the diagnosis of atherosclerosis. In this paper, we propose the semi-automatic matching of two promising and complementary intravascular imaging techniques, Intravascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT), with the ultimate goal of producing hybrid images with increased diagnostic value for assessing arterial health. If no ECG gating has been performed on the IVUS and OCT pullbacks, there is typically an anatomical shuffle (displacement in time and space) in the image sequences due to the catheter motion in the artery during the cardiac cycle, and thus, this is not possible to perform a 3D registration. Therefore, the goal of our work is to detect semi-automatically the corresponding images in both modalities as a preprocessing step for the fusion. Our method is based on the characterization of the lumen shape by a set of Gabor Jets features. We also introduce different correction terms based on the approximate position of the slice in the artery. Then we train different support vector machines based on these features to recognize these correspondences. Experimental results demonstrate the usefulness of our approach, which achieves up to 95% matching accuracy for our data.
Color management allows the deterministic handling of color data from
input to output. One off the fundamental problems of such system is the
definition of the appropriate color profile on creation. This is
especially difficult in situations, where the input is scanned on a
color scanner. In ssuch cases, the user has to correctly classify the
input source - be it offset, xerography, inkjet, AgX - so that the
correct scanner calibration can be used. This paper explores ways to
eliminate the step and shows how an estimate of heh input can be
generated from the originally scanned input data, without the need for
additional sensors or more than 3 color bands.
In this paper, we present a technique for object tracking in image sequences, which makes use of an active contour framework that moves vertices of a polygon. In this approach, upon capturing boundaries of an object by a polygon from the first few frames of the image sequence, both the spatial segmentation and motion segmentation of a polygonal object can be achieved quickly by involving only the vertex locations and the adjacent edges in the computations. We carry out velocity field estimation at an active polygon vertex using the optical flow constraint on its two adjacent edges. A spatial segmentation phase follows to further refine object’s vertex locations estimated by the optical flow. The advantage of our region-based active polygons over continuous active contours in object tracking in video applications is highlighted by its provision of a compact representation of object features, particularly for simply connected target shapes, hence will be essential for their tracking. Application of the method to target tracking in IR image sequences is illustrated.
Using salient features to drive image filtering is an important problem in image analysis and computer vision. The notion of a scale space has as a result gained popularity on account of its advantage in providing a dynamic description of image features. Object or shape information is inherently contained in level sets or level curves of an image, and its preservation while removing noise can be done reliably through processing of level sets of an image instead of directly working on its intensity values. Following this argument, and the fact that SAR images are impulsive in nature, we propose a level-set-based multiresolution filtering technique for segmentation of SAR imagery. Extracted target information of images, from various radar view angles are integrated to build up an overall target silhouette, which is then used for classification of two main target vehicle types.
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