In this paper, we present a novel approach on image object removal by extending subpatch texture synthesis technique into redundant wavelet transform (RDWT) domain. As an overcompleted wavelet transform, RDWT is shift...
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ISBN:
(纸本)9780819474957
In this paper, we present a novel approach on image object removal by extending subpatch texture synthesis technique into redundant wavelet transform (RDWT) domain. As an overcompleted wavelet transform, RDWT is shift invariant and obtained without downsampling. Also, each RDWT highpass subband exhibits one specific orientation features of the image, in horizontal, vertical, or diagonal. All these make RDWT ideal for performing texture synthesis object removal techniques. In our experiments, subpatch texture synthesis in RDWT is introduced to remove unwanted objects from digital photographs. Specifically, for each RDWT subband, depending on the subband orientation, a particular direction subpatch texture synthesis is applied independently. Experimental results reveal that our simple algorithm performs better than previous methods.
Based on our research in the last 17 years (with 68 papers published) on the subject of artificial neural network studied from the point of view of N-dimension geometry, a novel neural network system, the dynamic neur...
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ISBN:
(纸本)9780819474957
Based on our research in the last 17 years (with 68 papers published) on the subject of artificial neural network studied from the point of view of N-dimension geometry, a novel neural network system, the dynamic neural network, is proposed here for detecting an unknown moving (or time-varying) object such that the object will not only be detected by its static images, but also by the way it moves if this object follows a constant moving pattern. The system is designed to identify the unknown object by comparing a few time-separated snapshots of the object to a few standard moving objects learned or memorized in the system. The identification is determined by a user entered accuracy control. It could be very accurate, yet still be quite robust and quite fast in identification (e. g., identification in real-time) because of the simplicity of the algorithm. It is different from most other neural network systems because it employs the ND geometrical concept.
In this paper, we introduce the Vector Median M-type L (VMML) -filter to remove impulsive and Gaussian noise from color images and video color sequences. This filter utilizes vector approach and the Median M-type (MM)...
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ISBN:
(纸本)9780819474957
In this paper, we introduce the Vector Median M-type L (VMML) -filter to remove impulsive and Gaussian noise from color images and video color sequences. This filter utilizes vector approach and the Median M-type (MM) estimator with different influence functions in the filtering scheme of L-filter. We also introduce the use of impulsive noise detectors to improve the properties of noise suppression and detail preservation in the proposed filtering scheme in the case of low and high densities of impulsive noise. To demonstrate the performance of the proposed filtering scheme in real applications, we applied it for filtering of SAR images, which naturally have speckle noise. Simulation results indicate that the proposed filter consistently outperforms other color image filters by balancing the tradeoff between noise suppression, detail preservation, and color retention.
In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markov random field (MRF) was proposed for image restoration to reduce or remove the noise resulted from imperfect sensing. Ima...
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ISBN:
(纸本)9780819474957
In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markov random field (MRF) was proposed for image restoration to reduce or remove the noise resulted from imperfect sensing. image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this "double compound stochastic" image process is based on Bayes' theorem, and the MAP estimation employs the Point-Jacobian iteration to obtain the optimal solution. In the proposed algorithm, MRF is used to quantify the spatial interaction probabilistically, that is, to provide a type of prior information on the image texture and the neighbor window of any size is defined for contextual information on a local region. However, the window of a certain size would result in using wrong information for the estimation from adjacent regions with different characteristics at the pixels close to or on the boundary. To overcome this problem, the new method is designed to use less information from more distant neighbors as the pixel is closer to the boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The proximity to the boundary is estimated using a non-uniformity measurement based on edge value, standard deviation, entropy, and the 4th moment of intensity distribution. This study evaluated the new scheme using simulation data, and the experimental results show a considerable improvement in image restoration.
Nowadays, a strong need exists for the efficient organization of an increasing amount of home video content. To create an efficient system for the management of home video content, it is required to categorize home vi...
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ISBN:
(纸本)9780819474957
Nowadays, a strong need exists for the efficient organization of an increasing amount of home video content. To create an efficient system for the management of home video content, it is required to categorize home video content in a semantic way. So far, a significant amount of research has already been dedicated to semantic video categorization. However, conventional categorization approaches often rely on unnecessary concepts and complicated algorithms that are not suited in the context of home video categorization. To overcome the aforementioned problem, this paper proposes a novel home video categorization method that adopts semantic home photo categorization. To use home photo categorization in the context of home video, we segment video content into shots and extract key frames that represent each shot. To extract the semantics from key frames, we divide each key frame into ten local regions and extract low-level features. Based on the low level features extracted for each local region, we can predict the semantics of a particular key frame. To verify the usefulness of the proposed home video categorization method, experiments were performed with home video sequences, labeled by concepts part of the MPEG-7 VCE2 dataset. To verify the usefulness of the proposed home video categorization method, experiments were performed with 70 home video sequences. For the home video sequences used, the proposed system produced a recall of 77% and an accuracy of 78%.
Lattice associative memories also known as morphological associative memories are fully connected feedforward neural networks with no hidden layers, whose computation at each node is carried out with lattice algebra o...
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ISBN:
(纸本)9780819474957
Lattice associative memories also known as morphological associative memories are fully connected feedforward neural networks with no hidden layers, whose computation at each node is carried out with lattice algebra operations. These networks are a relatively recent development in the field of associative memories that has proven to be an alternative way to work with sets of pattern pairs for which the storage and retrieval stages use minimax algebra. Different associative memory models have been proposed to cope with the problem of pattern recall under input degradations, such as occlusions or random noise, where input patterns can be composed of binary or real valued entries. In comparison to these and other artificial neural network memories, lattice algebra based memories display better performance for storage and recall capability;however, the computational techniques devised to achieve that purpose require additional processing or provide partial success when inputs are presented with undetermined noise levels. Robust retrieval capability of an associative memory model is usually expressed by a high percentage of perfect recalls from non-perfect input. The procedure described here uses noise masking defined by simple lattice operations together with appropriate metrics, such as the normalized mean squared error or signal to noise ratio, to boost the recall performance of either the min or max lattice auto-associative memories. Using a single lattice associative memory, illustrative examples are given that demonstrate the enhanced retrieval of correct gray-scale image associations from inputs corrupted with random noise.
Manifolds are mathematical spaces whose points have Euclidean neighborhoods, but whose global structure could be more complex. A one dimensional manifold has a neighborhood that resembles a line. A two dimensional one...
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ISBN:
(纸本)9780819474957
Manifolds are mathematical spaces whose points have Euclidean neighborhoods, but whose global structure could be more complex. A one dimensional manifold has a neighborhood that resembles a line. A two dimensional one resembles a plane. If we consider a one dimensional example, most system neighborhoods cannot be represented optimally by a straight line. A multi-ordered nonlinear line would be better suited to represent most data. A learning algorithm to model the pipeline, based on Fischer Linear Discriminant (FLD), using least squares estimation is presented in this paper. Face patterns are known to show continuous variability. Yet face images of one individual tend to cluster together and can be considered as a neighborhood. Such similar patterns form a pipeline in state space that can be used for pattern classification. Multiple patterns can be trained by having separate lines for each pattern. Face points are now projected onto a low-dimensional mean nonlinear pipe-line, thus providing an easy intuitive way to place new points. Given a test point/face, the classification problem is now simplified to checking the nearest neighbors. This can be done by finding the minimum distance pipe-line from the test-point. The proposed representation of a face image results in improved accuracy when compared to the classical point representation.
It has long been known that the human visual system (HVS) has a nonlinear response to luminance. This nonlinearity can be quantified using the concept of just noticeable difference (JND), which represents the minimum ...
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ISBN:
(纸本)9780819474957
It has long been known that the human visual system (HVS) has a nonlinear response to luminance. This nonlinearity can be quantified using the concept of just noticeable difference (JND), which represents the minimum amplitude of a specified test pattern an average observer can discern from a uniform background. The JND depends on the background luminance following a threshold versus intensity (TVI) function. It is possible to define a curve which maps physical luminances into a perceptually linearized domain. This mapping can be used to optimize a digital encoding, by minimizing the visibility of quantization noise. It is also commonly used in medical applications to display images adapting to the characteristics of the display device. High dynamic range (HDR) displays, which are beginning to appear on the market, can display luminance levels outside the range in which most standard mapping curves are defined. In particular, dual-layer LCD displays are able to extend the gamut of luminance offered by conventional liquid crystals towards the black region;in such areas suitable and HVS-compliant luminance transformations need to be determined. In this paper we propose a method, which is primarily targeted to the extension of the DICOM curve used in medical imaging, but also has a more general application. The method can be modified in order to compensate for the ambient light, which can be significantly greater than the black level of an HDR display and consequently reduce the visibility of the details in dark areas.
Bed load transport is a longstanding problem despite its major implication in river morphodynamics. The physical processes ruling coarse-particle/fluid systems are indeed poorly known, impairing our ability to compute...
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ISBN:
(纸本)9780415453639
Bed load transport is a longstanding problem despite its major implication in river morphodynamics. The physical processes ruling coarse-particle/fluid systems are indeed poorly known, impairing our ability to compute local and even bulk quantities such as the sediment flux in rivers. We present an experimental study of a two-size mixture of coarse spherical glass beads entrained by a shallow turbulent water flow down a steep channel with a mobile bed. The particle diameters were 4 and 6 mm, the channel width 6.5 mm and the channel inclination 12.5%. The water flow rate and the solid discharge were kept constant at the upstream entrance. They were adjusted to obtain bed load equilibrium, that is, neither bed degradation nor aggradation over sufficiently long time intervals. Flows were filmed from the side by a high-speed camera. Using imageprocessingalgorithms made it possible to determine the position, velocity and trajectory of each spherical particle thanks to a PTV algorithm (particle tracking velocimetry). Transitions of the state of motion (rest, rolling or saltating) and flow depth were also determined. New data were compared to previous results obtained with spherical particles of uniform size. They confirm that the free surface acting as a physical barrier by truncating the saltation trajectories is very important on steep slopes. The use of a two-size mixture with the 4 turn beads tending to be blocked in the 6.5 mm wide channel resulted in a bed mainly formed by these 4 mm beads. This particular structure explained the single peak vertical distribution of the solid discharge contrary to the uniform case where several peaks corresponding to rolling were observed.
It is expected that the next generation of full 3D optical scanning systems will be able to measure volumetric objects in motion. Standard data representations like point clouds or sets of triangle meshes, which are u...
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ISBN:
(纸本)0819460966
It is expected that the next generation of full 3D optical scanning systems will be able to measure volumetric objects in motion. Standard data representations like point clouds or sets of triangle meshes, which are used nowadays for static 3D objects, will no longer be an efficient solution in this field. systems of this kind will have to use other data processing and representation methods. We propose our own solution in this paper, using an arbitrary full 3D mesh which is scaled and wrapped around a merged point cloud obtained from the measurements, instead of a standard point cloud representation. This solution was specifically prepared for a prototype of a fall-field 4D scanning system. This system is based on a dynamic laser triangulation. Four scanners capture a surface of a moving object from four different directions simultaneously. They are calibrated in time and space so finally we can obtain a full 3D object surface which changes in time. In this paper we present some details of the scanning system, 4D surface representation, general 4D data processing pipeline, developed algorithms and we finally show some exemplary results of our work in this field.
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