Lower Resolution Levels Research Articles

Color-based video segmentation using interlinked irregular pyramids

This paper presents a new segmentation technique for video sequences. It relies on building irregular pyramids based on its homogeneity over consecutive frames. Pyramids are interlinked to keep a relationship between the regions in the frames. Virtual nodes are considered to improve matching between low resolution levels of the pyramids. Its performance is good in real-world conditions because it does not depend on image constrains.

The Wavelet-Based Technique in Dispersive Wave Propagation

The paper expounds a wavelet-based technique. The techniques is applied to the dispersive wave equation by describing wave propagation by a composition of fundamental (harmonic) wavelets. The linear Klein–Gordon equation is analyzed and associated approximate wavelet solutions are considered for fixed resolution levels. Discretized wavelet families are computed explicitly using a basis of time harmonic wavelets. Some applications at various low resolution levels show that the technique proposed provides new opportunities for wave propagation analysis

Rotation-invariant pattern matching using wavelet decomposition

In this paper, we propose a wavelet decomposition approach for rotation-invariant template matching. In the matching process, we first decompose an input image into different multi-resolution levels in the wavelet-transformed domain, and use only the pixels with high wavelet coefficients in the decomposed detail subimage at a lower resolution level to compute the normalized correlation between two compared patterns. To make the matching invariant to rotation, we further use the ring-projection transform, which is invariant to object orientation, to represent an object pattern in the detail subimage. The proposed method significantly reduces the computational burden of the traditional pixel-by-pixel matching. Experimental results on a variety of real images have shown the efficacy of the proposed method.

Scale-dependent hierarchical unsupervised segmentation of textured images

This paper presents a new three-staged texture-based segmentation method that, using multiresolution techniques, provides high computational speed. The main novelty of the approach is that spatial proximity has a progressively decreasing importance on lower resolution levels when hierarchically segmenting the data structure. The method achieves low error rates and it does not require any knowledge about the number of textures in the image.

Hierarchical decomposition of multiscale skeletons

The paper presents a novel procedure to hierarchically decompose a multiscale discrete skeleton. The skeleton is a linear pattern representation that is generally recognized as a good shape descriptor. For discrete images, the discrete skeleton is often preferable. Multiresolution representations are convenient for many image analysis tasks. Our resulting skeleton decomposition shows two different types of hierarchy. The first type of hierarchy is one of different scales, as the original pattern is converted into an AND-pyramid and the skeleton is computed for each resolution level. The second type of hierarchy is established at each level of the pyramid by identifying and ranking skeleton subsets according to their permanence, where permanence is a property intrinsically related to local pattern thickness. To achieve the decomposition, both bottom-up and top-down analysis in the sense of moving from higher to lower resolution and vice versa are used. The bottom-up analysis is used to ensure that a part of the skeleton that is connected at a higher resolution level is also connected (if at all present) in the next, lower resolution level. The top-down analysis is used to build the permanence hierarchy ranking the skeleton components. Our procedure is based on the use of (3/spl times/3) local operations in digital images, so it is fast and easy to implement. This skeleton decomposition procedure is most effective on patterns having different thickness in different regions. A number of examples of decompositions of multiscale skeletons (with and without loops) are shown. The skeletons are, in most cases, nicely decomposed into meaningful parts. The procedure is general and not limited to any specific application.

Reverse Engineering: Multilevel-of-Detail Models for Design and Manufacturing

Three-dimensional scanning, data reduction and modelling are important elements in the reverse engineering process and in the rapid prototyping cycle as a whole. In order to achieve accurate reconstructed products, points are sampled at high density. For reconstruction, however, conventional methods usually reduce the data homogeneously without preserving the shape. As a result, essential characteristic details may be lost. In addition, multilevel-of-detail models, which are essential for design and manufacturing, do not exist in conventional methods where only one level of reduced data is created. In this paper, continuous and discrete methods for reconstructing progressive models are proposed. Through these methods, the reconstructed geometric model is represented by hierarchical levels of detail. The main advantage of the approach is that sampled points are significantly reduced to several levels of abstraction, while the geometric behaviour is preserved at each level. That is, even at lower resolution levels, critical details are preserved. The method is also extended to progressive volumetric geometric models, simulation and layered manufacturing.

3D Scanning and Level of Detail Modelling for Design and Manufacturing

3D scanning and modeling of manufacturing products play an important role in the rapid prototyping cycle. In order to achieve accurate reconstructed products, points are sampled at high density. However, for reconstruction, conventional methods usually reduce the data homogeneously without taking shape behavior, such as curvature and topology, into consideration. As a result, essential characteristic details may be lost. In this paper, an advanced method for reconstructing progressive models is proposed. Through this method, the reconstructed geometric model is represented by hierarchical levels of detail. The option to explicitly control the levels of detail is essential for design, modeling and analysis. The main advantage of the approach is that the sampled points are significantly reduced to several levels of abstraction, while at each level the geometric behavior is preserved. That is, even at lower resolution levels, critical details are preserved. Moreover, the method has been extended to progressive time and geometric models for simulation and is demonstrated by several examples.

Texture segmentation using hierarchical wavelet decomposition

This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.

Identification of an unbalanced cryptic translocation t(9;17)(q34.3;p13.3) in a child with dysmorphic features

We report a case of an unbalanced cryptic telomeric translocation 46,XY,der(17),t(9;17)(q34.3;p13.3) in a boy with dysmorphic features and developmental delay. The proband had intrauterine growth retardation, postnatal short stature, and mild microcephaly. Magnetic resonance imaging showed incomplete myelination, but no evidence of lissencephaly. Cytogenetic analysis of the proband's peripheral blood showed an abnormal 17p. Fluorescence in situ hybridisation (FISH) with a Miller-Dieker cosmid probe did not detect a deletion for that area. Further analysis with a 17p telomere specific probe identified an unbalanced telomeric translocation. The same probe was used to determine the presence of an apparent balanced translocation t(9;17)(q34.3;p13.3) in the mother of the proband. The balanced translocation was confirmed with two cosmids that map distally on 9q34.3. Two phenotypically normal half sibs, a maternal aunt, a maternal uncle, and the maternal grandmother were found to be balanced translocation carriers as well. A subtle translocation carriers as well. A subtle translocation is one mechanism that can produce an abnormal phenotype in a patient who had a normal karyotype at lower band resolution levels.

Far Infrared Studies at High Pressures with the Diamond Anvil Cell

The diamond anvil cell (DAC) is very convenient for visible-ultraviolet spectroscopic investigations. It has also been employed for Raman spectroscopy, but fluorescence from the diamond windows imposes severe restrictions. When the DAC is used in the infrared region, the low signal/noise ratio of conventional dispersive infrared spectrometers makes it necessary to condense the beam. Most infrared spectra recorded with a 4× or a 6× beam condenser and the DACs in dispersive instruments are of inferior quality, with low resolution and high noise levels. This is due to the small optical aperture of the DAC (typical 0.15 mm when gaskets are used).