In Kwon Kim

Document image binarization based on topographic analysis using a water flow model

Abstract This paper proposes a local adaptive thresholding method based on a water flow model, in which an image surface is considered as a three-dimensional (3-D) terrain. To extract characters from backgrounds, we pour water onto the terrain surface. Water flows down to the lower regions of the terrain and fills valleys. Then, the thresholding process is applied to the amount of filled water for character extraction, in which the proposed thresholding method is applied to gray level document images consisting of characters and backgrounds. The proposed method based on a water flow model shows the property of locally adaptive thresholding. Computer simulation with synthetic and real document images shows that the proposed method yields effective adaptive thresholding results for binarization of document images.

Still image coding based on vector quantization and fractal approximation

In this paper, we propose a coding algorithm for still images using vector quantization (VQ) and fractal approximation, in which low-frequency components of an input image are approximated by VQ, and its residual is coded by fractal mapping. The conventional fractal coding algorithms indirectly used the gray patterns of an original image with contraction mapping, whereas the proposed fractal coding method employs an approximated and then decimated image as a domain pool and uses its gray patterns. Thus, the proposed algorithm utilizes fractal approximation without the constraint of contraction mapping. For approximation of an original image, we employ the discrete cosine transform (DCT) rather than conventional polynomial-based transforms. In addition, for variable blocksize segmentation, we use the fractal dimension of a block that represents the roughness of the gray surface of a region. Computer simulations with several test images show that the proposed method shows better performance than the conventional fractal coding methods for encoding still pictures.

Automated analysis of mixed documents consisting of printed Korean/alphanumeric texts and graphic images

An efficient algorithm is proposed that recognizes a mixed document consisting of printed Korean/alphanumeric text and graphic images. In the preprocessing step, an input document is skew-normalized, if necessary, by rotating it by an angle detected with the Hough transform. Then we separate the graphic image parts from the text parts by considering chain codes of connected components. We further separate each character using vertical and horizontal projections. In the recognition step, a mixed text consisting of two different sets of characters, e.g. , Korean and alphanumeric characters is recognized. Korean and alphanumeric characters are classified and each is recognized hierarchically using several effective features. The output is obtained by combining the recognized characters and separated graphic parts. An efficient automated analysis algorithm for mixed documents consisting of graphic images and two different sets of characters is proposed and its performance is demonstrated via computer simulation.

Block matching algorithm using a genetic algorithm

In this paper, we propose a block matching algorithm (BMA) using a genetic algorithm. The genetic algorithm was inspired by an information processing scheme which is used by nature. To use the genetic algorithm in 2D block matching, we encode, based on a quad-tree structure, the phenotype representing a motion vector, i.e., the genotype is represented by four symbol strings. The probability of mutation is differently set for each position in a symbol string. Computer simulation results show that we can have the peak signal to noise ratio (PSNR) of the proposed genetic-based BMA comparable to that of the three step search (TSS) or full search (FS) by varying the number of search points.

Image coding based on fractal approximation and vector quantization

We propose a coding algorithm for still images using fractal approximation and vector quantization (VQ). The conventional fractal coding algorithms indirectly used the gray patterns of an original image with contraction mapping, whereas the proposed fractal coding method employs a previously approximated image as a domain pool and uses its gray patterns. Thus, the proposed algorithm employs fractal approximation without the constraint of contraction mapping. To approximate an original image, we use an orthogonal polynomial transform and to encode the transform coefficients we employ VQ. Also, for variable block-size segmentation, we use the fractal dimension that represents the similarity of regions in a block and the roughness of the gray surface of a region. Computer simulations with several test images show that the proposed method shows better performance than the conventional fractal coding methods for encoding still pictures.<<ETX>>

Analysis of Mixed Korean Documents Using the Branch and Bound Algorithm Based on DP Matching

This paper presents an effective automated analysis system for mixed documents consisting of handwritten texts and graphic images. In the preprocessing step, an input image is binarized, then graphic regions are separated from text parts using chain codes of connected components. In the character recognition step, we recognize two different sets of handwritten characters: Korean and alphanumeric characters. Considering the structural complexity and variations of Korean characters, we separate them based on partial recognition results of vowels and extract primitive phonemes using a branch and bound algorithm based on dynamic programming (DP) matching. Finally, to validate recognition results, a dictionary and knowledge are employed. Computer simulation with 50 test documents shows that the proposed algorithm analyzes effectively mixed documents.

A fast encoding method without search for fractal image compression

A fast coding algorithm for images using vector quantization (VQ) and pixelwise fractal approximation is proposed. The low frequency component of an input image is approximated and its residual is used to calculate the scaling factor of the fractal transform. The scaling factor is compressed by transform VQ (TVQ). In the proposed method, to encode a digital image by an iterated function system (IFS), we use the pixel-based IFS (PIFS) rather than the block-based IFS: the scaling factor is computed for each pixel. In the proposed method, the scaling factor of each pixel is calculated with the constraint of contraction mapping and it is transformed by wavelets and quantized by VQ. For approximation of an original image, the variable block-size segmentation using quadtrees is employed. Because the proposed method calculates the scaling factor using the PIFS, the encoding time is faster than the conventional algorithm using block-based IFS with search.

Color image coding based on recurrent iterated function systems

This paper proposes a color image coding method based on recurrent iterated function systems (RIFSs). To encode a set of multi- spectral images, we apply a RIFS to multiset data consisting of three images. In the proposed method, the mappings not only between blocks within an individual spectral image but also between blocks of different spectral images are performed with contraction constraint. Simulation results show that the fractal coding based on the RIFS is useful for encoding concurrently a set of images by describing the similarity exist- ing between a pair of images. In addition, the proposed color coding method can be applied to subband images and moving image se- quences consisting of a set of images having similar gray patterns.

Edge detection in noisy images using a water-flow model

We present an edge detection method based on a water-flow model and gradient information. The gradient magnitude image emphasizes edges of objects in an image and effective extraction of well-defined and connected edges having large gradient values follows. The proposed method can be classified as a locally adaptive thresholding method. To show the effectiveness of the proposed method, its simulation results for various noise-free and noisy synthetic and real images are compared with those of conventional methods. In addition, edge evaluation results of five edge detection methods are shown quantitatively.

Sequence image coding based on fractal approximation using dynamic residual pools

We propose a two-layer sequence image coding algorithm based on residual block matching using fractal approximation. First, the motion compensation (MC) error signal is encoded by the discrete cosine transform (DCT). The motion vector and DCT coefficients are transmitted as the first layer and the residual signal of MC/DCT is encoded by fractal approximation and transmitted as the second layer. The second layer is encoded by the matching block selected from a dynamic residual pool. The reconstructed MC error image is used as a dynamic residual signal which is called a domain pool in conventional fractal coding. The computer simulation result by the proposed methods and the DCT-based methods shows that the performance improvement by the proposed method is significant.