Color Map Images Research Articles

Lossless compression of color-mapped images

In a color-mapped (pseudo-color) image, pixel values repre- sent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with pseudo-color images, while universal compressors, such as Gzip, Pkzip and Compress, yield better compression gain. Recently, Burrows and Wheeler introduced the Block Sorting Lossless Data Com- pression Algorithm (BWA). The BWA algorithm received considerable attention. It achieves compression rates as good as context-based meth- ods, such as PPM, but at execution speeds closer to Ziv-Lempel tech- niques. The BWA algorithm is mainly composed of a block-sorting trans- formation which is known as Burrows-Wheeler Transformation (BWT), followed by Move-To-Front (MTF) coding. We introduce a new block transformation, Linear Order Transformation (LOT). We delineate its re- lationship to Burrows-Wheeler Transformation and show that LOT is faster than BWT transformation. We then show that when MTF coder is employed after the LOT, the compression gain obtained is better than the well-known compression techniques, such as GIF, JPEG, CALIC, Gzip, LZW (Unix Compress) and the BWA for pseudo-color images.

FRACTAL IMAGE ANALYSIS OF NATURAL SCENES AND MEDICAL IMAGES

We construct color map images of fractal dimension distribution from natural scenes and medical images by applying the box-counting method locally. The map images clearly show the difference between clouds and rocks, as well as between cancer parts and normal tissue in the colon. The method is simple and may be expected to be applicable to a real-time video-data processing.

On ordering color maps for lossless predictive coding

Linear predictive techniques perform poorly when used with color-mapped images where pixel values represent indices that point to color values in a look-up table. Reordering the color table, however, can lead to a lower entropy of prediction errors. In this paper, we investigate the problem of ordering the color table such that the absolute sum of prediction errors is minimized. The problem turns out to be intractable, even for the simple case of one-dimensional (1-D) prediction schemes. We give two heuristic solutions for the problem and use them for ordering the color table prior to encoding the image by lossless predictive techniques. We demonstrate that significant improvements in actual bit rates can be achieved over dictionary-based coding schemes that are commonly employed for color-mapped images.

Segmentation of map image using opponent color dimensions

This article describes a method for segmentation of color geographic map images based on color opponency. In this method, a color-map image is transformed into a color opponent representation as proposed for human vision. The color contrast is enhanced in a manner analogous to the opponent surround receptive fields. The enhancement process separates adjacent foreground and background regions into pairs of opponent colors. The segmentation process can then be easily completed based on this opponent structure. © 1996 John Wiley & Sons, Inc.

Segmentation of map image using opponent color dimensions

This article describes a method for segmentation of color geographic map images based on color opponency. In this method, a color-map image is transformed into a color opponent representation as proposed for human vision. The color contrast is enhanced in a manner analogous to the opponent surround receptive fields. The enhancement process separates adjacent foreground and background regions into pairs of opponent colors. The segmentation process can then be easily completed based on this opponent structure. © 1996 John Wiley & Sons, Inc.

Color image segmentation using fuzzy clustering and supervised learning

We propose a technique for the segmentation of color map images by means of an algorithm based on fuzzy clustering and prototype optimization. Its purpose is to facilitate the extraction of lines and characters from a wide variety of geographical map images. In this method, segmentation is considered to be a process of pixel classification. The fuzzy c-means clustering algorithm is applied to a number of training areas taken from a selection of different color map images. Prototypes, generated from the clustered pixels, that satisfy a set of validation criteria are then optimized using a neural network with supervised learning. The image is segmented using the optimized prototypes according to the nearest neighbor rule. The method has been verified to work efficiently with real geographical map data.

Text extraction from color map images

Separation of characters and lines in color map images, especially when they are connected or overlapped, is a very challenging task in image analysis. We present a method to tackle this problem using robust line tracing, connected component analysis, and color clustering algorithms. Good results have been obtained using this method with real test images.

Compression of color-mapped images

Multispectral data is often displayed and stored as a color-mapped or pseudo-color image. Pseudo-color is also used to enhance features in a single-band image. The use of pseudo-color tends to rearrange the structure in the image in such a way as to prevent efficient compression. This structure can be restored by sorting the color maps. Restoration of the structure increases the efficiency of lossless compression and permits the use of lossy compression algorithms. The latter benefit is especially useful for many progressive transmission algorithms. >

Character and line extraction from color map images using a multi-layer neural network

A multi-layer neural network based technique is presented in this paper for extracting characters and lines from color geographic map images. In this method, a neural network is first trained with feature values at known character and line pixels and background pixels and then used for image classification. The image segmentation problem is treated as a pattern classification process and the neural network classifier is used to generate non-linear decision regions to separate the foreground and background of an image that may contain a number of non-uniform regions with different colors. The method has been verified to work well with experimental studies.