Nano-coatings with adjustable optical features is one of the revolutionary technologies of today. In this work, we investigate how hyperspectral imaging can detect adjustable nano-surfaces used, for example, for activ...
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ISBN:
(纸本)0819457507
Nano-coatings with adjustable optical features is one of the revolutionary technologies of today. In this work, we investigate how hyperspectral imaging can detect adjustable nano-surfaces used, for example, for active camouflage. The distinct attributes of the nano-coating spectra are discussed. Fast algorithms of utilizing hyperspectral information for recognizing these attributes are suggested. The research applies to both recognizing the camouflaged objects and to building unrecognizable camouflage technology. In the context of tracking active camouflage, the identification of characteristic spectral attributes is especially important. Active spectra can constantly change, therefore confusing traditional hyperspectral classification. In contrast, the identified general spectral attributes stay the same allowing for robust identification and reliable tracking of the camouflaged objects.
The proceedings contain 59 papers from the Conference of SPIE - Image processing: algorithms and systemsiii. The topics discussed include: the fast parametric slantlet transform with applications;parameterized revers...
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The proceedings contain 59 papers from the Conference of SPIE - Image processing: algorithms and systemsiii. The topics discussed include: the fast parametric slantlet transform with applications;parameterized reversible integer discrete cosine transform;variations of matched filtering for reduced noise amplification;pupil detection in photo ID;template gradient matching in spherical images;and imaging-based dust sensors;equipment and methods.
Focus of attention is often attributed to biological vision system where the entire field of view is first monitored and then the attention is,focused to the object of interest. We propose using a similar approach for...
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ISBN:
(纸本)0819452017
Focus of attention is often attributed to biological vision system where the entire field of view is first monitored and then the attention is,focused to the object of interest. We propose using a similar approach for object recognition in a color image sequence. The intention is to locate an object based on a prior motive, concentrate on the detected object so that the imaging device can be guided toward it. We use the abilities of the intelligent image analysis framework developed in our laboratory to generate an algorithm dynamically to detect the particular type of object based on the user's object description. The proposed method uses color clustering along with segmentation. The segmented image with labeled regions is used to calculate the shape descriptor parameters. These and the color information are matched with the input description. Gaze is then controlled by, issuing camera movement commands as appropriate. We present some preliminary results that demonstrate the success of this approach.
We present a generalized approach to dynamically incorporate high level knowledge into a cooperative intelligent image analysis framework. We developed this framework in our laboratory to provide a uniform interface t...
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ISBN:
(纸本)0819452017
We present a generalized approach to dynamically incorporate high level knowledge into a cooperative intelligent image analysis framework. We developed this framework in our laboratory to provide a uniform interface to develop intelligent image analysis tools as well as to provide infrastructure facilities required by these tools in order to work cooperatively for accomplishing complex image analysis task goals. This framework is able to automatically generate processing plans which accomplish user defined image analysis task goals. The approach that we propose in this paper provides a flexible interface to incorporate high level knowledge in image processing tools. We provide two ways to develop this knowledge: 1) by taking a feedback from an image processing expert about a processing plan generated by the system;and 2) by accepting a processing plan which accomplishes a particular task from an expert user. and then extracting the high level knowledge encapsulated in this plan. The generalized nature of our approach allows each individual tool to use machine learning algorithms of its own interest in improving the knowledge-base. Preliminary results that we obtained from this work demonstrates the success of our approach.
There are numerous applications for image fusion, some of which include medical imaging, remote sensing, nighttime operations and multi-spectral imaging. In general, the discrete wavelet transform (DWT) and various py...
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ISBN:
(纸本)0819452017
There are numerous applications for image fusion, some of which include medical imaging, remote sensing, nighttime operations and multi-spectral imaging. In general, the discrete wavelet transform (DWT) and various pyramids (such as Laplacian, ratio. contrast, gradient and morphological pyramids) are the most common and effective methods. For quantitative evaluation of the quality of fused imagery, the root mean square error (RMSE) is the most suitable measure of quality if there is a "ground truth" image available;otherwise, the entropy, spatial frequency or image quality index of the input images and the fused images can be calculated and compared. Here, after analyzing the pyramids' performance with the four measures mentioned, an advanced wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is presented. Specifically, at each scale of the wavelet transformed images, a principle vector was derived from two input images and then applied to two of the images' approximation coefficients (i.e., they were fused by using the principal eigenvector). For the detail coefficients (i.e., three quarters of the coefficients), the larger absolute values were chosen and subjected to a neighborhood morphological processing procedure which served to verify the selected pixels by using a "filling" and "cleaning" operation (this operation filled or removed isolated pixels in a 3-by-3 local region). The fusion performance of the advanced DWT (aDWT) method proposed here was compared with six other common methods, and. based on the four quantitative measures, was found to perform the best when tested on the four input image types. Since the different image sources used here varied with respect to intensity, contrast, noise, and intrinsic characteristics, the aDWT is a promising image fusion procedure for inhomogeneous imagery.
Non-linear techniques for denoising images and video are known to be superior to linear ones. In addition video denoising using spatio-temporal information is considered to be more efficient compared with the use of j...
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ISBN:
(纸本)0819452017
Non-linear techniques for denoising images and video are known to be superior to linear ones. In addition video denoising using spatio-temporal information is considered to be more efficient compared with the use of just temporal information in the presence of fast motion and low noise. Earlier, we introduced a 3-D extension of the K-nearest neighbor filter and have investigated its properties. In this paper we propose a new, motion- and detail-adaptive filter, which solves some of the potential drawbacks of the non-adaptive version: motion caused artifacts and the loss of fine details and texture. We also introduce a novel noise level estimation technique for automatic tuning of the noise-level dependent parameters. The results show that the adaptive K-nearest neighbor filter outperforms the none-adaptive one, as well as some other state-of-the-art spatio-temporal filters such as the 3D a-trimmed mean and the state-of-the-art rational filter by Ramponi from both a PSNR and visual quality point of view.
Computational mathematical morphology (CMM) is a nonlinear filter representation particularly amenable to real-time image processing. In the state of the art implementation each pixel value in a windowed observation i...
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ISBN:
(纸本)0819452017
Computational mathematical morphology (CMM) is a nonlinear filter representation particularly amenable to real-time image processing. In the state of the art implementation each pixel value in a windowed observation is indexed into a separate lookup table to retrieve a set of bit vectors. Each bit in the vector corresponds to a basis element in the CMM filter representation. All retrieved bit vectors are "anded" together to produce a bit vector with a unique nonzero bit. The position of that bit corresponds to a basis element containing the observation and it used to look up a filter value in a table. The number of stored bit vectors is a linear function of the image or signal bit depth. We present an architecture for CMM implementation that uses a minimal number of bit vectors and required memory is less sensitive to bit depth. In the proposed architecture, basis elements are projected to subspaces and only bit vectors unique to each subspace are stored. With the addition of an intermediate lookup table to map observations to unique bit vectors, filter memory. is greatly reduced. Simulations show that the architecture provides an advantage for random tessellations of the observation space. A 50% memory savings is shown for a practical application to digital darkness control in electronic printing.
In the framework of computer-aided diagnosis, pulmonary airway investigation based on multi-detector computerized tomography (MDCT) requires the development of specific tools for data interaction and analysis. The 3D ...
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ISBN:
(纸本)0819452017
In the framework of computer-aided diagnosis, pulmonary airway investigation based on multi-detector computerized tomography (MDCT) requires the development of specific tools for data interaction and analysis. The 3D segmentation of the bronchial tree provides radiologists with appropriate examination modalities such as CT bronchography, for a global analysis, or virtual endoscopy, for a local endoluminal diagnosis. Focusing on the latter modality, this paper proposes a set of advanced navigation and investigation tools based on the automatic extraction of the central axis (CA) of the 3D segmented airways. In the case of complex branching structures, Such as the bronchial tree, the automatic CA computation is a challenging problem raising several difficulties related to geometry and topology preservation. In this respect, an original approach is presented, combining 3D distance map information and geodesic front propagation in order to accurately detect branching points and to preserve the original 3D topology of the airways, irrespective to both caliber variability with the bronchial order and to bronchial wall irregularities. The CA information is represented as a multi-valued and hierarchic tree structure, making possible automatic trajectory Computation between two given points, bronchial caliber estimation in the plane orthogonal to the bronchus axis at a given location, branch indexation, and so on. These applications are Illustrated on clinical data including both normal and pathological airway morphologies.
In this contribution, we explore the best basis paradigm for in feature extraction. According to this paradigm, a library of bases is built and the best basis is found for a given signal class with respect to some cos...
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In this contribution, we explore the best basis paradigm for in feature extraction. According to this paradigm, a library of bases is built and the best basis is found for a given signal class with respect to some cost measure. We aim at constructing a library of anisotropic bases that are suitable for the class of 2-D binarized character images. We consider two, a dyadic and a non-dyadic generalization scheme of the Haar wavelet packets that lead to anisotropic bases. For the non-dyadic case, generalized Fibonacci p-trees are used to derive the space division structure of the transform. Both schemes allow for an efficient O(N log N) best basis search algorithm. The so built extended library of anisotropic Haar bases is used in the problem of optical character recognition. A special case, namely recognition of characters from very low resolution, noisy TV images is investigated. The best Haar basis found is then used in the feature extraction stage of a standard OCR system. We achieve very promising recognition rates for experimental databases of synthetic and real images separated into 59 classes.
We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter....
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ISBN:
(纸本)0819452017
We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights. Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns. This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark spots. The adaptation rules are insensitive for parameter deviations., mismatch and non-ideal approximation of the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant differences in the synchronization properties between networks using the ideal adaptation rules and networks resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It comprises a 128 x 128 neuron array, analog image memory, and an address event representation pulse output.
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