Search

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.

This paper proposes an improvement to an interband version of the linear prediction approach for lossless compression of hyperspectral images. The improvements consisted of the use of non-predictable bands and the varied size of the sample set. Our improved method achieved an average compression ratio of 3.19 using 13 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images, compared to 3.08 in the basic method.

Methods for noise reduction in multicomponent spectral images are developed and discussed. Multicomponent spectral images can be corrupted by noise either on all the channels or on some of the channels only. In the first case there are two possibilities: either the noise is on all the channels in the same way or the noise is randomly distributed on all the channels. We studied two methods for noise reduction directly on the multicomponent spectral image: the vector median filter and our new method, the spectrum smoothing, which does not care about neighbouring pixels but tries to reduce noise on one pixel at a time. The idea behind spectrum smoothing lies on the nature of a color spectrum. Color spectrum is naturally smooth, and does not have any peaks, unlike a noisy spectrum would have. If some of the channels are noisy, there is a problem of finding the noisy channels. We came into a conclusion that if a channel correlates poorly with the neighboring channel, the channel can be considered noisy, and filtering is applied to that channel. Results from our new spectrum smoothing filter were very promising for Gaussian noise compared to Gaussian 3 by 3 filter and mean 5 by 5 filter.

Several powerful lossy compression methods have been developed for hyperspectral images. However, it is difficult to determine sufficient quality for reconstructed hyperspectral images. We have measured the information loss from the lossy compression with Signal-to-Noise-Ratio (SNR) and Peak-Signal-to-Noise-Ratio (PSNR). To get more illustrative error measures unsupervised K-means clustering combined with spectral matching methods was used. Spectral matching methods include Euclidean distance, Spectral Similarity Value (SSV) and Spectral Angle Mapper (SAM). We used two AVIRIS radiance images, which were compressed with three different methods: the Self-Organizing Map (SOM), Principal Component Analysis (PCA) and three-dimensional wavelet transform combined with lossless BWT/Huffman encoding. The two-dimensional JPEG2000 compression method was applied to the eigenimages produced by the PCA. It was found that clustering combined with spectral matching is a good method to realize the image quality for many applications. The high classification accuracies have been achieved even at very high compression ratios. The SAM and the SSV are much more vulnerable for information loss caused by the lossy compression than the Euclidean distance. The results suggest that lossy compression is possible in many real-world segmentation applications. The PCA transform combined with JPEG2000 was the best compression method according to all error metrics.

This article presents an algorithm for finding and visualizing the shortest route between two points on a gray-level height map. The route is computed using gray-level distance transforms, which are variations of the Distance Transform on Curved Space (DTOCS). The basic Route DTOCS uses the chessboard kernel for calculating the distances between neighboring pixels, but variations, which take into account the larger distance between diagonal pixels, produce more accurate results, particularly for smooth and simple image surfaces. The route opimization algorithm is implemented using the Weighted Distance Transform on Curved Space (WDTOCS), which computes the piecewise Euclidean distance along the image surface, and the results are compared to the original Route DTOCS. The implementation of the algorithm is very simple, regardless of which distance definition is used.

The Distance Transform on Curved Space (DTOCS) can be used to calculate distances on a gray-level surface, but the route along which the shortest distance is found, is lost during the calculation. In this article a new method for finding and visualizing the shortest path between two points on a gray-level height map is presented. The method is simple to implement, and example route images show that it produces good results.

This paper presents the implementation of a new method for lossless compression of hyperspectral images for novel parallel environments. The method in question is an interband version of the linear prediction approach for hyperspectral images. The interband linear prediction method consists of two stages: predictive decorrelation that produces residuals and the entropy coding of the residuals. The results and comparisons with other methods are discussed. The speedup of the thread version is almost linear with respect to the number of processors.

In this study, noise reduction methods for spectral images using soft morphological filters are presented. Three methods are introduced: filtering individual pixels in the direction of their spectra, two-dimensional componentwise filtering, and three-dimensional cubic filtering. The research involved removing impulsive noise, particularly salt and pepper and bit error noise. Filter performance was measured both quantitatively using mean absolute error, mean square error and signal to-noise ratio and visually by observing individual spectral channels and individual spectra of filtered images. The best results were achieved using componentwise filtering according to both numerical and visual criteria.

We have developed a model to calculate chromaticity differences from surfaces defined from the MacAdam ellipses in CIE 1931 (x,y)-chromaticity diagram. The idea is to transform the MacAdam ellipses to circles on planes and to connect these planes to each other as two surfaces through interpolation. The distances along the surfaces are calculated by a method based on the Weighted Distance Transform On Curved Space (WDTOCS). The chromaticity difference is calculated by combining the distances on the basis of the ellipse parametrization. The surface model corrects the planar values in chromaticity difference calculation. Our method can be applied to any set of planar ellipses.

We have composed several lossy compression methods for multispectral images. These methods include the Self-Organizing Map (SOM), Principal Component Analysis (PCA) and the three-dimensional wavelet transform combined with traditional lossless coding methods. The two-dimensional DCT/JPEG, JPEG2000 and SPIHT compression methods were applied to eigenimages produced by the PCA. The information loss from the compression was measured with Signal-to-Noise-Ratio (SNR) and Peak-Signal-to-Noise ratio (PSNR). To get more illustrative error measures C-means clustering and Euclidean distance for spectral matching were used. The test image was an AVIRIS image with 224 bands and 512 lines in 614 columns. The PCA in the spectral dimension was the best method in terms of image quality and compression speed. If required, JPEG2000 or SPIHT can be applied in spatial dimensions to get better compression ratios.

In this paper, a new group of noise reduction methods for multispectral images is presented. First, a 1-dimensional Self-Organizing Map (SOM) is taught using the pixel vectors of the noisy multispectral image. Then, a gray-level index image is formed containing the indexes of the SOM vectors. Several gray-level noise reduction methods are applied to the index image for three noise types: impulse, Gaussian, and coherent noise. Tests are made for three kinds of noise distrubutions: for all channels, for channels 30-50, and for 9 selected channels. Error measures imply that the obtained results are very good for coherent noise images, but rather poor for other noise categories, compared to the bandwise coherent filter.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

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.

Deflection compensation of flexible boom structures in robot positioning is becoming an important part of machine automation. Positioning is usually done using tables containing the magnitude of the deflection with inverse kinematics solutions of a rigid manipulator. In this paper, a method for locating the tip of a flexible manipulator using machine vision and a method for positioning the tip of a flexible manipulator using neural networks are proposed. A machine vision system was used in the data collection phase to locate the boom tip and the collected data was used to train MLP-networks. The developed methods improve the accuracy of manipulator positioning, and it can be integrated in the control system of the manipulator. The methods have been tested in real-time laboratory environment, and the results were promising. During the testing, the locating and the positioning were noticed to function as required, yielding reliable results with sufficient computation times.

In this paper ,a new method for edge detection in multispectral imags is presented. It is based on the use of the Self-Organizing Map (SOM), Peano scan and a conventional edge detector. The method presented in this paper order the vectors of the original image in such a way that vectors that are near each other according to some similarity criterium have scalar ordering values near each other. This is achieved using a 2D self-organizing map and the Peano scan. After ordering, the original vector image reduces to a gray-value image, and a conventional edge detector can be applied. In this paper, the Laplace and the Canny edge detectors are used. It is shown, that using the proposed method sit is possible to find the same relevant edges that R-ordering based methods find. Furthermore, it is also possible to find edges in images which consist of metameric colors, i.e. images in which every pixel vector maps into the same location in RGB space. This is not possible using conventional edge detectors which use an RGB image as input. Finally, the new method is tested with a real-world airplane image, giving results comparable with R-ordering based methods.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

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.

The amount of data generated by ultraspectral sounders is so large that considerable savings in data storage and transmission bandwidth can be achieved using data compression. Due to this large amount of data, the data compression time is of utmost importance. Increasing the programmability of the commodity Graphics Processing Units (GPUs) offer potential for considerable increases in computation speeds in applications that are data parallel. In our experiments, we implemented a spectral image data compression method called Linear Prediction with Constant Coefficients (LP-CC) using NVIDIA's CUDA parallel computing architecture. LP-CC compression method represents a current state-of-the-art technique in lossless compression of ultraspectral sounder data. The method showed an average compression ratio of 3.39 when applied to publicly available NASA AIRS data. We achieved a speed-up of 86 compared to a single threaded CPU version. Thus, the commodity GPU was able to significantly decrease the computational time of a compression algorithm based on a constant coefficient linear prediction.

In this paper, a new fast image compression method utilizing an improved version of the Distance Function On Curved Space (DTOCS), is presented. The maximas of distances are used directly to select control points. Also a new concept, a varying structuring element, a curvature constant, is applied to the calculation of distances. Its influence on compression results is studied.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper, two control point-based image compression methods are presented. In the first method, the encoding is based on the roughness of the surface defined by the gray levels of the image. The second method utilizes a new distance transform, called the Distance Transform on Curved Space (DTOCS). The related compression ratios of both of the methods are very good. Also the computations needed are quite simple and require only a short processing time. The study includes the investigation of properties of the control point-based interpolation.

We present the implementation of a lossless hyperspectral image compression method for novel parallel environments. The method is an interband version of a linear prediction approach for hyperspectral images. The interband linear prediction method consists of two stages: predictive decorrelation that produces residuals and the entropy coding of the residuals. The compression part is embarrassingly parallel, while the decompression part uses pipelining to parallelize the method. The results and comparisons with other methods are discussed. The speedup of the thread version is almost linear with respect to the number of processors.

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.

Advances in colour image sensors and computer technology have opened up new opportunities for colour-based image analysis by allowing it to use multispectral images - images that have tens or even hundreds of spectral colour channels. As multispectral images usually occupy large amounts of memory, some suitable way is needed to compress such images and to represent them efficiently. Image compression has been one of the mainstream research topics for a long time; however, the research usually focuses on compressing images that are intended to be finally seen by humans. While many traditional methods can also be reused for multispectral images, some features of the multispectral images can be addressed differently than in traditional images. This paper describes a way of how to represent the multispectral colour data as a linear combination of colours contained in the clusters of colours that most frequently occur in an image.

In this paper we present a method for optical paper surface roughness measurement, which overcomes the disadvantages of the traditional methods. Airflow-based roughness measurement methods and profilometer require expensive special equipment, essential laboratory conditions, are contact-based and slow and unsuitable for on-line control purposes methods. We employed an optical microscope with a built-in CCD-camera to take images of paper surface. The obtained image is considered as a texture. We applied statistical brightness measures and fractal dimension analysis for texture analysis. We have found a strong correlation between the roughness and a fractal dimension. Our method is non-contact–based, fast and is suitable for on-line control measurements in the paper industry.