3,379 research outputs found

    Empirical Analysis of Aerial Camera Filters for Shoreline Mapping

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    Accurate, up-to-date national shoreline is critical in defining the territorial limits of the Unites States, updating nautical charts, and managing coastal resources. The National Oceanic and Atmospheric Administration (NOAA) delineates the interpreted shoreline photogrammetrically using tide-coordinated stereo photography acquired with black-and-white infrared emulsion. In this paper, we present the results of a two-phased study aimed at quantifying the effect of camera filter selection on the interpreted shoreline when utilizing this method of shoreline mapping

    Multispectral Palmprint Encoding and Recognition

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    Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

    Data compressive paradigm for spectral sensing and classification using electrically tunable detectors

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    This dissertation contains three major parts: (1) demonstration of the algorithmic spectrometry in the mid-IR sensing regime using spectrally tunable quantum dots-in-a-well (DWELL) IR detector without employing any spectral filters; (2) further demonstration of the spectral-classification capability of tunable DWELL IR focal-plane array (FPA), again without using any spectral filters; and (3) development of a generalized filter-free data-compressive spectral sensing paradigm using the DWELL detector that enables arbitrarily specified MS sensing (e.g., spectral matched filtering, slope sensing, multicolor sensing, etc.) without using any spectral filters and possibly under constrained acquisition times

    LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

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    The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

    Combining transverse field detectors and color filter arrays to improve multispectral imaging systems

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    This work focuses on the improvement of a multispectral imaging sensor based on transverse field detectors (TFDs). We aimed to achieve a higher color and spectral accuracy in the estimation of spectral reflectances from sensor responses. Such an improvement was done by combining these recently developed silicon-based sensors with color filter arrays (CFAs). Consequently, we sacrificed the filter-less full spatial resolution property of TFDs to narrow down the spectrally broad sensitivities of these sensors.We designed and performed several experiments to test the influence of different design features on the estimation quality (type of sensor, tunability, interleaved polarization, use of CFAs, type of CFAs, number of shots), some of which are exclusive to TFDs.We compared systems that use a TFD with systems that use normal monochrome sensors, both combined with multispectral CFAs as well as common RGB filters present in commercial digital color cameras. Results showed that a system that combines TFDs and CFAs performs better than systems with the same type of multispectral CFA and other sensors, or even the same TFDs combined with different kinds of filters used in common imaging systems. We propose CFA+TFD-based systems with one or two shots, depending on the possibility of using longer capturing times or not. Improved TFD systems thus emerge as an interesting possibility for multispectral acquisition, which overcomes the limited accuracy found in previous studies.Spanish Ministry of Economy and Competitiveness through the research project DPI2011-2320

    Quantitative analysis of multi-spectral fundus images

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    We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages

    Analysis of image noise in multispectral color acquisition

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    The design of a system for multispectral image capture will be influenced by the imaging application, such as image archiving, vision research, illuminant modification or improved (trichromatic) color reproduction. A key aspect of the system performance is the effect of noise, or error, when acquiring multiple color image records and processing of the data. This research provides an analysis that allows the prediction of the image-noise characteristics of systems for the capture of multispectral images. The effects of both detector noise and image processing quantization on the color information are considered, as is the correlation between the errors in the component signals. The above multivariate error-propagation analysis is then applied to an actual prototype system. Sources of image noise in both digital camera and image processing are related to colorimetric errors. Recommendations for detector characteristics and image processing for future systems are then discussed

    Optimal Filter Selection for Multispectral Object Classification Using Fast Binary Search

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    When designing multispectral imaging systems for classifying different spectra it is necessary to choose a small number of filters from a set with several hundred different ones. Tackling this problem by full search leads to a tremendous number of possibilities to check and is NP-hard. In this paper we introduce a novel fast binary search for optimal filter selection that guarantees a minimum distance metric between the different spectra to classify. In our experiments, this procedure reaches the same optimal solution as with full search at much lower complexity. The desired number of filters influences the full search in factorial order while the fast binary search stays constant. Thus, fast binary search allows to find the optimal solution of all combinations in an adequate amount of time and avoids prevailing heuristics. Moreover, our fast binary search algorithm outperforms other filter selection techniques in terms of misclassified spectra in a real-world classification problem

    Quantitative analysis of multi-spectral fundus images

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    We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages

    Expanding Dimensionality in Cinema Color: Impacting Observer Metamerism through Multiprimary Display

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    Television and cinema display are both trending towards greater ranges and saturation of reproduced colors made possible by near-monochromatic RGB illumination technologies. Through current broadcast and digital cinema standards work, system designs employing laser light sources, narrow-band LED, quantum dots and others are being actively endorsed in promotion of Wide Color Gamut (WCG). Despite artistic benefits brought to creative content producers, spectrally selective excitations of naturally different human color response functions exacerbate variability of observer experience. An exaggerated variation in color-sensing is explicitly counter to the exhaustive controls and calibrations employed in modern motion picture pipelines. Further, singular standard observer summaries of human color vision such as found in the CIE’s 1931 and 1964 color matching functions and used extensively in motion picture color management are deficient in recognizing expected human vision variability. Many researchers have confirmed the magnitude of observer metamerism in color matching in both uniform colors and imagery but few have shown explicit color management with an aim of minimized difference in observer perception variability. This research shows that not only can observer metamerism influences be quantitatively predicted and confirmed psychophysically but that intentionally engineered multiprimary displays employing more than three primaries can offer increased color gamut with drastically improved consistency of experience. To this end, a seven-channel prototype display has been constructed based on observer metamerism models and color difference indices derived from the latest color vision demographic research. This display has been further proven in forced-choice paired comparison tests to deliver superior color matching to reference stimuli versus both contemporary standard RGB cinema projection and recently ratified standard laser projection across a large population of color-normal observers
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