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This PDF file contains the front matter associated with SPIE Proceedings Volume 6497, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.
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Canny edge detector is based both on local and global image analysis, present in the gradient computation and
connectivity-related hysteresis thresholding, respectively. This contribution proposes a generalization of these ideas.
Instead of the sole gradient magnitude, we consider several local statistics, to take into account how much texture is
present around each pixel. This information is used in biologically inspired surround inhibition of texture. Global
analysis is generalized by introducing a long range connectivity analysis. We demonstrate the effectiveness of our
approach by extensive experimentation.
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Object detection in images was conducted using a nonlinear means of improving signal to noise ratio termed "stochastic
resonance" (SR). In a recent United States patent application, it was shown that arbitrarily large signal to noise ratio
gains could be realized when a signal detection problem is cast within the context of a SR filter. Signal-to-noise ratio
measures were investigated. For a binary object recognition task (friendly versus hostile), the method was implemented
by perturbing the recognition algorithm and subsequently thresholding via a computer simulation. To fairly test the
efficacy of the proposed algorithm, a unique database of images has been constructed by modifying two sample library
objects by adjusting their brightness, contrast and relative size via commercial software to gradually compromise their
saliency to identification. The key to the use of the SR method is to produce a small perturbation in the identification
algorithm and then to threshold the results, thus improving the overall system's ability to discern objects. A background
discussion of the SR method is presented. A standard test is proposed in which object identification algorithms could be
fairly compared against each other with respect to their relative performance.
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Corner matching is an important operation in digital image processing and computer vision where it is used for a range
of applications including stereo vision and image registration. A number of corner similarity metrics have been
developed to facilitate matching, however, any individual metric has a limited effectiveness depending on the content of
images to be registered and the different types of distortions that may be present. This paper explores combining corner
similarity metrics to produce more effective measures for corner matching. In particular the combination of two
similarity metrics is investigated using experiments on a number of images exhibiting different types of transformations
and distortions. The results suggest that a linear combination of different similarity metrics may produce more accurate
and robust assessments of corner similarity.
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This paper proposes a junction detection method that detects junctions as those points where edges join or intersect. The
edges that form a junction are searched in a square neighbourhood, and the subtended angles among them are calculated
by using edge orientations. Local edge orientation at a pixel is estimated by utilizing those edge points close to the pixel.
Based on the subtended angles, the pixel is determined to be a junction candidate or not. Each actual junction is accurately
localized by suppressing the candidates of non-minimum orientation difference. The proposed method analyzes real cases
of extracted edges, and estimates the change of orientations of edge segments in digital fields. The experimental results
show that the proposed algorithm can robustly detect junctions in digital images.
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Conventional approach in single-chip digital cameras is a use of color filter arrays (CFA) in order to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, in order to produce an RGB image. In this paper we propose a novel demosaicing algorithm for the Bayer CFA. It is assumed that the initial estimates of color channels contain two additive components: the true values of color intensities and the errors. The errors are considered as an additive noise, and often called as a demosaicing noise, that has been removed. However, this noise is not white and strongly depends on a signal. Usually, the intensity of this noise is higher near edges of image details. We use spatially designed signal-adaptive filter to remove the noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals (ICI) applied for selection adaptively varying scales (window sizes) of LPA. The LPA-ICI technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The efficiency of the proposed approach is demonstrated by simulation results.
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A fundamental challenge in analyzing spatial patterns in images is the notion of scale. Texture based analysis
typically characterizes spatial patterns only at the pixel level. Such small scale analysis usually fails to capture
spatial patterns that occur over larger scales. This paper presents a novel solution, termed hierarchical texture
motifs, to this texture-of-textures problem. Starting at the pixel level, spatial patterns are characterized using
parametric statistical models and unsupervised learning. Higher levels in the hierarchy use the same analysis to
characterize the motifs learned at the lower levels. This multi-level analysis is shown to outperform single-level
analysis in classifying a standard set of image textures.
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Dust, scratches or hair on originals (prints, slides or negatives) distinctly appear as light or dark artifacts on a
scan. These unsightly artifacts have become a major consumer concern. This paper describes an algorithmic
solution to the dust and scratch removal task. The solution is divided into two phases: a detection phase
and a reconstruction phase. Some scanners have dedicated hardware to detect dust and scratch areas in the
original. Without hardware assistance, dust and scratch removal algorithms generally resort to blurring, at
the loss of image detail. We present an algorithmic alternative for dust and scratch detection that effectively
differentiates between defects and image details. In addition we present reconstruction algorithms, that preserve
image sharpness better than available alternatives. For detection we generate a detail-less image in which the
defects are "erased". We compare properties of the luminance channel of the input image relative to the detailless
image. For reconstruction of the defective areas we suggest both a fast small support algorithm and a large
support algorithm, which is better able to mimic the existing image texture.
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To understand a comprehensive atmospheric state, it is important to classify clouds in satellite images into
appropriate classes. Many researches utilizing various features concerning the cloud texture have been reported
in cloud classification. However, some clouds can not be classified uniquely only with the texture features.
According to the knowledge of the experts, they classify the clouds in two stages. They firstly categorize the
clouds into the provisional classes according to the brightnesses of the satellite images. They then classify each
provisional class into the objective class based on the texture, shape and velocity of the cloud employing the
meteorological knowledge about the time and location of the image. In this paper, we propose a novel method
for the cloud classification that consists of two stages and utilizes cloud movement as human experts adopt. We
firstly classify the clouds into 20 classes based on their brightnesses of the two-band spectral images. We then
closely analyze the classes according to five features such as the brightnesses, deviations of brightness and cloud
velocity estimated by varying window size adaptively. The experimental results are shown to verify the proposed
method.
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Our challenge was to develop a semi-automatic target detection algorithm to aid human operators in
locating potential targets within images. In contrast to currently available methods, our approach is
relatively insensitive to image brightness, image contrast and object orientation. Working on overlapping
image blocks, we used a sliding difference method of histogram matching. Incrementally sliding the
histograms of the known object template and the image region of interest (ROI) together, the sum of
absolute histogram differences was calculated. The minimum of the resultant array was stored in the
corresponding spatial position of response surface matrix. Local minima of the response surface suggest
possible target locations. Because the template contrast will rarely perfectly match the contrast of the actual
image contrast, which can be compromised by illumination conditions, background features, cloud cover,
etc., we perform a random contrast manipulation, which we term 'wobble', on the template histogram. Our
results have shown increased object detection with the combination of the sliding histogram difference and
wobble.
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With the increased emphasis on security and personal authentication, an accurate biometric-based authentication system
has become a critical requirement in a variety of applications. Among different biometrics, authentication based on iris
features has received a lot of attention since its introduction in 1992. The wavelet transform has been proposed by
several researchers for extracting iris features for authentication. Although classical wavelets provide a good
performance, they suffer from limited orientation selectivity. In this paper, we investigate the potentials of using the
contourlet transform to represent the iris texture. A new iris representation and matching system based on contourlet
transform is proposed. The contourlet transform not only shares the multiscale and localization properties of wavelets,
but also has a higher degree of directionality and anisotropy. The proposed matching system is experimented in both
verification and identification modes. Results have shown the significance of the new technique, especially in case of
low quality iris images and highly security demanding applications.
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In this paper,we study optical flow determination with Complex Sinusoidally Modulated Imaging (CSMI) using a novel imaging device, the correlation image sensor (CIS). The proposed method is based on a newly obtained relation what we call the optical flow identity (OFI) between intensity image and complex inusoidally-modulated image captured simultaneously by the CIS. This equation is complex-valued and the optical flow is a 2-dimensional vector for each pixel. Therefore, it is possible to compute the optical flow from one pixel value and its spatial gradient. Since the OFI does not involve time derivative, information on a single frame is sufficient. Moreover, the velocity limitation due to the spatio-temporal aliasing and approximate frame differentials used in conventionalt methods is avoided.
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In the context of site-specific weed management by vision systems, an efficient image processing for a crop/weed
discrimination is required in order to quantify the Weed Infestation Rate (WIR) in an image. This paper presents a
modeling of crop field in presence of different Weed Infestation Rates and a set of simulated agronomic images is used
to test and validate the effectiveness of a crop/weed discrimination algorithm. For instance, an algorithm has been
implemented to firstly detect the crop rows in the field by the use of a Hough Transform and secondly to detect plant
areas by a region based-segmentation on binary images. This image processing has been tested on virtual cereal fields of
a large field of view with perspective effects. The vegetation in the virtual field is modeled by a sowing pattern for crop
plants and the weed spatial distribution is modeled by either a Poisson process or a Neyman-Scott cluster process. For
each simulated image, a comparison between the initial and the detected weed infestation rate allows us to assess the
accuracy of the algorithm. This comparison demonstrates an accuracy of better than 80% is possible, despite that intrarow
weeds can not be detected from this spatial method.
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Robust image-based motion stabilization is developed to enable visual surveillance in the maritime domain. The
algorithm developed is neither a dense registration method nor a traditional feature-based method, but rather it captures
the best aspects of each of these approaches. It avoids feature tracking and so can handle large intra-frame motions, and
at the same time it is robust to large lighting variations and moving clutter. It is thus well-suited for challenges in the
maritime domain. Advantage is taken of the maritime environment including use of the horizon and shoreline, and fused
data from an inexpensive inertial measurement unit. Results of real-time operation on an in-water buoy are presented.
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Image enhancement is the task of applying certain alterations to an input image such as to obtain a more visually
pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output
resulting image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis
process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a
challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been
employed for enhancing images that have overall improper illumination or are over/under exposed. However, these
techniques perform poorly for images that contain various regions of improper illumination or improper exposure.
In this paper, we introduce new human vision model based automatic image enhancement techniques, multi-histogram
equalization as well as local and adaptive algorithms. These enhancement algorithms address the previously mentioned
shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization
methods. Computer simulations are presented showing that the proposed algorithms outperform the other algorithms in
two important areas. First, they have better performance, both in terms of subjective and objective evaluations, then
that currently used algorithms on a series of poorly illuminated images as well as images with uniform and non-uniform
illumination, and images with improper exposure. Second, they better adapt to local features in an image, in
comparison to histogram equalization methods which treat the images globally.
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Iris pattern matching is a key step in iris patter recognition system. This paper proposes a new
approach for iris pattern matching using block-based spectral angle measure (SAM). The iris
patterns in this paper are extracted using Log-Gabor wavelet method. The proposed SAM method
will capture not only the phase information, but also the magnitude information. The block based
method will enable the flexibility in the pattern matching step, which improves the accuracy and is
more tolerable of the pattern dilation/constrain resulted from imperfect preprocessing. The
preliminary experimental results show that the proposed method has an encouraging performance.
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Video image processing based vehicle tracking and traffic monitoring provides
several advantages over traditional approaches. One of the challenges lies in the
robust segmentation and region tracking. The traditional approaches have assumed 1)
the region of the object of interest is uniform and homogeneous; 2) adjacent regions
should differ significantly. These are often not true for a vehicle object. In this paper,
we propose a dynamic content based image segmentation method for vehicle tracking
and traffic monitoring. Only initial lane information is needed for camera calibration.
The system will automatically detect the direction of the traffic flow for vehicle
detection and traffic monitoring. The preliminary experimental results show that this
method is effective.
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What visually distinguishes a painting from a photograph is often the absence of texture and the sharp edges: in many
paintings, edges are sharper than in photographic images while textured areas contain less detail. Such artistic effects can
be achieved by filters that smooth textured areas while preserving, or enhancing, edges and corners. However, not all
edge preserving smoothers are suitable for artistic imaging. This study presents a generalization of the well know
Kuwahara filter aimed at obtaining an artistic effect. Theoretical limitations of the Kuwahara filter are discussed and
solved by the new nonlinear operator proposed here. Experimental results show that the proposed operator produces
painting-like output images and is robust to corruption of the input image such as blurring. Comparison with existing
techniques shows situations where traditional edge preserving smoothers that are commonly used for artistic imaging fail
while our approach produces good results.
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In this paper we propose a novel color demosaicing algorithm for noisy data. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise which is common for CCD and CMOS digital image sensors. Demosaicing algorithms are used to reconstruct missed red, green, and blue values to produce an RGB image. This is an interpolation problem usually called color filter array interpolation (CFAI). The conventional approach used in image restoration chains for the noisy raw sensor data exploits denoising and CFAI as two independent steps. The denoising step comes first and the CFAI is usually designed to perform on noiseless data. In this paper we propose to integrate the denoising and CFAI into one procedure. Firstly, we compute initial directional interpolated estimates of noisy color intensities. Afterward, these estimates are decorrelated and denoised by the special directional anisotropic adaptive filters. This approach is found to be efficient in order to attenuate both noise and interpolation errors. The exploited denoising technique is based on the local polynomial approximation (LPA). The adaptivity to data is provided by the multiple hypothesis testing called the intersection of confidence intervals (ICI) rule which is applied for adaptive selection of varying scales (window sizes) of LPA. We show the efficiency of the proposed approach in terms of both numerical and visual evaluation.
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A practical impossibility of prediction of signs of DCT coefficients is generally accepted. Therefore each coded sign of
DCT coefficients occupies usually 1 bit of memory in compressed data. At the same time data of all coded signs of DCT
coefficients occupy about 20-25% of a compressed image. In this work we propose an effective approach to predict signs
of DCT coefficients in block based image compression. For that, values of pixels of already coded/decoded neighbor
blocks of the image are used. The approach consist two stages. At first, values of pixels of a row and a column which
both are the nearest to already coded neighbor blocks are predicted by a context-based adaptive predictor. At second
stage, these row and column are used for prediction of the signs of the DCT coefficients. Depending on complexity of an
image proposed method allows to compress signs of DCT coefficients to 60-85% from their original size. It corresponds
to increase of compression ratio of the entire image by 3-9% (or up to 0.5 dB improvement in PSNR).
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The dynamic range of an image is defined as the ratio between the maximum and minimum luminance value
it contains. This value in real images can be several thousands or even millions, whereas the dynamic range
of consumer imaging devices rarely exceeds 100; therefore some processing is needed in order to display a high
dynamic range image correctly. Global operators map each pixel individually with the same nonlinear function;
local operators use spatially-variant functions in order to achieve a higher quality. The lower computational cost
of global operators makes them attractive for real-time processing; the nonlinear mapping can however attenuate
the image details. In this paper we define an expression which gives a quantitative measure of this artifact, and
compare the performance of some commonly used operators. We show that a modified logarithm we propose has
a satisfactory performance for a wide class of images, and has a theoretical justification based on some properties
of the human visual system. We also introduce a method for the automatic tuning of the parameters of our
system, based on the statistics of the input image. We finally compare our method with others proposed in the
literature.
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Classification of image segments on textures can be helpful for target recognition. Sometimes target cueing is
performed before target recognition. Textures are sometimes used to cue an image processor of a potential region of
interest. In certain imaging sensors, such as those used in synthetic aperture radar, textures may be abundant. The
textures may be caused by the object material or speckle noise. Even speckle noise can create the illusion of texture,
which must be compensated in image pre-processing. In this paper, we will discuss how to perform texture
classification but constrain the number of wavelet packet node decomposition. The new approach performs a twochannel
wavelet decomposition. Comparing the strength of each new subband with others at the same level of the
wavelet packet determines when to stop further decomposition. This type of decomposition is performed recursively.
Once the decompositions stop, the structure of the packet is stored in a data structure. Using the information from the
data structure, dominating channels are extracted. These are defined as paths from the root of the packet to the leaf with
the highest strengths. The list of dominating channels are used to train a learning vector quantization neural network.
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Classification, Recognition, and Feature Extraction
With the development of Synthetic Aperture Radar (SAR) technology, automatic target recognition (ATR) is becoming
increasingly important. In this paper, we proposed a 3-class target classification system in SAR images. The system is
based on invariant wavelet moments and support vector machine (SVM) algorithm. It is a two-stage approach. The first
stage is to extract and select a small set of wavelet invariant moment features to indicate target images. The wavelet
invariant moments take both advantages of the wavelet inherent property of multi-resolution analysis and moment
invariants quality of invariant to translation, scaling changes and rotation. The second stage is classification of targets
with SVM algorithm. SVM is based on the principle of structural risk minimization (SRM), which has been shown
better than the principle of empirical risk minimization (ERM) which is used by many conventional networks. To test
the performance and efficiency of the proposed method, we performed experiments on invariant wavelet moments,
different kernel functions, 2-class identification, and 3-class identification. Test results show that wavelet invariant
moments indicate the target effectively; linear kernel function achieves better results than other kernel functions, and
SVM classification approach performs better than conventional nearest distance approach.
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In this paper, a novel color space transform is presented. It is an adaptive transform based on the application of
independent component analysis to the RGB data of an entire color image. The result is a linear and reversible
color space transform that provides three new coordinate axes where the projected data is as much as statistically
independent as possible, and therefore highly uncorrelated. Compared to many non-linear color space transforms
such as the HSV or CIE-Lab, the proposed one has the advantage of being a linear transform from the RGB
color space, much like the XYZ or YIQ. However, its adaptiveness has the drawback of needing an estimate of
the transform matrix for each image, which is sometimes computationally expensive for larger images due to the
common iterative nature of the independent component analysis implementations. Then, an image subsampling
method is also proposed to enhance the novel color space transform speed, efficiency and robustness. The new
color space is used for a large set of test color images, and it is compared to traditional color space transforms,
where we can clearly visualize its vast potential as a promising tool for segmentation purposes for example.
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This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained
with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space
relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring
operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded
image as input and the non-degraded image as output for the supervised learning process. The neural network thus
performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference
of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing
relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an
optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation.
In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the
same steps use for the artificial circle image.
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Template matching in real-time is a fundamental issue in many applications in computer vision such as tracking,
stereo vision and autonomous navigation. The goal of this paper is present a system for automatic landmarks
recognition in video frames over a georeferenced high resolution satellite image, for autonomous aerial navigation
research. The video frames employed were obtained from a camera fixed to a helicopter in a low level flight,
simulating the vision system of an unmanned aerial vehicle (UAV). The landmarks descriptors used in recognition
task were texture features extracted by a Gabor Wavelet filters bank. The recognition system consists on a
supervised neural network trained to recognize the satellite image landmarks texture features. In activation
phase, each video frame has its texture feature extracted and the neural network has to classify it as a predefined
landmark. The video frames are also preprocessed to reduce their difference of scale and rotation from the satellite
image before the texture feature extraction, so the UAV altitude and heading for each frame are considered as
known. The neural network techniques present the advantage of low computational cost, been appropriate to
real-time applications. Promising results were obtained, mainly during flight over urban areas.
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Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to
assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately.
Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the
performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands,
is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not
considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological
network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components,
corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian
rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features,
large amount of samples can be used in learning efficiently.
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Evolutionary Programming, Filtering, and Enhancement
As reported in our publications in SPIE conferences in the last two years, we can use a simple VB6 program to break the boundaries of some selected objects in an edge-detected binary picture into many simple branches and reconstruct accurately the boundaries of the original objects free of noise. By this means we can then program the computer to automatically learn some standard objects and automatically recognize any test objects by a novel topological connections of the simple, bi-directional graph of the object boundaries. It is very accurate, yet very robust, way to recognize the test objects because it is like the design of an electric circuit. When the way of connection of an electric circuit, or the topology of an electric circuit, is fixed, all the electrical properties of the circuit are fixed.
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Dot diffusion is a halftoning technique that is based on the traditional error diffusion concept, but offers a high degree of
parallel processing by its block based approach. Traditional dot diffusion however suffers from periodicity artifacts. To
limit the visibility of these artifacts, we propose grid diffusion, which applies different class matrices for different blocks.
Furthermore, in this paper we will discuss two approaches in the dot diffusion framework to generate green-noise halftone
patterns. The first approach is based on output dependent feedback (hysteresis), analogous to the standard green-noise
error diffusion techniques. We observe that the resulting halftones are rather coarse and highly dependent on the used dot
diffusion class matrices. In the second approach we don't limit the diffusion to the nearest neighbors. This leads to less
coarse halftones, compared to the first approach. The drawback is that it can only cope with rather limited cluster sizes.
We can reduce these drawbacks by combining the two approaches.
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This paper presents parameter estimation techniques useful for detecting background changes in a video sequence with
extreme foreground activity. A specific application of interest is automated detection of the covert placement of threats
(e.g., a briefcase bomb) inside crowded public facilities. We propose that a histogram of pixel intensity acquired from a
fixed mounted camera over time for a series of images will be a mixture of two Gaussian functions: the foreground
probability distribution function and background probability distribution function. We will use Pearson's Method of
Moments to separate the two probability distribution functions. The background function can then be "remembered" and
changes in the background can be detected. Subsequent comparisons of background estimates are used to detect
changes. Changes are flagged to alert security forces to the presence and location of potential threats. Results are
presented that indicate the significant potential for robust parameter estimation techniques as applied to video
surveillance.
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This study proposes a new approach to perform motion estimation on a set of feature points via elastic graph matching.
The approach starts by constructing a labeled graph on a set of feature points in the first image of a given sequence, and
then continues to sequentially match the graph with the remaining images in this sequence. The matching is based on a
similarity function that depends on image brightness and motion characteristics on one side, and on geometric distortion
on the other side. The main advantage of the proposed approach is that it preserves high-level image characteristics
outlined by the geometrical structure of moving objects and their relative positions in space and time, while it
simultaneously accounts for both low-level measurements (motion and intensity).
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The resizing of data, either upscaling or downscaling based on need for increased or decreased resolution, is an
important signal processing technique due to the variety of data sources and formats used in today's world. Image
interpolation, the 2D variation, is commonly achieved through one of three techniques: nearest neighbor, bilinear
interpolation, or bicubic interpolation. Each method comes with advantages and disadvantages and selection of the
appropriate one is dependent on output and situation specifications. Presented in this paper are algorithms for the
resizing of images based on the analysis of the sum of primary implicants representation of image data, as generated by
a logical transform. The most basic algorithm emulates the nearest neighbor technique, while subsequent variations
build on this to provide more accuracy and output comparable to the other traditional methods. Computer simulations
demonstrate the effectiveness of these algorithms on binary and grayscale images.
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This paper presents a fuzzy system approach using texture and color to classify living coral cover in underwater color
images acquired by an autonomous underwater vehicle (AUV). The proposed fuzzy system for classification consists in
the assigning of fuzzy memberships to different image features such as the mean, the spatial variance, the Gabor filter
response standard deviation, and the wavelet energy. These fuzzy sensors are applied to the different segments present
in the images. The segmentation of the images is previously done using the Homogeneity Coefficient Segmentation
Algorithm (LHC). The resulted classification of the regions is compared against ground truth maps of the images. A
correct classification over 80% was achieved in two different 25 images sets of two different areas.
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In different applications, it is often desirable to retrieve useful information from multichannel (color, multispectral, dual
or full-polarization) images. On one hand, multichannel images are potentially able to provide a lot of useful information
about sensed objects (terrains). On the other hand, the task of its reliable extraction is very complicated. And there are
many reasons behind this like inherent noise, lack of a priori information about object features, complexity of scenes,
etc. Therefore, numerous different approaches based on various functional principles and mathematical background have
been already put forward. In majority of them, image classification and segmentation are common operations that precede
estimation of object parameters. However, practically all methods are far away from completeness and/or perfection
since they suffer from different drawbacks and application restrictions. Recently we have proposed methods based
on learning with local parameter clustering that were rather successfully applied to image locally adaptive filtering and
detection of objects with certain properties. This paper is an attempt to extend this approach to image classification,
segmentation and object parameter estimation. A particular application of substance quantitative analysis from color
images is considered. The proposed approach is shown to solve the aforementioned task quite well and to have a rather
high potential for other applications.
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Time varying motion blur is sometimes brought by camera shake,
and alternate blurred frames and sharp frames are included in video clips.
This degradation is considered a kind of breathing distortion, which means oscillatory changes of blur.
We propose a motion deblurring method which aims to suppress breathing distortions.
A spatio-temporal regularization is introduced into the deconvolution approach to smooth the temporal change of blur.
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In this paper we present the capability of the Rank M-Type Radial Basis Function (RMRBF) Neural Network in medical
image processing applications. The proposed neural network uses the proposed RM-estimators in the scheme of radial
basis function to train the neural network. The RMRBF-based training is less biased by the presence of outliers in the
training set and was proved an accurate estimation of the implied probabilities. Other RBF based algorithms were
compared with our approach in pdf estimation on the microcalcification detection in mammographic image analysis.
From simulation results we observe that the RMRBF gives better estimation of the implied pdfs and has show better
classification capabilities.
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Vegetables are widely planted all over China, but they often suffer from the some diseases. A method of major technical
and economical importance is introduced in this paper, which explores the feasibility of implementing fast and reliable
automatic identification of vegetable diseases and their infection grades from color and morphological features of leaves.
Firstly, leaves are plucked from clustered plant and pictures of the leaves are taken with a CCD digital color camera.
Secondly, color and morphological characteristics are obtained by standard image processing techniques, for examples,
Otsu thresholding method segments the region of interest, image opening following closing algorithm removes noise,
Principal Components Analysis reduces the dimension of the original features. Then, a recently proposed boosting
algorithm AdaBoost. M2 is applied to RBF networks for diseases classification based on the above features, where the
kernel function of RBF networks is Gaussian form with argument taking Euclidean distance of the input vector from a
center. Our experiment performs on the database collected by Chinese Academy of Agricultural Sciences, and result
shows that Boosting RBF Networks classifies the 230 cucumber leaves into 2 different diseases (downy-mildew and
angular-leaf-spot), and identifies the infection grades of each disease according to the infection degrees.
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