Sei-ichiro Kamata

Peano scanning of arbitrary size images

Discrete space-filling curves are not uniquely defined. In addition to the condition that the curve must pass all the points of the array only once, continuously, it is necessary to add some critera to select the best curves. The authors aim is to preserve two-dimensional continuity as much as possible. The weighted sum of the distances of the points in the curve is minimized, where the weights are inversely proportional to the spatial distance between the points. However, the minimum is not unique. Particularly, space-filling curves always come on symmetric pairs. The generation of a near optimal space-filling curve is done hierarchically.<<ETX>>

Depth-first coding for multivalued pictures using bit-plane decomposition

A data compression technique using a bit-plane decomposition strategy of multivalued images is described. Although the bit-plane decomposition is mainly used for image transmission, our method takes the image expression for image database into consideration. It has two merits which are a hierarchical representation using depth-first (DF) expression and a simple noise reduction algorithm for the DF expression that is similar to human perception. The DF expression is useful for image expansion, rotation, etc. We study the information in an image that should be eliminated by noise reduction. Noise-like patterns in an image are uniformalized and the edge and smooth surfaces remain nearly unchanged. They are not blurred, but instead are a little enhanced. We also study the properties of the black-and-white (B/W) boundary points on bit-planes. The algorithm of the uniformalization process with a DF-expression of an image is described. An experiment for real image data is carried out by a comparison to other methods, and the results are discussed. >

An interactive analysis method for multidimensional images using a Hilbert curve

To analyze multidimensional images we need a mapping of feature vectors from a multidimensional space to a lower dimensional space. In general, these are performed using linear transformation methods, such as principal component analysis, etc. Linear transformation requires many rotations of data from several points of view because the mapping is not one-to-one. Here, a new interactive method for classifying multispectral images using a Hilbert curve is presented. The Hilbert curve is a one-to-one mapping from N-dimensional space to one-dimensional space and preserves the neighborhood as much as possible. Hilbert curve is a kind of space filling curves, and provides a continuous scan. The merit of the system presented is that the user can extract category clusters without computing any distance in N-dimensional space easily. The method presented here is explained in brief. Clusters are extracted from 1-D data mapped by a Hilbert curve interactively, i.e., a pixel is classified as a category. The user can analyze multidimensional images hierarchically from gross data distribution to fine data distribution. To realize the real time response from the system, data tables storing the addresses and the occurrences of data are used. Here, the address is defined by using the coordinates in N-dimensional space, and a part of mapping which cannot preserve the neighborhood is utilized. In the experiments ex-extracting categories from LANDSAT data, it is confirmed that the user can obtain the real time response from the system after once making the data tables.

Hilbert scanning arithmetic coding for multispectral image compression

We consider the compression of multispectral images using Hilbert scanning and adaptive arithmetic coding. The Hilbert scan is a general technique for continuous scanning of multidimensional data. Arithmetic coding has established itself as the superior method for lossless compression. The aim of this paper is to investigate the integration of the arithmetic coding methodology and a n-dimensional Hilbert scanning algorithm developed by Perez, Kamata and Kawaguchi.

Interactive analysis of multi-spectral images using a Hilbert curve

There have been many new developments in interactive analysis for multi-spectral images in the research of remote sensing. In general, the methods used are linear transformations such as principal component analysis. In this paper, the authors present a new interactive method for classifying multi-spectral images using a Hilbert curve which is a one-to-one mapping and preserves the neighborhood as much as possible. This method is based on a hierarchical histogram expression with different resolutions for the mapped one-dimensional data. The classification on this expression can be performed easily instead of using N-dimensional data directly. In order to realize the real time response from the system, the authors make use of data tables storing the addresses and the occurrences of data, etc. Here the address is defined by using the coordinates in N-dimensional space, and is made use of dealing with a part of mapping which can not preserve the neighborhood. In the experiments using LANDSAT image data, it is confirmed that the user can get the real time response from the system after once making the data tables.

A neural network classifier for LANDSAT image data

There have been many new developments in neural network (NN) research, and many new applications have been studied. The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Among the multispectral data, we concentrate on the Landsat-5 Thematic Mapper (TM) image data which has been available since 1984. Using this classical maximum likelihood approach, a category is modeled as a multivariate normal distribution; however, the distribution for Landsat images is unknown. It is well known that NN approaches have the ability to classify without assuming a distribution. We apply the NN approach to the classification of Landsat TM images in order to investigate the robustness of this approach for multi-temporal data classification. The authors confirmed that the NN approach is effective for the classification even if the test data is taken at the different time.<<ETX>>

A camera calibration using four point-targets

A method for determining the position of a camera using four point-targets is studied. three rotation angles and a translation vector are used to describe the position of the camera for a pinhole model. For solving the six unknown parameters, a minimum of six point-targets is required to define the matrix uniquely (rotation and translation). However, it is shown that by using the properties of the matrix this number can be reduced to four. The error properties of this method are discussed using real image data.<<ETX>>

Arithmetic coding model for compression of LANDSAT images

The compression of LANDSAT images using Hilbert or Peano scanning and adaptive arithmetic coding is considered. The Hilbert scan is a general technique for continuous scanning of multidimensional data. Arithmetic coding has established itself as the superior method for lossless compression. This paper extends on previous work on the integration of the arithmetic coding methodology and an n-dimensional Hilbert scanning algorithm developed by Perez, Kamata and Kawaguchi. Hilbert scanning preserves the spatial continuity of an image, on both the x and y directions, and a higher correlation exists between continuous points than in a raster scan. Therefore, a Hilbert adaptive scheme can better estimate the local probability distributions. Arithmetic coding is most efficient when the probabilities of the symbols are close to one. Therefore, by integrating both the spatial and spectral information into a unified context a high rate of compression can be achieved.

Data reductive image coding by bit-plane modifications

A bit-plane data-reduction scheme based on some characteristics of human image perception is described. This scheme is on information lossy/modifying type. General properties of bit-plane-decomposed images are reviewed. A method that modifies less important portions on the bit-planes into flat areas, to achieve more uniform images, is introduced. A hierarchical algorithm for the purpose is presented as the actual strategy. The important features of this algorithm are that it can use the coded data in the depth first expression and that the data modification is not likely to affect edges and isolated small areas, which are both very important for image perception.<<ETX>>

Application of neural network approach to classify multi-temporal Landsat images

The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Recently there have been many new developments in neural network (NN) research, and many new applications have been studied. It is well known that NN approaches have the ability to classify without assuming a distribution. The authors have proposed an NN model to use the spectral and spatial information. In this paper, they apply the NN approach to the classification of multi-temporal LANDSAT TM images in order to investigate the robustness of a normalization method. From their experiments, they confirmed that the NN approach with the preprocessing is more effective for the classification than the original NN approach even if the test data, is taken at the different time.<<ETX>>