Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

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

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms

Efficient image mosaicing for multi-robot visual underwater mapping

Robotic platforms have advanced greatly in terms of their remote sensing capabilities, including obtaining optical information using cameras. Alongside these advances, visual mapping has become a very active research area, which facilitates the mapping of areas inaccessible to humans. This requires the efficient processing of data to increase the final mosaic quality and computational efficiency. In this paper, we propose an efficient image mosaicing algorithm for large area visual mapping in underwater environments using multiple underwater robots. Our method identifies overlapping image pairs in the trajectories carried out by the different robots during the topology estimation process, being this a cornerstone for efficiently mapping large areas of the seafloor. We present comparative results based on challenging real underwater datasets, which simulated multi-robot mapping

Color ad spectral image assessment using novel quality and fidelity techniques

The ongoing development of the digital media has brought a new set of challenges with it. As images containing more than three wavelength bands, often called spectral images, are becoming a more integral part of everyday life, problems in the quality of the RGB reproduction from the spectral images have turned into an important area of research. The notion of image quality is often thought to comprise two distinctive areas – image quality itself and image fidelity, both dealing with similar questions, image quality being the degree of excellence of the image, and image fidelity the measure of the match of the image under study to the original. In this thesis, both image fidelity and image quality are considered, with an emphasis on the influence of color and spectral image features on both. There are very few works dedicated to the quality and fidelity of spectral images. Several novel image fidelity measures were developed in this study, which include kernel similarity measures and 3D-SSIM (structural similarity index). The kernel measures incorporate the polynomial, Gaussian radial basis function (RBF) and sigmoid kernels. The 3D-SSIM is an extension of a traditional gray-scale SSIM measure developed to incorporate spectral data. The novel image quality model presented in this study is based on the assumption that the statistical parameters of the spectra of an image influence the overall appearance. The spectral image quality model comprises three parameters of quality: colorfulness, vividness and naturalness. The quality prediction is done by modeling the preference function expressed in JNDs (just noticeable difference). Both image fidelity measures and the image quality model have proven to be effective in the respective experiments.

The importance of pre-treatment of spent hydrotreating catalysts on metals recovery

This work describes a three-step pre-treatment route for processing spent commercial NiMo/Al2O3 catalysts. Extraction of soluble coke with n-hexane and/or leaching of foulant elements with oxalic acid were performed before burning insoluble coke under air. Oxidized catalysts were leached with 9 mol L-1 sulfuric acid. Iron was the only foulant element partially leached by oxalic acid. The amount of insoluble matter in sulfuric acid was drastically reduced when iron and/or soluble coke were previously removed. Losses of active phase metals (Ni, Mo) during leaching with oxalic acid were compensated by the increase of their recovery in the sulfuric acid leachate.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

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.

Construction Plan Image Service for Mobile Devices

The forthcoming media revolution of exchanging paper documents to digital media in construction engineering requires new tools to be developed. The basis of this bachelor’s thesis was to explore the preliminary possibilities of exporting imagery from a Building Information Modelling –software to a mobile phone on a construction yard. This was done by producing a Web Service which uses the design software’s Application Programming Interface to interact with a structures model in order to produce the requested imagery. While mobile phones were found lacking as client devices, because of limited processing power and small displays, the implementation showed that the Tekla Structures API can be used to automatically produce various types of imagery. Web Services can be used to transfer this data to the client. Before further development the needs of the contractor, benefits for the building master and inspector and the full potential of the BIM-software need to be mapped out with surveys.

Image analysis for assessing broiler breeder behavior response to thermal environment

The research proposes a methodology for assessing broiler breeder response to changes in rearing thermal environment. The continuous video recording of a flock analyzed may offer compelling evidences of thermal comfort, as well as other indications of welfare. An algorithm for classifying specific broiler breeder behavior was developed. Videos were recorded over three boxes where 30 breeders were reared. The boxes were mounted inside an environmental chamber were ambient temperature varied from cold to hot. Digital images were processed based on the number of pixels, according to their light intensity variation and binary contrast allowing a sequence of behaviors related to welfare. The system used the default of x, y coordinates, where x represents the horizontal distance from the top left of the work area to the point P, and y is the vertical distance. The video images were observed, and a grid was developed for identifying the area the birds stayed and the time they spent at that place. The sequence was analyzed frame by frame confronting the data with specific adopted thermal neutral rearing standards. The grid mask overlapped the real bird image. The resulting image allows the visualization of clusters, as birds in flock behave in certain patterns. An algorithm indicating the breeder response to thermal environment was developed.

Cognitive Development of Very Low Birth Weight Children from Infancy to Pre-school Age

The survival of preterm born infants has increased but the prevalence of long-term morbidities has still remained high. Preterm born children are at an increased risk for various developmental impairments including both severe neurological deficits as well as deficits in cognitive development. According to the literature the developmental outcome perspective differs between countries, centers, and eras. Definitions of preterm infant vary between studies, and the follow-up has been carried out with diverse methods making the comparison less reliable. It is essential to offer parents upto-date information about the outcome of preterm infants born in the same area. A centralized follow-up of children at risk makes it possible to monitor the consequences of changes in the treatment practices of hospitals on developmental outcome. This thesis is part of a larger regional, prospective multidisciplinary follow-up project entitled “Development and Functioning of Very Low Birth Weight Infants from Infancy to School Age” (PIeniPAinoisten RIskilasten käyttäytyminen ja toimintakyky imeväisiästä kouluikään, PIPARI). The thesis consists of four original studies that present data of very low birth weight (VLBW) infants born between 2001 and 2006, who are followed up from the neonatal period until the age of five years. The main outcome measure was cognitive development and secondary outcomes were significant neurological deficits (cerebral palsy, CP, deafness, and blindness). In Study I, the early crying and fussing behavior of preterm infants was studied using parental diaries, and the relation of crying behavior and cognitive and motor development at the age of two years was assessed. In Study II, the developmental outcome (cognitive, CP, deafness, and blindness) at the age of two years was studied in relation to demographic, antenatal, neonatal, and brain imaging data. Development was studied in relationship to a full-term born control group born in the same hospital. In Study III, the stability of cognitive development was studied in VLBW and full-term groups by comparing the outcomes at the ages of two and five years. Finally, in Study IV the precursors of reading skills (phonological processing, rapid automatized naming, and letter knowledge) were assessed for VLBW and full-term children at the age of five years. Pre-reading skills were studied in relation to demographic, antenatal, neonatal, and brain imaging data. The main findings of the thesis were that VLBW infants who fussed or cried more in the infancy were not at greater risk for problems in their cognitive development. However, crying was associated with poorer motor development. The developmental outcome of the present population was better that has been reported earlier and this improvement covered also cognitive development. However, the difference to fullterm born peers was still significant. Major brain pathology and intestinal perforation were independent significant risk factors for adverse outcome, also when several individual risk factors were controlled for. Cognitive development at the age of two years was strongly related with development at the age of five years, stressing the importance of the early assessment, and the possibility for early interventions. Finally, VLBW children had poorer pre-reading skills compared with their full-term born peers, but the IQ was an important mediator even when children with mental retardation were excluded from the analysis. The findings suggest that counseling parents about the developmental perspectives of their preterm infant should be based on data covering the same birth hospital. Neonatal brain imaging data and neonatal morbidity are important predictors for developmental outcome. The findings of the present study stress the importance of both short-term (two years) and long-term (five years) follow-ups for the individual, and for improving the quality of care.

A distributed, cloud-ready, digital content processing and transformation platform and a specific use case

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

Rapid test for the evaluation of the activity of the prodrug hydroxymethylnitrofurazone in the processing of Trypanosoma cruzi messenger RNAs

No fully effective treatment has been developed since the discovery of Chagas' disease by Carlos Chagas in 1909. Since drug-resistant Trypanosoma cruzi strains are occurring and the current therapy is effectiveness in the acute phase but with various adverse side effects, more studies are needed to characterize the susceptibility of T. cruzi to new drugs. Many natural and/or synthetic substances showing trypanocidal activity have been used, even though they are not likely to be turned into clinically approved drugs. Originally, drug screening was performed using natural products, with only limited knowledge of the molecular mechanism involved in the development of diseases. Trans-splicing, which is unusual RNA processing reaction and occurs in nematodes and trypanosomes, implies the processing of polycistronic transcription units into individual mRNAs; a short transcript spliced leader (SL RNA) is trans-spliced to the acceptor pre-mRNA, giving origin to the mature mRNA. In the present study, permeable cells of T. cruzi epimastigote forms (Y, BOL and NCS strains) were treated to evaluate the interference of two drugs (hydroxymethylnitrofurazone - NFOH-121 and nitrofurazone) in the trans-splicing reaction using silver-stained PAGE analysis. Both drugs induced a significant reduction in RNA processing at concentrations from 5 to 12.5 µM. These data agreed with the biological findings, since the number of parasites decreased, especially with NFOH-121. This proposed methodology allows a rapid and cost-effective screening strategy for detecting drug interference in the trans-splicing mechanism of T. cruzi.