Multiresolutional Representation Research Articles

A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas

Neural somas perform most of the metabolic activities in the neuron and support the chemical process that generates the basic elements of the synapses, and consequently the brain activity. The morphology of the somas is one of the fundamental features for classifying neurons and their functionality. In this paper, we characterize the morphology of the 39 three-dimensional reconstructed human pyramidal somas in terms of their multiresolutional Reeb graph representation, from which we extract a set of directional and linear variables to perform model-based clustering. To deal with this dataset, we introduce the novel Extended Mardia-Sutton mixture model whose mixture components are distributed according to a newly proposed multivariate probability density function that is able to capture the directional-linear correlations. We exploit the capabilities of Bayesian networks in combination with the Structural Expectation-Maximization algorithm to learn the finite mixture model that clusters the neural somas by their morphology and the conditional independence constraints between variables. We also derive the Kullback-Leibler divergence of the Extended Mardia-Sutton distribution to be used as a measure of similarity between soma clusters. The proposed finite mixture model discovered three subtypes of human pyramidal somas. We performed Weltch t-tests and Watson-Williams tests, as well as rule-based identification of clusters to characterize each group by its most prominent features. Furthermore, the resulting model allows us to simulate the 3D virtual representations of somas from each cluster, which can be a useful tool for neuroscientists to reason and suggest new hypotheses.

THE AUGMENTED MULTIRESOLUTION REEB GRAPH APPROACH FOR CONTENT-BASED RETRIEVAL OF 3D SHAPES

This article presents a 3D shape matching method for 3D mesh models applied to content-based search in database of 3D objects. The approach is based on the multiresolution Reeb graph (MRG) proposed by Hilaga et al.1 MRG provides a rich representation of shapes able in particular to embed the object topology. In our framework, we consider 3D mesh models of various geometrical complexity, of different resolution, and when available with color texture map. The original approach, mainly based on the 3D object topology, is not accurate enough to obtain satisfying matching. Therefore we propose to reinforce the topological consistency conditions of the matching and to merge within the graph geometrical and visual information to improve matching and calculation of shape similarity between models. Besides, all these new attributes can be freely weighted to fit the user requirements for object retrieval. We obtain a flexible multiresolutional and multicriteria representation that we called augmented multiresolution Reeb graph (aMRG). The approach has been tested and compared with other methods. It reveals very performant for the retrieval and the classification of similar 3D shapes.

A framework for scalable delivery of digitized spaces

We present a framework for scalable delivery of digitized environments. Today’s digital museums distribute images, text, sounds, and videos. Our work targets the distribution of a more advanced media type that allows users to independently and interactively explore digitized spaces. We describe a multidimensional and multiresolutional representation that maps directly to a set of communication channels. Clients receive data by subscribing to and unsubscribing from these channels. Client adaptation to current application and network conditions is performed by managing a working set of channels. This mechanism enables distribution of digitized environments to large groups of independent digital museum visitors.

계층적 Level-of-Detail 표현을 이용한 해마의 국부적인 형상 분석

뇌의 하부 구조인 해마의 전역적 부피 감소와 국부적 형상 변화는 정신의학적 질환에 깊게 관련되어 있다. 해마 구조에 관한 형상 분석 연구는 크게 해마 형상 표현 모델을 구축하고, 이러한 형상 표현으로부터 형상 유사성을 계산하는 과정으로 구성된다. 본 논문에서는 메쉬, 복셀, 골격 데이터를 포함하는 복합적인 옥트리 기반의 형상 표현을 이용하여 해마의 형상을 분석하기 위한 새로운 방법을 제시한다. 우선 해마에 관한 MRI 데이터를 입력으로 받아, 마칭큐브 알고리즘을 사용하여 다해상도 메쉬 모델을 구축한다. 이렇게 구성된 다각형 모델은 깊이맵 기반의 복셀화 방법을 이용하여 중간 단계의 이진 복셀 데이터로 변환된다. 그리고 변환된 복셀 데이터로부터 슬라이스 기반의 골격화 방법에 의하여 해마의 3차원 골격을 추출한다. 그런 후에 옥트리 기반의 다해상도 형상 표현을 얻기위해 해마의 메쉬, 복셀, 골격 데이터를 계층적으로 공간 분할하여 저장하고, 광선 추적 기반의 메쉬 샘플링 방법을 적용하여 샘플 메쉬 데이터를 추출한다. 최종적으로, 형상간 유사성 측정을 위하여 추출된 골격으로부터 방사되는 광선들과 충돌되는 각 샘플 메쉬 쌍에 대하여 <TEX>$L_2$</TEX>과 하우스도르프 거리를 계산하고 인터랙티브한 국부적 형상 분석을 지원하기 위하여 마우스 피킹 인터페이스를 채택한다. 이것은 형상의 국부적 변화에 대하여 다양한 해상도에 기반한 형상 분석을 가능하게 한다. 본 논문에서는 실험을 통하여, 제시한 형상 분석 방법이 회전과 스케일 등의 변환에 강인하고, 특히 형상의 국부적 변화 정도를 정확도를 유지하면서 빠르게 평가하는데에 효과적임을 확인하였다. 경로의 수신 신호가 완전 동기 된 수신 신호임을 확인하였다.omonas aeruginosa PA01과 <TEX>$82\%$</TEX>로 가장 높은 유사성을 보였고 Pseudomonas arvilla C-1와는 <TEX>$71\%,$</TEX> Pseudomonas putida KT2440과는 <TEX>$59\%,$</TEX> 그리고 Pseudomonas sp. CA10과는 <TEX>$53\%$</TEX>의 상동성이 각각 존재하는 것으로 확인하였다.)을 가지고 있음이 확인되었다. 사람에 직접적인 유해성을 가지고 있는 지 확인하기 위해 사람 방광 유래의 T-24세포와 장내 표피 유래의 Caco-2세포에 대한 부착능을 시험하였을 때, 16균주<TEX>$(42.1\%)$</TEX>가 T-24방광 세포에, 그리고 17균주<TEX>$(44.7\%)$</TEX>가 Caco-2장세포에 대해 강한 부착능을 나타내었다. 특히 11균주<TEX>$(28.9\%)$</TEX>는 두 세포 모두에 강한 부착능을 가지고 있었다. Filter mating method를 수행하여 이들 균주들의 독소 생산 유전자와 항생제 내성 유전자가 사람에서 분리된 균주로 전달되는 것을 확인할 수 있었다. 본 실험의 결과는 설사 중상을 나타내는 돼지로부터 분리된 용혈성 E. coli의 독성과 세포 부착능력, 그리고 항생제 내성간의 상호 연관성을 보여주지 않았으나 동물 분리 세균의 항생제 내성과 독소 생산 능력이 유전자 전달을 통해서 뿐만 아니라 세균의 직접 접촉에 의해서도 인체로 전달될 수 있는 것을 보여주는 것이다.다. 본 연구를 토대로 장시간의 체외순환에서는 신장기능을 대표하는 수치들에도 Both global volume reduction and local shape changes of hippocampus within the brain indicate their abnormal neurological states. Hippocampal shape analysis consists of two main steps. First, construct a hippocampal shape representation model ; second, compute a shape similarity from this representation. This paper proposes a novel method for the analysis of hippocampal shape using integrated Octree-based representation, containing meshes, voxels, and skeletons. First of all, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. This model is converted to intermediate binary voxel representation. And we extract the 3D skeleton from these voxels using the slice-based skeletonization method. Then, in order to acquire multiresolutional shape representation, we store hierarchically the meshes, voxels, skeletons comprised in nodes of the Octree, and we extract the sample meshes using the ray-tracing based mesh sampling technique. Finally, as a similarity measure between the shapes, we compute <TEX>$L_2$</TEX> Norm and Hausdorff distance for each sam-pled mesh pair by shooting the rays fired from the extracted skeleton. As we use a mouse picking interface for analyzing a local shape inter-actively, we provide an interaction and multiresolution based analysis for the local shape changes. In this paper, our experiment shows that our approach is robust to the rotation and the scale, especially effective to discriminate the changes between local shapes of hippocampus and more-over to increase the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach.

Multiresolutional hierarchical decision support systems

Decision support systems gain better performance and higher accuracy by the virtue of building multiresolutional (multigranular, multiscale) representation, and employing multiscale behavior generation subsystem (planning and control). The latter are equipped by devices for unsupervised learning that adjust their functioning to the results of self-identification. We demonstrate that planning and learning are joint processes. The author's intention is to emphasize that the concepts of multiresolutional representation (MR) and multiresolutional decision support (MR-DSS) probably have in common a general significance that crosses the boundaries of particular domains of applications and disciplines. The paper explores this phenomenon. The ubiquity of a principle that somehow persistently delivers benefits to many areas of knowledge and technology seems to be more important than a habit to follow the pigeonhole principle of paper presentation.

Component-based polygonal approximation of soft objects

We propose a new polygonal approximation method for soft objects. While conventional polygonization methods decompose space into small-sized cells and compute many pieces of polygons for the cells, this new method polygonizes a soft object by smoothing an initial polygonal approximation using subdivision surface schemes and an exact computation scheme. The initial polygonal approximation is generated by the union of the polygonal approximations of the components that constitute the soft object. Our method has many advantages over conventional methods: faster computation time, regular-sized polygons, efficient robust results, and multiresolutional representation. These features will lead to an interactive modeling environment using soft objects as well as an efficient polygonal representation that is proper to be used in virtual reality and distributed environments.

Recognition of 2D Object Contours Using Starting-Point-Independent Wavelet Coefficient Matching

In this paper, a new recognition algorithm for 2D object contours, based on the decimated wavelet transform, is presented, emphasizing the starting point dependency problem. The proposed matching algorithm consists of two parts: Firstly, we present new data structures for the decimated wavelet representation and a searching algorithm to estimate the misalignment between the starting points for the reference model and unknown object. We also adopt a polynomial approximation technique and propose a fast searching algorithm. And then, matching is performed in an aligned condition on the multiresolutional wavelet representation. By employing a variable-rate decimation scheme, we can achieve fast and accurate recognition results, even in the presence of heavy noise. We provide an analysis on the computational complexity, showing that our approach requires only less than 25% of the computational load required for the conventional method [1]. Various experimental results on both synthetic and real imagery are presented to demonstrate the performance of the proposed algorithm. The simulation results show that the proposed algorithm successfully estimates the misalignment and classifies 2D object contours, even for the input SNR = 5 dB.

Stereo matching algorithm based on modified wavelet decomposition process

Multiresolutional representation such as pyramidal structures is useful for stereo matching as coarse-to-fine strategy. However, conventional pyramidal structures using Gaussian or Laplacian filters lose much information due to their low-pass filtering characteristics and also cannot obtain any spatial orientation selectivity. The adoption of wavelet transform can remedy these problems, but at the time of image translation, it changes wavelet coefficients. In this paper, a pyramid using modified wavelet decomposition process is proposed to have translation invariance. The image transformed by the proposed method is converted into appropriate multiple features without loss of information. Since the importance of each feature is determined heuristically in the multiple feature-based stereo matching method, it is very difficult to fuse them adequately. In the proposed algorithm, the weight of each feature, that is, the relative importance of each feature, is decided from the similarity between the intensities in the local region of each left and right wavelet channels. Since the window size used for the decision of weight and disparity values greatly influences the processed result, the window is adaptively determined from the disparities estimated in the coarse resolution and low-varying channel of fine resolution. The window size must be large enough to obtain signal-to-noise ratio, but not too large as to induce the effects of projective distortion. Also, a new relaxation algorithm which can reduce false matches without blurring the disparity edge is proposed. By integrating adaptive weight, variable window selection method, and relaxation process, an accurate and stable disparity map is obtained. Experimental results for various images show that the proposed algorithm has good performance even if the image has the unfavorable conditions.

A Multiresolutional Controller for Sensitivity Minimization

A multiresolutional signal decomposition allows a signal to have equivalent representations at different scales or levels of detail. This idea can be extended to a control system by having a multiresolutional representation of the plant and of the controller. One important purpose for developing a multiresolutional control system is to have the capability of controlling a plant with a high resolution representation using a low resolution controller. A multiresolutional controller could also be used to control only a particular aspect of a plant. We take a first step towards achieving a multiresolutional control system in this paper. We model the plant using a best, orthonormal wavelet packet basis and then determining the optimal controller using the same wavelet packet basis or another related wavelet packet basis. This paper presents a theorem for designing a wavelet packet controller which minimizes the sensitivity function for a SISO system. We also present new theoretical results on wavelet packets required for making the implementation of this multiresolutional controller very practical.

Three-dimensional morphological pyramid and its application to color image sequence coding

In this paper, we propose an efficient and multiresolutional image sequence representation scheme in which the input image sequence is decomposed and represented as three-dimensional (3-D) morphological pyramids. An input image sequence is first decomposed into a 3-D morphological lowpass pyramid using 3-D morphological filters. A 3-D morphological bandpass pyramid is then built by taking the difference between the parent and the expanded son or daughter. Finally, a 3-D vector quantization is applied to each level of the 3-D morphological bandpass pyramid to reduce the bandwidth required for transmitting the bandpass pyramid. An example of color image sequence is presented to show the effectiveness of this coding system.