Based on the space spanned by {1, t, , tn-5, sint, cost, t sin t, t cos t}, (n≥5), this paper presents a new kind of uniform splines. These splines are called uniform algebraic-trigonometric splines, which share most...
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Based on the space spanned by {1, t, , tn-5, sint, cost, t sin t, t cos t}, (n≥5), this paper presents a new kind of uniform splines. These splines are called uniform algebraic-trigonometric splines, which share most of the properties as those of the B-splines in the polynomial space. Such splines can encompass conical solenoids, involutes of circles and some other transcendent curves. The subdivision scheme of this new kind of curves is given, and it is verified to be convergent. The subdivision scheme is equal to a series of linear subdividing process, which followed the V.D. property and the convexity preserving property of the splines.
image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an app...
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ISBN:
(纸本)1901725340
image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.
We describe algorithms for active segmentation (AS) of the first frame, and subsequent, adaptive object tracking through succeeding frames, in a video sequence. Object boundaries that include different known colours a...
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ISBN:
(纸本)1901725340
We describe algorithms for active segmentation (AS) of the first frame, and subsequent, adaptive object tracking through succeeding frames, in a video sequence. Object boundaries that include different known colours are segmented against complex backgrounds;it is not necessary for the object to be homogeneous. As the object moves, we develop a tracking algorithm that adaptively changes the colour space model (CSM) according to measures of similarity between object and background. We employ a kernel weighted by the normalized Chamfer distance transform, that changes shape according to a level set definition, to correspond to changes in the perceived 2D contour as the object rotates or deforms. This improves target representation and localisation. Experiments conducted on various synthetic and real colour images illustrate the segmentation and tracking capability and versatility of the algorithmin comparison with results using previously publishedmethods.
Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are d...
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Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are designed to match image features with unique local neighborhoods. However, the interest point detectors used with SIFT often fail to select perceptible local structures in the image, and the SIFT descriptor does not directly encode the local neighborhood shape. In this paper we propose a symmetry based interest point detector and radial local structure descriptor which consistently captures the majority of basic local image structures and provides a geometrical description of the structure boundaries. This approach concentrates on the extraction of shape properties in image patches, which are an intuitive way to represent local appearance for matching and classification. We explore the specificity and sensitivity of this local descriptor in the context of classification of natural patterns. The implications of the performance comparison with standard approaches like SIFT are discussed.
Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee ...
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Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.
Illegal trade and theft of coins appears to be a major part of the illegal antiques market. image based recognition of coins could substantially contribute to fight against it. Central component in the permanent ident...
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This paper describes the use of variable kernels based on the normalized Chamfer distance transform (NCDT) for mean shift, object tracking in colour video sequences. This replaces the more usual Epanechnikov kernel, i...
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This paper describes the use of variable kernels based on the normalized Chamfer distance transform (NCDT) for mean shift, object tracking in colour video sequences. This replaces the more usual Epanechnikov kernel, improving target representation and localization without increasing the processing time, minimising the distance between successive frame RGB distributions using the Bhattacharya coefficient. The target shape which defines the NCDT is found either by regional segmentation or background-difference imaging, dependent on the nature of the video sequence. The improved performance is demonstrated on a number of colour video sequences.
Lattice vector quantization (LVQ) offers substantial reduction in computational load and design complexity due to the lattice regular structure [1]. In this paper, we extended the SPIHT [2] coding algorithm with latti...
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Lattice vector quantization (LVQ) offers substantial reduction in computational load and design complexity due to the lattice regular structure [1]. In this paper, we extended the SPIHT [2] coding algorithm with lattice vector quantization to code hyperspectral images. In the proposed algorithm, multistage lattice vector quantization (MLVQ) is used to exploit correlations between image slices, while offering successive refinement with low coding complexity and computation. Different four-dimensional lattices and significance metrics are considered. Their rate-distortion performance is compared with other 2D and 3D wavelet-based image compression algorithms.
In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. Inside that one of...
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In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. Inside that one of the most exciting fields is the simulation of fluid flow, which involves for example problems of air, sea and land vehicle motion. In this paper a CNN-UM based solver of 2D inviscid, isothermal, compressible fluids will be presented. Unfortunately the necessity of the coupled multi-layered computational structure with non-linear, space-variant templates does not make it possible to utilize the huge computing power of the analog CNN-UM chips. To improve the performance of our solution an emulated digital CNN-UM has been used.
In this paper, we propose a cross-layer design framework combining adaptive modulation and coding (AMC) with hybrid automatic repeat request (HARQ) based on rate-compatible low-density parity-check codes (RC-LDPC) in ...
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