Optical Character Recognition for Symbian OS

This Master's thesis addresses the design and implementation of the optical character recognition (OCR) system for a mobile device working on the Symbian operating system. The developed OCR system, named OCRCapriccio, emphasizes the modularity, effective extensibility and reuse. The system consists of two parts which are the graphical user interface and the OCR engine that was implemented as a plug-in. In fact, the plug-in includes two implementations of the OCR engine for enabling two types of recognition: the bitmap comparison based recognition and statistical recognition. The implementation results have shown that the approach based on bitmap comparison is more suitable for the Symbian environment because of its nature. Although the current implementation of bitmap comparison is lacking in accuracy, further development should be done in its direction. The biggest challenges of this work were related to developing an OCR scheme that would be suitable for Symbian OS Smartphones that have limited computational power and restricted resources.

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.

Characterization of fiber and vessel elements in pulp suspension images

The thesis is related to the topic of image-based characterization of fibers in pulp suspension during the papermaking process. Papermaking industry is focusing on process control optimization and automatization, which makes it possible to manufacture highquality products in a resource-efficient way. Being a part of the process control, pulp suspension analysis allows to predict and modify properties of the end product. This work is a part of the tree species identification task and focuses on analysis of fiber parameters in the pulp suspension at the wet stage of paper production. The existing machine vision methods for pulp characterization were investigated, and a method exploiting direction sensitive filtering, non-maximum suppression, hysteresis thresholding, tensor voting, and curve extraction from tensor maps was developed. Application of the method to the microscopic grayscale pulp images made it possible to detect curves corresponding to fibers in the pulp image and to compute their morphological characteristics. Performance of the method was evaluated based on the manually produced ground truth data. An accuracy of fiber characteristics estimation, including length, width, and curvature, for the acacia pulp images was found to be 84, 85, and 60% correspondingly.

Developing raster bands computed from LiDAR to improve forest

This study examines the use of di erent features derived from remotely sensed data in segmentation of forest stands. Surface interpolation methods were applied to LiDAR points in order to represent data in the form of grayscale images. Median and mean shift ltering was applied to the data for noise reduction. The ability of di erent compositions of rasters obtained from LiDAR data and an aerial image to maximize stand homogeneity in the segmentation was evaluated. The quality of forest stand delineations was assessed by the Akaike information criterion. The research was performed in co-operation with Arbonaut Ltd., Joensuu, Finland.

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.

Spectral retinal image processing and analysis for ophthalmology

Diabetic retinopathy, age-related macular degeneration and glaucoma are the leading causes of blindness worldwide. Automatic methods for diagnosis exist, but their performance is limited by the quality of the data. Spectral retinal images provide a significantly better representation of the colour information than common grayscale or red-green-blue retinal imaging, having the potential to improve the performance of automatic diagnosis methods. This work studies the image processing techniques required for composing spectral retinal images with accurate reflection spectra, including wavelength channel image registration, spectral and spatial calibration, illumination correction, and the estimation of depth information from image disparities. The composition of a spectral retinal image database of patients with diabetic retinopathy is described. The database includes gold standards for a number of pathologies and retinal structures, marked by two expert ophthalmologists. The diagnostic applications of the reflectance spectra are studied using supervised classifiers for lesion detection. In addition, inversion of a model of light transport is used to estimate histological parameters from the reflectance spectra. Experimental results suggest that the methods for composing, calibrating and postprocessing spectral images presented in this work can be used to improve the quality of the spectral data. The experiments on the direct and indirect use of the data show the diagnostic potential of spectral retinal data over standard retinal images. The use of spectral data could improve automatic and semi-automated diagnostics for the screening of retinal diseases, for the quantitative detection of retinal changes for follow-up, clinically relevant end-points for clinical studies and development of new therapeutic modalities.

Electrical and dielectric properties of uncoated and coated wood-free paper for electrophotography

The print substrate influences the print result in dry toner electrophotography, which is a widely used digital printing method. The influence of the substrate can be seen more easily in color printing, as that is a more complex process compared to monochrome printing. However, the print quality is also affected by the print substrate in grayscale printing. It is thus in the interests of both substrate producers and printing equipment manufacturers to understand the substrate properties that influence the quality of printed images in more detail. In dry toner electrophotography, the image is printed by transferring charged toner particles to the print substrate in the toner transfer nip, utilizing an electric field, in addition to the forces linked to the contact between toner particles and substrate in the nip. The toner transfer and the resulting image quality are thus influenced by the surface texture and the electrical and dielectric properties of the print substrate. In the investigation of the electrical and dielectric properties of the papers and the effects of substrate roughness, in addition to commercial papers, controlled sample sets were made on pilot paper machines and coating machines to exclude uncontrolled variables from the experiments. The electrical and dielectric properties of the papers investigated were electrical resistivity and conductivity, charge acceptance, charge decay, and the dielectric permittivity and losses at different frequencies, including the effect of temperature. The objective was to gain an understanding of how the electrical and dielectric properties are affected by normal variables in papermaking, including basis weight, material density, filler content, ion and moisture contents, and coating. In addition, the dependency of substrate resistivity on the electric field applied was investigated. Local discharging did not inhibit transfer with the paper roughness levels that are normal in electrophotographic color printing. The potential decay of paper revealed that the charge decay cannot be accurately described with a single exponential function, since in charge decay there are overlapping mechanisms of conduction and depolarization of paper. The resistivity of the paper depends on the NaCl content and exponentially on moisture content although it is also strongly dependent on the electric field applied. This dependency is influenced by the thickness, density, and filler contents of the paper. Furthermore, the Poole-Frenkel model can be applied to the resistivity of uncoated paper. The real part of the dielectric constant ε’ increases with NaCl content and relative humidity, but when these materials cannot polarize freely, the increase cannot be explained by summing the effects of their dielectric constants. Dependencies between the dielectric constant and dielectric loss factor and NaCl content, temperature, and frequency show that in the presence of a sufficient amount of moisture and NaCl, new structures with a relaxation time of the order of 10-3 s are formed in paper. The ε’ of coated papers is influenced by the addition of pigments and other coating additives with polarizable groups and due to the increase in density. The charging potential decreases and the electrical conductivity, potential decay rate, and dielectric constant of paper increase with increasing temperature. The dependencies are exponential and the temperature dependencies and their activation energies are altered by the ion content. The results have been utilized in manufacturing substrates for electrophotographic color printing.