274 research outputs found

    NASA Tech Briefs, September 2008

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    Topics covered include: Nanotip Carpets as Antireflection Surfaces; Nano-Engineered Catalysts for Direct Methanol Fuel Cells; Capillography of Mats of Nanofibers; Directed Growth of Carbon Nanotubes Across Gaps; High-Voltage, Asymmetric-Waveform Generator; Magic-T Junction Using Microstrip/Slotline Transitions; On-Wafer Measurement of a Silicon-Based CMOS VCO at 324 GHz; Group-III Nitride Field Emitters; HEMT Amplifiers and Equipment for their On-Wafer Testing; Thermal Spray Formation of Polymer Coatings; Improved Gas Filling and Sealing of an HC-PCF; Making More-Complex Molecules Using Superthermal Atom/Molecule Collisions; Nematic Cells for Digital Light Deflection; Improved Silica Aerogel Composite Materials; Microgravity, Mesh-Crawling Legged Robots; Advanced Active-Magnetic-Bearing Thrust- Measurement System; Thermally Actuated Hydraulic Pumps; A New, Highly Improved Two-Cycle Engine; Flexible Structural-Health-Monitoring Sheets; Alignment Pins for Assembling and Disassembling Structures; Purifying Nucleic Acids from Samples of Extremely Low Biomass; Adjustable-Viewing-Angle Endoscopic Tool for Skull Base and Brain Surgery; UV-Resistant Non-Spore-Forming Bacteria From Spacecraft-Assembly Facilities; Hard-X-Ray/Soft-Gamma-Ray Imaging Sensor Assembly for Astronomy; Simplified Modeling of Oxidation of Hydrocarbons; Near-Field Spectroscopy with Nanoparticles Deposited by AFM; Light Collimator and Monitor for a Spectroradiometer; Hyperspectral Fluorescence and Reflectance Imaging Instrument; Improving the Optical Quality Factor of the WGM Resonator; Ultra-Stable Beacon Source for Laboratory Testing of Optical Tracking; Transmissive Diffractive Optical Element Solar Concentrators; Delaying Trains of Short Light Pulses in WGM Resonators; Toward Better Modeling of Supercritical Turbulent Mixing; JPEG 2000 Encoding with Perceptual Distortion Control; Intelligent Integrated Health Management for a System of Systems; Delay Banking for Managing Air Traffic; and Spline-Based Smoothing of Airfoil Curvatures

    JERS-1 SAR and LANDSAT-5 TM image data fusion: An application approach for lithological mapping

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    Satellite image data fusion is an image processing set of procedures utilise either for image optimisation for visual photointerpretation, or for automated thematic classification with low error rate and high accuracy. Lithological mapping using remote sensing image data relies on the spectral and textural information of the rock units of the area to be mapped. These pieces of information can be derived from Landsat optical TM and JERS-1 SAR images respectively. Prior to extracting such information (spectral and textural) and fusing them together, geometric image co-registration between TM and the SAR, atmospheric correction of the TM, and SAR despeckling are required. In this thesis, an appropriate atmospheric model is developed and implemented utilising the dark pixel subtraction method for atmospheric correction. For SAR despeckling, an efficient new method is also developed to test whether the SAR filter used remove the textural information or not. For image optimisation for visual photointerpretation, a new method of spectral coding of the six bands of the optical TM data is developed. The new spectral coding method is used to produce efficient colour composite with high separability between the spectral classes similar to that if the whole six optical TM bands are used together. This spectral coded colour composite is used as a spectral component, which is then fused with the textural component represented by the despeckled JERS-1 SAR using the fusion tools, including the colour transform and the PCT. The Grey Level Cooccurrence Matrix (GLCM) technique is used to build the textural data set using the speckle filtered JERS-1 SAR data making seven textural GLCM measures. For automated thematic mapping and by the use of both the six TM spectral data and the seven textural GLCM measures, a new method of classification has been developed using the Maximum Likelihood Classifier (MLC). The method is named the sequential maximum likelihood classification and works efficiently by comparison the classified textural pixels, the classified spectral pixels, and the classified textural-spectral pixels, and gives the means of utilising the textural and spectral information for automated lithological mapping

    Perceptual Fidelity for Digital Color Imagery

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    The problem of measuring the fidelity of digital color images in a manner that corresponds to human perceptual assessments is addressed. Experiments are performed to validate human visual system (HVS) models, which provide access to a \u27perceptual space\u27 in which visual distortions may be measured, and then a model is proposed for assessing the perceptual fidelity of digital color image. Color Mach bands are produced in the first experiment, demonstrating that, as in the brightness channel, low spatial frequency attenuation occurs in the chromatic channels of the HVS. In the second experiment, a correlation between the chromatic channels of the HVS model and color discrimination axes of color blind observers is demonstrated. Removing variation from one of the chromatic channels of a natural image produces a color-distorted image which the color blind subjects cannot distinguish from the original. Removing variation from the other chromatic channel produces an image that appears colorful to normally-sighted observers, but monochrome to the color blind observers. The third experiment shows that a Gabor filter-based HVS model produces illusory contours in several illusory contour stimuli. These results provide a unique validation of multiple-channel HVS models which process the image in multiple spatial frequency bands that are tuned to match measured sensitivities of neurons in the primary visual cortex of cats and monkeys. Finally, the multiple-channel processing used in the illusory contour experiment is combined with the color vision model from the first two experiments to produce a multiple-channel, color HVS model for measuring perceptual fidelity of color images. A demonstration of the model shows that the structure of the new model is correct. However, inaccurate parameter values for the multiple-channel processing of the chromatic channels cause over-prediction of visible differences in these channels

    Discrete Wavelet Transforms

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    The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

    Two and three dimensional segmentation of multimodal imagery

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    The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

    Change blindness: eradication of gestalt strategies

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    Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

    BIOLOGICALLY-INFORMED COMPUTATIONAL MODELS OF HARMONIC SOUND DETECTION AND IDENTIFICATION

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    Harmonic sounds or harmonic components of sounds are often fused into a single percept by the auditory system. Although the exact neural mechanisms for harmonic sensitivity remain unclear, it arises presumably in the auditory cortex because subcortical neurons typically prefer only a single frequency. Pitch sensitive units and harmonic template units found in awake marmoset auditory cortex are sensitive to temporal and spectral periodicity, respectively. This thesis is a study of possible computational mechanisms underlying cortical harmonic selectivity. To examine whether harmonic selectivity is related to statistical regularities of natural sounds, simulated auditory nerve responses to natural sounds were used in principal component analysis in comparison with independent component analysis, which yielded harmonic-sensitive model units with similar population distribution as real cortical neurons in terms of harmonic selectivity metrics. This result suggests that the variability of cortical harmonic selectivity may provide an efficient population representation of natural sounds. Several network models of spectral selectivity mechanisms are investigated. As a side study, adding synaptic depletion to an integrate-and-fire model could explain the observed modulation-sensitive units, which are related to pitch-sensitive units but cannot account for precise temporal regularity. When a feed-forward network is trained to detect harmonics, the result is always a sieve, which is excited by integer multiples of the fundamental frequency and inhibited by half-integer multiples. The sieve persists over a wide variety of conditions including changing evaluation criteria, incorporating Dale’s principle, and adding a hidden layer. A recurrent network trained by Hebbian learning produces harmonic-selective by a novel dynamical mechanism that could be explained by a Lyapunov function which favors inputs that match the learned frequency correlations. These model neurons have sieve-like weights like the harmonic template units when probed by random harmonic stimuli, despite there being no sieve pattern anywhere in the network’s weights. Online stimulus design has the potential to facilitate future experiments on nonlinear sensory neurons. We accelerated the sound-from-texture algorithm to enable online adaptive experimental design to maximize the activities of sparsely responding cortical units. We calculated the optimal stimuli for harmonic-selective units and investigated model-based information-theoretic method for stimulus optimization
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