Computational Techniques for Spectral Image Analysis

Multispectral images are becoming more common in the field of remote sensing, computer vision, and industrial applications. Due to the high accuracy of the multispectral information, it can be used as an important quality factor in the inspection of industrial products. Recently, the development on multispectral imaging systems and the computational analysis on the multispectral images have been the focus of a growing interest. In this thesis, three areas of multispectral image analysis are considered. First, a method for analyzing multispectral textured images was developed. The method is based on a spectral cooccurrence matrix, which contains information of the joint distribution of spectral classes in a spectral domain. Next, a procedure for estimating the illumination spectrum of the color images was developed. Proposed method can be used, for example, in color constancy, color correction, and in the content based search from color image databases. Finally, color filters for the optical pattern recognition were designed, and a prototype of a spectral vision system was constructed. The spectral vision system can be used to acquire a low dimensional component image set for the two dimensional spectral image reconstruction. The data obtained by the spectral vision system is small and therefore convenient for storing and transmitting a spectral image.

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.

Image fusion based on median filters and SOFM neural networks: a three-step scheme

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

Compressão Seletiva de Imagens Coloridas com Detecção Automática de Regiões de Interesse

There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE    Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS    Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS    A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was [Formula: see text], requiring [Formula: see text] s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS    A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Benthic cover map of Heron Reef derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

Maine, 1798 Raster Image

This layer is a digitized geo-referenced raster image of a 1798 map of Maine drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.

New Hampshire, 1796 Raster Image

This layer is a digitized geo-referenced raster image of a 1796 map of New Hampshire drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.

Vermont, 1796 Raster Image

This layer is a digitized geo-referenced raster image of a 1796 map of Vermont drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.

Massachusetts, 1797 Raster Image

This layer is a digitized geo-referenced raster image of a 1797 map of Massachusetts drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.

Rhode Island, 1797 Raster Image

This layer is a digitized geo-referenced raster image of a 1797 map of Rhode Island drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.

Connecticut, 1796 Raster Image

This layer is a digitized geo-referenced raster image of a 1796 map of Connecticut drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.

New York, 1799 Raster Image

This layer is a digitized geo-referenced raster image of a 1799 map of New York drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.