Image enlargement using fractal

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Interpolation of Low Resolution Images for Improved Accuracy in Human Face Recognition

In a wide range of face recognition applications, such as the surveillance camera in law enforcement, it is cannot provide enough resolution of face for recognition. The first part of this research demonstrates the impact of the image resolution on the performance of the face recognition system. The performance of several holistic face recognition algorithms is evaluated for low-resolution face images. For the classification, this research uses k-nearest neighbor (k-NN) and Extreme Learning Machine-based neural network (ELM). The recognition rate of these systems is a function in the image resolution. In the second part of this research, nearest neighbor, bilinear, and bicubic interpolation techniques are applies as a preprocessing step to increase the resolution of the input image to obtain better results. The results show that increasing the image resolution using the mentioned interpolation methods improves the performance of the recognition systems considerably

Development of Some Spatial-domain Preprocessing and Post-processing Algorithms for Better 2-D Up-scaling

Image super-resolution is an area of great interest in recent years and is extensively used in applications like video streaming, multimedia, internet technologies, consumer electronics, display and printing industries. Image super-resolution is a process of increasing the resolution of a given image without losing its integrity. Its most common application is to provide better visual effect after resizing a digital image for display or printing. One of the methods of improving the image resolution is through the employment of a 2-D interpolation. An up-scaled image should retain all the image details with very less degree of blurring meant for better visual quality. In literature, many efficient 2-D interpolation schemes are found that well preserve the image details in the up-scaled images; particularly at the regions with edges and fine details. Nevertheless, these existing interpolation schemes too give blurring effect in the up-scaled images due to the high frequency (HF) degradation during the up-sampling process. Hence, there is a scope to further improve their performance through the incorporation of various spatial domain pre-processing, post-processing and composite algorithms. Therefore, it is felt that there is sufficient scope to develop various efficient but simple pre-processing, post-processing and composite schemes to effectively restore the HF contents in the up-scaled images for various online and off-line applications. An efficient and widely used Lanczos-3 interpolation is taken for further performance improvement through the incorporation of various proposed algorithms. The various pre-processing algorithms developed in this thesis are summarized here. The term pre-processing refers to processing the low-resolution input image prior to image up-scaling. The various pre-processing algorithms proposed in this thesis are: Laplacian of Laplacian based global pre-processing (LLGP) scheme; Hybrid global pre-processing (HGP); Iterative Laplacian of Laplacian based global pre-processing (ILLGP); Unsharp masking based pre-processing (UMP); Iterative unsharp masking (IUM); Error based up-sampling(EU) scheme. The proposed algorithms: LLGP, HGP and ILLGP are three spatial domain preprocessing algorithms which are based on 4th, 6th and 8th order derivatives to alleviate nonuniform blurring in up-scaled images. These algorithms are used to obtain the high frequency (HF) extracts from an image by employing higher order derivatives and perform precise sharpening on a low resolution image to alleviate the blurring in its 2-D up-sampled counterpart. In case of unsharp masking based pre-processing (UMP) scheme, the blurred version of a low resolution image is used for HF extraction from the original version through image subtraction. The weighted version of the HF extracts are superimposed with the original image to produce a sharpened image prior to image up-scaling to counter blurring effectively. IUM makes use of many iterations to generate an unsharp mask which contains very high frequency (VHF) components. The VHF extract is the result of signal decomposition in terms of sub-bands using the concept of analysis filter bank. Since the degradation of VHF components is maximum, restoration of such components would produce much better restoration performance. EU is another pre-processing scheme in which the HF degradation due to image upscaling is extracted and is called prediction error. The prediction error contains the lost high frequency components. When this error is superimposed on the low resolution image prior to image up-sampling, blurring is considerably reduced in the up-scaled images. Various post-processing algorithms developed in this thesis are summarized in following. The term post-processing refers to processing the high resolution up-scaled image. The various post-processing algorithms proposed in this thesis are: Local adaptive Laplacian (LAL); Fuzzy weighted Laplacian (FWL); Legendre functional link artificial neural network(LFLANN). LAL is a non-fuzzy, local based scheme. The local regions of an up-scaled image with high variance are sharpened more than the region with moderate or low variance by employing a local adaptive Laplacian kernel. The weights of the LAL kernel are varied as per the normalized local variance so as to provide more degree of HF enhancement to high variance regions than the low variance counterpart to effectively counter the non-uniform blurring. Furthermore, FWL post-processing scheme with a higher degree of non-linearity is proposed to further improve the performance of LAL. FWL, being a fuzzy based mapping scheme, is highly nonlinear to resolve the blurring problem more effectively than LAL which employs a linear mapping. Another LFLANN based post-processing scheme is proposed here to minimize the cost function so as to reduce the blurring in a 2-D up-scaled image. Legendre polynomials are used for functional expansion of the input pattern-vector and provide high degree of nonlinearity. Therefore, the requirement of multiple layers can be replaced by single layer LFLANN architecture so as to reduce the cost function effectively for better restoration performance. With single layer architecture, it has reduced the computational complexity and hence is suitable for various real-time applications. There is a scope of further improvement of the stand-alone pre-processing and postprocessing schemes by combining them through composite schemes. Here, two spatial domain composite schemes, CS-I and CS-II are proposed to tackle non-uniform blurring in an up-scaled image. CS-I is developed by combining global iterative Laplacian (GIL) preprocessing scheme with LAL post-processing scheme. Another highly nonlinear composite scheme, CS-II is proposed which combines ILLGP scheme with a fuzzy weighted Laplacian post-processing scheme for more improved performance than the stand-alone schemes. Finally, it is observed that the proposed algorithms: ILLGP, IUM, FWL, LFLANN and CS-II are better algorithms in their respective categories for effectively reducing blurring in the up-scaled images

Earth Observatory Satellite system definition study. Report 3: Design cost trade-off studies and recommendations

An analysis of the design and cost tradeoff aspects of the Earth Observatory Satellite (EOS) development is presented. The design/cost factors that affect a series of mission/system level concepts are discussed. The subjects considered are as follows: (1) spacecraft subsystem cost tradeoffs, (2) ground system cost tradeoffs, and (3) program cost summary. Tables of data are provided to summarize the results of the analyses. Illustrations of the various spacecraft configurations are included

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg

An integrated study of earth resources in the state of California using remote sensing techniques

The author has identified the following significant results. A weighted stratified double sample design using hardcopy LANDSAT-1 and ground data was utilized in developmental studies for snow water content estimation. Study results gave a correlation coefficient of 0.80 between LANDSAT sample units estimates of snow water content and ground subsamples. A basin snow water content estimate allowable error was given as 1.00 percent at the 99 percent confidence level with the same budget level utilized in conventional snow surveys. Several evapotranspiration estimation models were selected for efficient application at each level of data to be sampled. An area estimation procedure for impervious surface types of differing impermeability adjacent to stream channels was developed. This technique employs a double sample of 1:125,000 color infrared hightflight transparency data with ground or large scale photography

Electromagnetic Waves

This book is dedicated to various aspects of electromagnetic wave theory and its applications in science and technology. The covered topics include the fundamental physics of electromagnetic waves, theory of electromagnetic wave propagation and scattering, methods of computational analysis, material characterization, electromagnetic properties of plasma, analysis and applications of periodic structures and waveguide components, and finally, the biological effects and medical applications of electromagnetic fields

Structural and Hydrothermal Inferences from a Magnetotelluric Survey across Mt. Ruapehu, New Zealand

This thesis explains the electrical conductivity structure of Mt. Ruapehu. To identify hydrothermal or volcanic components of the volcano, data from 25 magnetotelluric sites are analyzed. Data collected are first analyzed in the time domain prior to conversion into the frequency domain. Here, data are remote referenced, and the impedance tensors, tippers, apparent resistivity and phase values are calculated. These components are then analyzed to identify major features within the data. The new phase tensor ellipse method is applied to identify influential features and determine the dimensionality of data. This analysis indicates where it is appropriate to apply 1 or 2 dimensional inversion schemes. Dimensionality analysis led to 1-D modelling of the determinant impedance at each site; and limited 2-D profiles across the Tongariro Volcanic Centre boundaries. These models are used to create a simple 3-D structural model of the volcano that is then forward modelled. The results of the 3-D forward modelling indicate that the dominating features of the volcano's electrical structure have been identified in the previous models. Crater Lake is the only possible hydrothermal system on Mt. Ruapehu identified in this study. It is also very unlikely that any large coherent bodies of magma exist in the near surface. However, a second thin conductor laying somewhere between 10 and 30 km deep beneath the eastern flank may contain 13% melt and is the probable driving heat force beneath the volcano. The structure of Mt. Ruapehu can be split into seven layers. A resistive surface layer (100 ohm m) of young volcanic debris within the Tongariro Volcanic Centre that is up to 500 m thick near the crater. A conductive layer (10 - 30 ohm m) of wet, fractured and altered volcanic debris underlaying the younger debris throughout the Tongariro Volcanic Centre. A layer of Tertiary sediment under the Tongariro Volcanic Centre that extends to the south and west. This layer is electrically indistinguishable from the previous layer and extends to approximately sea level. A resistive layer (400 ohm m), and consistent with greywacke basement covers the entire field area. A second conductive layer (20 ohm m) is identified under the eastern flank of the volcano somewhere between the depths of 10 and 30 km. This layer is likely to be the heat and magma source driving the volcanic activity. A surrounding resistive layer extends beyond and below the second conductive layer mentioned above. This surrounding layer is electrically similar to the greywacke above. A very high resistivity layer (7000 ohm m) is identified below 80 km deep, and may be associated with the land/sea boundary or subduction zone to the east