Pekka J. Toivanen

New geodesic distance transforms for gray-scale images

In this paper, two new geodesic distance transforms for gray-scale images are presented. The first transform, the Distance Transform on Curved Space (DTOCS), performs the calculation with integer numbers. The second transform, the Weighted Distance Transform on Curved Space (WDTOCS), gives a weighted distance map with real numbers for an arbitrary gray-value image. Both transforms give a distance map in which the distance value of a single point corresponds to the length of the shortest discrete 8-path to the nearest background point. Both differ from the previously presented gray-level distance transforms by not weighting the distance values directly by the gray-values, but by gray-value differences.

Clustered DPCM for the lossless compression of hyperspectral images

A clustered differential pulse code modulation lossless compression method for hyperspectral images is presented. The spectra of a hyperspectral image is clustered, and an optimized predictor is calculated for each cluster. Prediction is performed using a linear predictor. After prediction, the difference between the predicted and original values is computed. The difference is entropy-coded using an adaptive entropy coder for each cluster. The achieved compression ratios presented here are compared with those of existing methods. The results show that the proposed lossless compression method for hyperspectral images outperforms previous methods.

Shortest routes on varying height surfaces using gray-level distance transforms

The distance transform on curved space (DTOCS) and its locally Euclidean modification weighted DTOCS (WDTOCS) calculate distances along gray-level surfaces. This article presents the Route DTOCS algorithm for finding and visualizing the shortest route between two points on a gray-level height map, and also introduces new distance definitions producing more accurate global distances. The algorithm is very simple to implement, and finds all optimal paths between the two points at once. The Route DTOCS is an efficient 2D approach to finding routes on a 3D surface. It also provides a more flexible solution to obstacle avoidance problems than the constrained distance transform.

Distance and nearest neighbor transforms on gray-level surfaces

This article presents an efficient priority pixel queue algorithm for calculating the distance transform on curved space (DTOCS), the corresponding nearest neighbor transform, and the new projection DTOCS (PDTOCS). The transforms provide tools for approximating distances and finding shortest paths on gray-level surfaces. Surface variation, or roughness, can also be measured.

Correlation-based band-ordering heuristic for lossless compression of hyperspectral sounder data

A novel algorithm for the lossless compression of hyperspectral sounding data is presented. The algorithm rests upon an efficient technique for three-dimensional image band reordering. The technique is based on a correlation factor. The correlation-based band ordering gives 5% higher compression ratios than natural ordering does. On the other hand, the obtained compression ratios are within a percent of those produced by optimal ordering, but the computational time is much lower compared to the optimal ordering. The low computational complexity of the algorithm is based on the use of correlation for the band ordering. Moreover, the algorithm results in 7% to 12% improvement over fast nearest neighbor reordering scheme versions of JPEG-LS and the context-based adaptive lossless image codec algorithms.

Linear prediction in lossless compression of hyperspectral images

This study proposes an interband version of the linear prediction approach for hyperspectral images. Linear prediction represents one of the best performing and most practical and general purpose lossless image compression techniques known today. The interband linear prediction method consists of two stages: predictive decorrelation producing residuals, and entropy coding of these residuals. Our method achieved an average compression ratio of 3.23 using 13 airborne visible/infrared imaging spectrometer (AVIRIS) images.

Lossless Hyperspectral Image Compression via Linear Prediction

This paper proposes an interband version of the linear prediction approach for hyperspectral images. Linear prediction represents one of the best performing and most practical and general purpose lossless image compression techniques known today. The interband linear prediction method consists of two stages: predictive decorrelation producing residuals and entropy coding of the residuals. Our method achieved a compression ratio in the range of 3.02 to 3.14 using 13 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Digital watermarking of spectral images in PCA/wavelet-transform domain

Digital imaging continues its triumphal march especially in geoscience and remote sensing. Digital information can be easily processed, manipulated and copied. Thus, the need for document ownership verification arises. In this study, we propose a watermarking method for spectral images. The watermark is embedded in a transform domain of the spectral image. The transform is based on the principal component analysis and on the wavelet transform. With the inverse transforms the watermarked spectral image is reconstructed. The experiments indicate that the watermark is robust against attacks in lossy compression but more sensitive against mean filtering.

Edge Detection of Multispectral Images Using the 1-D Self-Organizing Map

In this paper, a new method for edge detection in multispectral images is presented. It is based on the use of the Self-Organizing Map (SOM) and a conventional edge detector. The method presented in this paper orders the vectors of the original image in such a way that vectors that are near each other according to some similarity criterium should have scalar ordering values near each other. This is achieved using the 1-dimensional Self-Organizing Map. After ordering, the original vector image reduces to a gray-value image, and conventional edge detectors can be applied. In this paper, the Laplace and Canny edge detectors are used. It is shown, that using the Self-Organizing Map (SOM) in ordering the vectors of the original spectral image it is possible to find also those edges that the R-ordering based methods miss.

Image compression by selecting control points using distance function on curved space

Abstract In this paper, a new image compression method is presented. It is based on a new morphological distance transform, the Distance Function on Curved Space, which is a generalization to the geodesic distance transformation. Also a new interpolation scheme based on the use of group-morphological operations is presented.