Multispectral image compression using the wavelet transform

Multispectral images contain information from several spectral wavelengths and currently multispectral images are widely used in remote sensing and they are becoming more common in the field of computer vision and in industrial applications. Typically, one multispectral image in remote sensing may occupy hundreds of megabytes of disk space and several this kind of images may be received from a single measurement. This study considers the compression of multispectral images. The lossy compression is based on the wavelet transform and we compare the suitability of different waveletfilters for the compression. A method for selecting a wavelet filter for the compression and reconstruction of multispectral images is developed. The performance of the multidimensional wavelet transform based compression is compared to other compression methods like PCA, ICA, SPIHT, and DCT/JPEG. The quality of the compression and reconstruction is measured by quantitative measures like signal-to-noise ratio. In addition, we have developed a qualitative measure, which combines the information from the spatial and spectral dimensions of a multispectral image and which also accounts for the visual quality of the bands from the multispectral images.

New Distance Transforms for Gray Level Image Compression

This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4

A Technique for Digital Color Image Watermarking Using ICA

With the increase of use of digital media the need for the methods of multimedia protection becomes extremely important. The number of the solutions to the problem from encryption to watermarking is large and is growing every year. In this work digital image watermarking is considered, specifically a novel method of digital watermarking of color and spectral images. An overview of existing methods watermarking of color and grayscale images is given in the paper. Methods using independent component analysis (ICA) for detection and the ones using discrete wavelet transform (DWT) and discrete cosine transform (DCT) are considered in more detail. A novel method of watermarking proposed in this paper allows embedding of a color or spectral watermark image into color or spectral image consequently and successful extraction of the watermark out of the resultant watermarked image. A number of experiments have been performed on the quality of extraction depending on the parameters of the embedding procedure. Another set of experiments included the test of the robustness of the algorithm proposed. Three techniques have been chosen for that purpose: median filter, low-pass filter (LPF) and discrete cosine transform (DCT), which are a part of a widely known StirMark - Image Watermarking Robustness Test. The study shows that the proposed watermarking technique is fragile, i.e. watermark is altered by simple image processing operations. Moreover, we have found that the contents of the image to be watermarked do not affect the quality of the extraction. Mixing coefficients, that determine the amount of the key and watermark image in the result, should not exceed 1% of the original. The algorithm proposed has proven to be successful in the task of watermark embedding and extraction.

Improving Satellite Image Based Forest Inventory by Using A Priori Site Quality Information

Summary

Calibration of a desktop scanner and digital image analysis procedure for quantification of a root morphology

Selostus: Tasoskannerin ja digitaalisen kuva-analyysimenetelmän kalibrointi juurten morfologian kvantifioimiseksi

Calculation of wood volume and stem taper using terrestrial single-image close-range photogrammetry and contemporary software tools

Abstract

Stress intensity factor of wood from crack-tip displacement fields obtained from digital image processing

Abstract

An Image Enhancement Technique using MTF of Human eye

Image filtering is a highly demanded approach of image enhancement in digital imaging systems design. It is widely used in television and camera design technologies to improve the quality of an output image to avoid various problems such as image blurring problem thatgains importance in design of displays of large sizes and design of digital cameras. This thesis proposes a new image filtering method basedon visual characteristics of human eye such as MTF. In contrast to the traditional filtering methods based on human visual characteristics this thesis takes into account the anisotropy of the human eye vision. The proposed method is based on laboratory measurements of the human eye MTF and takes into account degradation of the image by the latter. This method improves an image in the way it will be degraded by human eye MTF to give perception of the original image quality. This thesis gives a basic understanding of an image filtering approach and the concept of MTF and describes an algorithm to perform an image enhancement based on MTF of human eye. Performed experiments have shown quite good results according to human evaluation. Suggestions to improve the algorithm are also given for the future improvements.

Embedding, Extraction and Detection of Digital Watermark in Spectral Images

Tässä diplomityössä tutkitaan tekniikoita, joillavesileima lisätään spektrikuvaan, ja menetelmiä, joilla vesileimat tunnistetaanja havaitaan spektrikuvista. PCA (Principal Component Analysis) -algoritmia käyttäen alkuperäisten kuvien spektriulottuvuutta vähennettiin. Vesileiman lisääminen spektrikuvaan suoritettiin muunnosavaruudessa. Ehdotetun mallin mukaisesti muunnosavaruuden komponentti korvattiin vesileiman ja toisen muunnosavaruuden komponentin lineaarikombinaatiolla. Lisäyksessä käytettävää parametrijoukkoa tutkittiin. Vesileimattujen kuvien laatu mitattiin ja analysoitiin. Suositukset vesileiman lisäykseen esitettiin. Useita menetelmiä käytettiin vesileimojen tunnistamiseen ja tunnistamisen tulokset analysoitiin. Vesileimojen kyky sietää erilaisia hyökkäyksiä tarkistettiin. Diplomityössä suoritettiin joukko havaitsemis-kokeita ottamalla huomioon vesileiman lisäyksessä käytetyt parametrit. ICA (Independent Component Analysis) -menetelmää pidetään yhtenä mahdollisena vaihtoehtona vesileiman havaitsemisessa.

Retinal Image Analysis Using Machine Vision

The topic of this thesis is studying how lesions in retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. Methods for equalizing uneven illumination in fundus images, detecting regions of poor image quality due toinadequate illumination, and recognizing abnormal lesions were developed duringthe work. The developed methods exploit mainly the color information and simpleshape features to detect lesions. In addition, a graphical tool for collecting lesion data was developed. The tool was used by an ophthalmologist who marked lesions in the images to help method development and evaluation. The tool is a general purpose one, and thus it is possible to reuse the tool in similar projects.The developed methods were tested with a separate test set of 128 color fundus images. From test results it was calculated how accurately methods classify abnormal funduses as abnormal (sensitivity) and healthy funduses as normal (specificity). The sensitivity values were 92% for hemorrhages, 73% for red small dots (microaneurysms and small hemorrhages), and 77% for exudates (hard and soft exudates). The specificity values were 75% for hemorrhages, 70% for red small dots, and 50% for exudates. Thus, the developed methods detected hemorrhages accurately and microaneurysms and exudates moderately.

Methods for Region Based Paper Surface Roughness Measurement

Paperin pinnan karheus on yksi paperin laatukriteereistä. Sitä mitataan fyysisestipaperin pintaa mittaavien laitteiden ja optisten laitteiden avulla. Mittaukset vaativat laboratorioolosuhteita, mutta nopeammille, suoraan linjalla tapahtuville mittauksilla olisi tarvetta paperiteollisuudessa. Paperin pinnan karheus voidaan ilmaista yhtenä näytteelle kohdistuvana karheusarvona. Tässä työssä näyte on jaettu merkitseviin alueisiin, ja jokaiselle alueelle on laskettu erillinen karheusarvo. Karheuden mittaukseen on käytetty useita menetelmiä. Yleisesti hyväksyttyä tilastollista menetelmää on käytetty tässä työssä etäisyysmuunnoksen lisäksi. Paperin pinnan karheudenmittauksessa on ollut tarvetta jakaa analysoitava näyte karheuden perusteella alueisiin. Aluejaon avulla voidaan rajata näytteestä selvästi karheampana esiintyvät alueet. Etäisyysmuunnos tuottaa alueita, joita on analysoitu. Näistä alueista on muodostettu yhtenäisiä alueita erilaisilla segmentointimenetelmillä. PNN -menetelmään (Pairwise Nearest Neighbor) ja naapurialueiden yhdistämiseen perustuvia algoritmeja on käytetty.Alueiden jakamiseen ja yhdistämiseen perustuvaa lähestymistapaa on myös tarkasteltu. Segmentoitujen kuvien validointi on yleensä tapahtunut ihmisen tarkastelemana. Tämän työn lähestymistapa on verrata yleisesti hyväksyttyä tilastollista menetelmää segmentoinnin tuloksiin. Korkea korrelaatio näiden tulosten välillä osoittaa onnistunutta segmentointia. Eri kokeiden tuloksia on verrattu keskenään hypoteesin testauksella. Työssä on analysoitu kahta näytesarjaa, joidenmittaukset on suoritettu OptiTopolla ja profilometrillä. Etäisyysmuunnoksen aloitusparametrit, joita muutettiin kokeiden aikana, olivat aloituspisteiden määrä ja sijainti. Samat parametrimuutokset tehtiin kaikille algoritmeille, joita käytettiin alueiden yhdistämiseen. Etäisyysmuunnoksen jälkeen korrelaatio oli voimakkaampaa profilometrillä mitatuille näytteille kuin OptiTopolla mitatuille näytteille. Segmentoiduilla OptiTopo -näytteillä korrelaatio parantui voimakkaammin kuin profilometrinäytteillä. PNN -menetelmän tuottamilla tuloksilla korrelaatio oli paras.

The role of culture in international students' image formation: the emergence of expected and perceived university image

Tämän tutkimuksen tavoitteena oli selvittää, vaikuttaako kansainvälisen opiskelijan kulttuuritausta opiskelijan odotetun ja koetun yliopistoimagon muodostumiseen. Jotta kulttuurin vaikutuksia yliopistoimagoon voitiin tutkia, tutkimuksessa tunnistettiin yliopistoimagon muodostumiseen oleellisesti vaikuttavat tekijät. Kulttuurin roolia organisaation imagon muodostumisessa ei ole tutkittu aiemmissa tieteellisissä julkaisuissa. Näin ollen tämän tutkimuksen voidaan katsoa edistäneen nykyistä imagotutkimusta. Tutkimuksen kohdeyliopistona oli Lappeenrannan teknillinen yliopisto (LTY). Tutkimuksen empiirinen osa toteutettiin kvantitatiivisena Internet - pohjaisena kyselytutkimuksena tilastollisen analyysin menetelmin. Otos (N=179) koostui kaikista Lappeenrannan teknillisessä yliopistossa lukuvuonna 2005-2006 opiskelleista kansainvälisistä opiskelijoista. Kyselyyn vastasi 68,7 % opiskelijoista. Johtopäätöksenä voidaan todeta, että kulttuurilla ei ole merkittävää vaikutusta yliopistoimagon muodostumiseen. Tutkimuksessa saatiin selville, että yliopiston Internet-sivujen laatu vaikuttaa positiivisesti odotetun yliopistoimagon muodostumiseen, kun taas koettuun yliopistoimagoon vaikuttavat positiivisesti odotettu yliopistoimago, pedagoginen laatu sekä opetusympäristö. Markkinoinnin näkökulmasta tulokset voidaan vetää yhteen toteamalla, että yliopistojen ei tarvitsisi räätälöidä tutkimuksessa tunnistettuja imagoon vaikuttavia tekijöitä eri kulttuureistatulevia opiskelijoita varten.

Selecting Wavelet Filter for Compression of Spectra from Multispectral Images

The problem of selecting anappropriate wavelet filter is always present in signal compression based on thewavelet transform. In this report, we propose a method to select a wavelet filter from a predefined set of filters for the compression of spectra from a multispectral image. The wavelet filter selection is based on the Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter for each spectrum has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectra from multispectral images. When a new image is to be compressed, a set of spectra from that image is selected, the spectra are classified by the trained LVQand the filter associated to the largest class is selected for the compression of every spectrum from the multispectral image. The results show, that almost inevery case our method finds the most suitable wavelet filter from the pre-defined set for the compression.