Multiresolutional Approach Research Articles

다중해상도 kd-트리와 클러스터 유효성을 이용한 점증적 EM 알고리즘과 이의 영상 분할에의 적용

In this paper, we propose a new multiresolutional and dynamic approach of the EM algorithm. EM is a very popular and powerful clustering algorithm. EM, however, has problems that indexes multiresolution data and requires a priori information on a proper number of clusters in many applications, To solve such problems, the proposed EM algorithm can impose a multiresolution kd-tree structure in the E-step and allocates a cluster based on sequential data. To validate clusters, we use a merge criteria for cluster merging. We demonstrate the proposed EM algorithm outperforms for texture image segmentation.

Cost-efficient speckle interferometry with plastic optical fiber for unobtrusive monitoring of human vital signs

A cost-efficient plastic optical fiber (POF) system for unobtrusive monitoring of human vital signs is presented. The system is based on speckle interferometry. A laser diode is butt-coupled to the POF whose exit face projects speckle patterns onto a linear optical sensor array. Sequences of acquired speckle images are transformed into one-dimensional signals by using the phase-shifting method. The signals are analyzed by band-pass filtering and a Morlet-wavelet-based multiresolutional approach for the detection of cardiac and respiratory activities, respectively. The system is tested with 10 healthy nonhospitalized persons, lying supine on a mattress with the embedded POF. Experimental results are assessed statistically: precisions of 98.8% ± 1.5% and 97.9% ± 2.3%, sensitivities of 99.4% ± 0.6% and 95.3% ± 3%, and mean delays between interferometric detections and corresponding referential signals of 116.6 ± 55.5 and 1299.2 ± 437.3 ms for the heartbeat and respiration are obtained, respectively.

Contextual clustering for image labeling: an application to degraded forest assessment in Landsat TM images of the Brazilian Amazon

The modified adaptive pappas clustering (MPAC) algorithm, previously published in the image processing literature, is proposed as a valuable tool in the analysis of remotely sensed images where texture information is negligible. Owing to its contextual, adaptive, and multiresolutional labeling approach, MPAC preserves genuine but small regions, is easy to use (i.e., it requires minor user interaction to run), and is robust to changes in input parameters. As an application example, an MPAC-based three-stage classifier is applied to degraded forest detection in Landsat Thematic Mapper (TM) scenes of the Brazilian Amazon, where intermediate states of forest alterations caused by anthropogenic activities can be characterized by image structures 1-3 pixels wide. In three TM images of the Para test site, where classification results are validated by means of qualitative and quantitative comparisons with aerial photos, degraded forest areas cover 13% to 45% of the image ground coverage. In the Mato Grosso test site, the degraded forest class overlaps with 1) 10% of the closed-canopy forest detected by the deforestation mapping program of the Food and Agriculture Organization (FAO, 1992), and 2) 19% of the closed-canopy forest detected by the Tropical Rain Forest Information Center (TRFIC, 1996). These figures are in line with the conclusions of a study where present estimates of annual deforestation for the Brazilian Amazon are speculated to capture less than half of the forest area that is actually impoverished each year.

Recent numerical methods – A challenge for efficient visualization

Together with a rapid development of computer hardware, sophisticated, efficient numerical algorithms allow simulation computations of complex physical phenomena. Methods, such as finite volume, multigrid finite element schemes, sparse grid, wavelet approaches, and particle methods or gridless discretizations, all carry data structures, which are tailored to the respective method. These data structures reflect the decomposition of the function space as well as the decomposition in physical space. In this paper an efficient multiresolutional visualization approach is described, which tries to reuse as much of the hierarchical structure in the numerical data as possible. The duality between grid and function space, both carrying an intrinsic hierarchical structure, is explained as the key issue of the approach. Furthermore, a general method of local error measurement is discussed, which allows a reliable representation of the desired multiresolutional data. Finally, the method of spatial, hierarchical subdivision combined with the procedural recovery of the local function spaces is presented to address fairly general numerical data. This leads to a visualization beyond prescribed data formats. Examples from various numerical methods and different data bases underline the applicability of the proposed concept.

Multiresolutional decomposition and modeling with an application to joint probabilistic data association

A multiresolutional approach for measurement decomposition and system modeling is presented in this paper. The decomposition is performed in both spatial and time domains and provides an excellent platform for developing computationally efficient algorithms. Using multiresolutional decomposition and modeling, a multiresolutional joint probabilistic data association (MR-JPDA) algorithm is developed for multiple target tracking. Monte Carlo simulations demonstrate that the computation of the MRJPDA algorithm is much less than the traditional joint probabilistic data association (JPDA) algorithm with a comparable performance.

A multiresolutional approach to 3D object recognition

A multiresolutional approach is presented for effectively recognizing three-dimensional (3D) objects. The approach is both pose and scale invariant. A multiresolutional model base is constructed, and multiscale edges of the object are detected using the wavelet transform. The minimum alignment between model base and object is realized by the linear mapping scheme.

A multiresolutional approach to fuzzy text meaning*

Abstract The present paper focuses on the connotative meaning aspect of language signs on the level of texts. The view is taken that texts can be defined as a kind of supersign, to which ‐ in the same way as to other signs ‐ a meaning can be assigned. A text can therefore be described as the result of a sign articulation which connects the material text sign with a corresponding meaning. For the constitution of the structural text meaning, a kind of a semiotic composition principle is responsible, which leads to the emergence of interlocked levels of language units, demonstrating different grades of resolution. Starting on the level of words, and going through the level of sentences, this principle finally reaches the level of texts by aggregating step by step the meaning of a unit on a higher level out of the meanings of all components one level below, which occur within this unit. In addition, this article will elaborate the hypothesis that the meaning constitution as a two‐stage process, corresponding ...