Sei Ichiro Kamata

A new algorithm for N-dimensional Hilbert scanning

There have been many applications of the Hilbert curve, such as image processing, image compression, computer hologram, etc. The Hilbert curve is a one-to-one mapping between N-dimensional space and one-dimensional (l-D) space which preserves point neighborhoods as much as possible. There are several algorithms for N-dimensional Hilbert scanning, such as the Butz algorithm and the Quinqueton algorithm. The Butz algorithm is a mapping function using several bit operations such as shifting, exclusive OR, etc. On the other hand, the Quinqueton algorithm computes all addresses of this curve using recursive functions, but takes time to compute a one to-one mapping correspondence. Both algorithms are complex to compute and both are difficult to implement in hardware. In this paper, we propose a new, simple, nonrecursive algorithm for N-dimensional Hilbert scanning using look-up tables. The merit of our algorithm is that the computation is fast and the implementation is much easier than previous ones.

A gray image compression using a Hilbert scan

Hilbert curve is one of the space-filling curves published by Peano. There are several applications using this curve such as image processing, computer hologram, etc. In this paper, we concentrate on a lossy compression technique for a gray image using the Hilbert curve. The merit of this curve is to pass through all points in a quadrant, and it always moves to the neighbor quadrant. Our method is based on this neighborhood property, by a simple segmentation of the scanned one-dimensional data using a zero order interpolation. From our experiments, we have confirmed that in spite of the simple computation in comparison to JPEG, acceptable quality images can be obtained at bit-rates above 0.6 bit/pixel.

Lossless compression for RGB color still images

In the present paper we propose lossless compression for RGB color still images which consist of three planes, though many of the proposed techniques for lossless compression are for gray scale images. In the proposed method, we realize effective coding by modifying the prediction errors obtained from each plane. We use color space correlation in modification of prediction errors. High speed and effective context modeling in entropy coding is also realized using prediction errors of previously coded pixels around the pixel being encoded.

NIR: Content based image retrieval on cloud computing

NIR is an open source cloud computing enabled content based image retrieval system. With the development and popularization of cloud computing, more and more researchers from different research areas do research with the help of cloud computing. Nowadays content based image retrieval as one of the challenging and emerging technologies is high computation task because of the algorithm computation complexity and big amount of data. As based on cloud computing infrastructure, NIR is easy to extent and flexible for deployment. As an open source project, NIR can be improved on demand and integrated to other existing systems. This paper presents our ideas, findings, design and the system from our work of NIR.

An address generator of a pseudo-Hilbert scan in a rectangle region

The Hilbert curve is one of space filing curves presented by G. Peano in 1890. We apply this curve to scanning an arbitrary sized image for image compression, image processing, etc. In this paper, we propose a new, simple, non-recursive algorithm for pseudo-Hilbert scanning using lookup tables. The merit of our algorithm is that the computation is fast and the hardware implementation is much easier than previous ones. From our experimental results, we have confirmed that our method is about 50 times faster than other methods.

Compressive Bilateral Filtering

This paper presents an efficient constant-time bilateral filter that produces a near-optimal performance tradeoff between approximate accuracy and computational complexity without any complicated parameter adjustment, called a compressive bilateral filter (CBLF). The constant-time means that the computational complexity is independent of its filter window size. Although many existing constant-time bilateral filters have been proposed step-by-step to pursue a more efficient performance tradeoff, they have less focused on the optimal tradeoff for their own frameworks. It is important to discuss this question, because it can reveal whether or not a constant-time algorithm still has plenty room for improvements of performance tradeoff. This paper tackles the question from a viewpoint of compressibility and highlights the fact that state-of-the-art algorithms have not yet touched the optimal tradeoff. The CBLF achieves a near-optimal performance tradeoff by two key ideas: 1) an approximate Gaussian range kernel through Fourier analysis and 2) a period length optimization. Experiments demonstrate that the CBLF significantly outperforms state-of-the-art algorithms in terms of approximate accuracy, computational complexity, and usability.

An address generator, for an N-dimensional pseudo-Hilbert scan in a hyper-rectangular, parallelepiped region

The Hilbert curve is a one-to-one mapping between N-dimensional (N-D) space and 1-D space. The Hilbert curve has been applied to image processing as a scanning technique (Hilbert scan). Applications to multi-dimensional image processing are also studied. In this application. We use the N-D Hilbert scan which maps N-D data to 1-D data along the N-D Hilbert curve. However, the N-D Hilbert scan is the application limited to data in a hyper-cube region. In this paper, we present a novel algorithm for generating N-D pseudo-Hilbert curves in a hyper-rectangular parallelepiped region. Our algorithm is suitable for real-time processing and is easy to implement in hardware, since it is a simple and non-recursive computation using look-up tables.

Region-based image coding with multiple algorithms

The wide usage of small satellite imagery, especially its commercialization, makes data-based onboard compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data generated onboard and the very limited downlink bandwidth. The authors propose a method that encodes different regions with different algorithms. The authors use three shape-adaptive image compression algorithms as the candidates. The first one is a JPEG-based algorithm, the second one is based on the object-based wavelet transform method proposed by Katata et al. (1997), and the third adopts Hilbert scanning of the regions of interest followed by one-dimensional (1-D) wavelet transform. The three algorithms are also applied to the full image so that one can compare their performance on a whole rectangular image. The authors use eight Landsat TM multispectral images and another 12 small satellite single-band images as their data set. The results show that these compression algorithms have significantly different performance for different regions.

Front view gait recognition using Spherical Space Model with Human Point Clouds

In this paper, we propose a novel gait recognition framework which is Spherical Space Model with Human Point Clouds (SSM-HPC). A new gait representation is also introduced, which is called Marching in Place (MIP) gait and preserves the spatiotemporal characteristics of individual gait manner. Various researches for gait recognition have used human silhouette images from moving picture. This research uses Three Dimensional (3D) point clouds data of human body obtained from stereo camera, which has the scale-invariant property. The framework is applied for frontal view gait recognition. This framework showed superior results for gait recognition rate than other gait recognition methods.

Fast Polar Harmonic Transforms

Polar Harmonic Transform (PHT) is termed to represent a set of transforms those kernels are basic waves and harmonic in nature. PHTs consist of Polar Complex Exponential Transform (PCET), Polar Cosine Transform (PCT) and Polar Sine Transform (PST). They are proposed to represent invariant image patterns for two dimensional image retrieval and pattern recognition tasks. They are demonstrated to show superiorities comparing with other methods on describing rotation invariant patterns for images. Kernel computation of PHTs is also simple and has no numerical stability issue. However in order to increase the computation speed, fast computation method is needed especially for real world applications like limited computing environments, large image databases and realtime systems. This paper presents Fast Polar Harmonic Transforms (FPHTs) including Fast Polar Complex Exponential Transform (FPCET), Fast Polar Cosine Transform (FPCT) and Fast Polar Sine Transform (FPST) that are deduced based on mathematical properties of trigonometric functions. The proposed FPHTs are averagely over 6 ∼ 8 times faster than PHTs that significantly boost computation process. The experimental results on both synthetic and real data are given to illustrate the effectiveness of the proposed fast transforms.