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Fully automatic localization of lumbar vertebrae from clinical X-ray images is very challenging due to the variation of X-ray quality, scale, contrast, number of visible vertebrae, etc. To overcome these challenges, we present a novel framework, where we accelerate a scale-invariant object detection method using Support Vector Machines (SVM) trained on Histogram of Oriented Gradients (HOG) features...
This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina...
Object tracking under complex circumstances remains to be a challenging problem because the appearance of an object can be drastically changed by illumination variations, pose variations and occlusion. This paper proposes an adaptive multiCfeature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram feature and edge orientation histogram...
Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance. In this paper, we propose a segmentation...
Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets...
In this paper, we propose a novel edge descriptor method for background modeling. In comparison to previous edge-based local-pattern methods, it is more robust to noise and illumination variations due to the use of principal gradient information in a local neighborhood. For the background modeling problem, we combined the proposed method with the Local Hybrid Pattern and experimented with an adaptive-dictionary-model...
Detecting infrared pedestrian in outdoor smart video surveillance is always a challenging and difficult problem. Although there have been many methods based on histograms of oriented gradients (HOG) to solve this problem, they would probably fail because of shelter and poor quality of image. To overcome this problem, we propose a robust feature to describe pedestrian which is called entropy-edge weighted...
In the last three decades there have been many multi-frame image super-resolution (SR) algorithms proposed, but there are still many problems, such as edge blurring, artifacts are existing in the final reconstructed image, and many previous algorithms are sensitive to noise. This paper reviews some typical algorithms and addresses their limitations. In this paper, we propose super-resolution with...
To improve the accuracy of corner's detection in the traditional black and white chessboard, a new method based on multi-features is proposed. Three distinct local features of the corners have been analyzed, they are structural response, symmetric response and edge response. By selectively applying these features, initial selection and later screening of potential corners have been done. Non-maximum...
The proposal of new edge detectors is a constant in last years due to the importance of the edge detection result for high-level image processing tasks. It is known that there is no optimal edge detector for all kinds of images and in addition, it is not a straightforward mission to set the optimal set of parameters of the edge detector for each image. In this paper, an algorithm to deal with these...
Though various image segmentation techniques have been developed, it is still a very challenging task to design a robust and efficient algorithm to segment (noisy, blurred or even discontinuous edged) images having high intensity inhomogeneity or non-homogeneity. In this article, a robust fuzzy energy based active contour, using both global and local information, is proposed to detect objects in a...
Projective distortion remains an open problem for image stitching. This paper proposed a novel weight-based shape-optimizing warping framework, which combines a projective transformation and a similarity transformation so as to reduce the projective distortion. This method aligned images with the projective transformation, and optimized images' shape under the constraint condition of similarity transformation...
A robust algorithm that detects text from natural scene images and extracts them regardless of the orientation is proposed. All existing methods are designed to operate under a certain constraint, like detecting text only in one direction. Maximally Stable Extremal Regions (MSER) detector is chosen to extract binary regions since it has proven to be robust to lighting conditions. An enhancement technique...
In view of the traditional image edge detection method easily lead to the disadvantage such as burr and discontinuous, use LVQ neural network to detect the digital image edge. By extracting and calculating the median characteristics, directional information characteristics and Krisch operator direction characteristics of each sample image point, generate the neural network training set and train the...
In this paper, we propose a simple and effective luminance weight prior for single image dehazing. This prior is based on the observation that the atmospheric airlight closely relates to luminance of haze-free image. Plenty of statistical experiments validates that, at each pixel, the normalized luminance of input image can represent the portion of global atmospheric light that reaches the camera...
Pose tracking is a fundamental technology needs to be solved in on-orbit service. Visual pose tracking provides an inexpensive solution while it remains a large obstacle to track satellites under space illumination changing condition. This paper combines point feature with edge feature to track a model-known satellite. It makes use of the distinctiveness of point feature and robustness of edge feature,...
An object often has many distinct manifestations in computer vision, which brings a great challenge to utilizing more comprehensive information. Inspired by some biological researches about edge sensitivity and global structure priority, our key insight is to establish unified transfer classification network with shared contour information. Combining two convolutional networks with three cascaded...
Depth Image Based Rendering (DIBR) is a technology which converts two dimensional images to three dimension using colour image and its associated depth image. The performance of any DIBR system depends on the perfection of the depth image. Holes/disocclusion will occur in the virtual views generated if the depth map is not perfect. Since holes occur in the virtual views when the intensity changes...
The objective of this work is to propose a novel methodology for the finger knuckle print recognition, which is essentially a digital photo of the finger-knuckle region. We have employed very simple concepts of visual computing such as a filter based on the Sobel operator for finding edges and a simple noise reduction algorithm. These operations are exceptionally fast and produce binary images, which...
This paper presents a novel steganographic method based on Least Significant Bit (LSB) substitution and 8-neighboring Pixel Value Differencing (8nPVD) for gray scale image in order to improve the embedding capacity with an imperceptible stego image. The proposed method partitions the cover image into some 3×3 non-overlapping pixel blocks in row major order. k-bits of the secret bit stream are embedded...
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