Abstract: Image interpolation is a common problem in imaging applications. However, most interpolation algorithms in existence suffer visually to some extent the effects of blurred edges and jagged artifacts in the image. This paper presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to enhance edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts (''jaggies'') along the tangent directions. In order to preserve image features such as edges, angles and textures, the nonlinear diffusion coefficients are locally adjusted according to the first and second order directional derivatives of the image. Experimental results on synthetic images and nature images demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.
Abstract: Assessment for image quality traditionally needs its
original image as a reference. The conventional method for assessment
like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR)
is invalid when there is no reference. In this paper, we present a new
No-Reference (NR) assessment of image quality using blur and noise.
The recent camera applications provide high quality images by help of
digital Image Signal Processor (ISP). Since the images taken by the
high performance of digital camera have few blocking and ringing
artifacts, we only focus on the blur and noise for predicting the
objective image quality. The experimental results show that the
proposed assessment method gives high correlation with subjective
Difference Mean Opinion Score (DMOS). Furthermore, the proposed
method provides very low computational load in spatial domain and
similar extraction of characteristics to human perceptional assessment.
Abstract: Color image segmentation plays an important role in
computer vision and image processing areas. In this paper, the
features of Volterra filter are utilized for color image segmentation.
The discrete Volterra filter exhibits both linear and nonlinear
characteristics. The linear part smoothes the image features in
uniform gray zones and is used for getting a gross representation of
objects of interest. The nonlinear term compensates for the blurring
due to the linear term and preserves the edges which are mainly used
to distinguish the various objects. The truncated quadratic Volterra
filters are mainly used for edge preserving along with Gaussian noise
cancellation. In our approach, the segmentation is based on K-means
clustering algorithm in HSI space. Both the hue and the intensity
components are fully utilized. For hue clustering, the special cyclic
property of the hue component is taken into consideration. The
experimental results show that the proposed technique segments the
color image while preserving significant features and removing noise
effects.
Abstract: With the rapid growth and development of information and communication technology, the Internet has played a definite and irreplaceable role in people-s social lives in Taiwan like in other countries. In July 2008, on a general social website, an unexpected phenomenon was noticed – that there were more than one hundred users who started forming clubs voluntarily and having face-to-face gatherings for specific purposes. In this study, it-s argued whether or not teenagers- social contact on the Internet is involved in their life context, and tried to reveal the teenagers- social preferences, values, and needs, which merge with and influence teenagers- social activities. Therefore, the study conducts multiple user experience research methods, which include practical observations and qualitative analysis by contextual inquiries and in-depth interviews. Based on the findings, several design implications for software related to social interactions and cultural inheritance are offered. It is concluded that the inherent values of a social behaviors might be a key issue in developing computer-mediated communication or interaction designs in the future.
Abstract: Volume rendering is widely used in medical CT image
visualization. Applying 3D image visualization to diagnosis
application can require accurate volume rendering with high
resolution. Interpolation is important in medical image processing
applications such as image compression or volume resampling.
However, it can distort the original image data because of edge
blurring or blocking effects when image enhancement procedures
were applied. In this paper, we proposed adaptive tension control
method exploiting gradient information to achieve high resolution
medical image enhancement in volume visualization, where restored
images are similar to original images as much as possible. The
experimental results show that the proposed method can improve
image quality associated with the adaptive tension control efficacy.
Abstract: A new method, based on the normal shrink and
modified version of Katssagelous and Lay, is proposed for multiscale
blind image restoration. The method deals with the noise and blur in
the images. It is shown that the normal shrink gives the highest S/N
(signal to noise ratio) for image denoising process. The multiscale
blind image restoration is divided in two sections. The first part of
this paper proposes normal shrink for image denoising and the
second part of paper proposes modified version of katssagelous and
Lay for blur estimation and the combination of both methods to reach
a multiscale blind image restoration.
Abstract: This paper proposes a new technique based on nonlinear Minmax Detector Based (MDB) filter for image restoration. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Image degradation can be due to the addition of different types of noise in the original image. Image noise can be modeled of many types and impulse noise is one of them. Impulse noise generates pixels with gray value not consistent with their local neighborhood. It appears as a sprinkle of both light and dark or only light spots in the image. Filtering is a technique for enhancing the image. Linear filter is the filtering in which the value of an output pixel is a linear combination of neighborhood values, which can produce blur in the image. Thus a variety of smoothing techniques have been developed that are non linear. Median filter is the one of the most popular non-linear filter. When considering a small neighborhood it is highly efficient but for large window and in case of high noise it gives rise to more blurring to image. The Centre Weighted Mean (CWM) filter has got a better average performance over the median filter. However the original pixel corrupted and noise reduction is substantial under high noise condition. Hence this technique has also blurring affect on the image. To illustrate the superiority of the proposed approach, the proposed new scheme has been simulated along with the standard ones and various restored performance measures have been compared.
Abstract: In this paper we propose a new content-weighted
method for full reference (FR) video quality control using a region of
interest (ROI) and wherein two-component weighted metrics for Deaf
People Video Communication. In our approach, an image is
partitioned into region of interest and into region "dry-as-dust", then
region of interest is partitioned into two parts: edges and background
(smooth regions), while the another methods (metrics) combined and
weighted three or more parts as edges, edges errors, texture, smooth
regions, blur, block distance etc. as we proposed. Using another idea
that different image regions from deaf people video communication
have different perceptual significance relative to quality. Intensity
edges certainly contain considerable image information and are
perceptually significant.
Abstract: The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.
Abstract: This paper aims to present a survey of object
recognition/classification methods based on image moments. We
review various types of moments (geometric moments, complex
moments) and moment-based invariants with respect to various
image degradations and distortions (rotation, scaling, affine
transform, image blurring, etc.) which can be used as shape
descriptors for classification. We explain a general theory how to
construct these invariants and show also a few of them in explicit
forms. We review efficient numerical algorithms that can be used
for moment computation and demonstrate practical examples of
using moment invariants in real applications.
Abstract: The general idea behind the filter is to average a pixel
using other pixel values from its neighborhood, but simultaneously to
take care of important image structures such as edges. The main
concern of the proposed filter is to distinguish between any variations
of the captured digital image due to noise and due to image structure.
The edges give the image the appearance depth and sharpness. A
loss of edges makes the image appear blurred or unfocused.
However, noise smoothing and edge enhancement are traditionally
conflicting tasks. Since most noise filtering behaves like a low pass
filter, the blurring of edges and loss of detail seems a natural
consequence. Techniques to remedy this inherent conflict often
encompass generation of new noise due to enhancement.
In this work a new fuzzy filter is presented for the noise reduction
of images corrupted with additive noise. The filter consists of three
stages. (1) Define fuzzy sets in the input space to computes a fuzzy
derivative for eight different directions (2) construct a set of IFTHEN
rules by to perform fuzzy smoothing according to
contributions of neighboring pixel values and (3) define fuzzy sets in
the output space to get the filtered and edged image.
Experimental results are obtained to show the feasibility of the
proposed approach with two dimensional objects.
Abstract: Image interpolation is a common problem in imaging applications. However, most interpolation algorithms in existence suffer visually the effects of blurred edges and jagged artifacts in the image to some extent. This paper presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to sharpen edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts (“jaggies") along the tangent directions. In order to preserve image features such as edges, corners and textures, the nonlinear diffusion coefficients are locally adjusted according to the directional derivatives of the image. Experimental results on synthetic images and nature images demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.
Abstract: An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous region and diminish the edge-blurring effect and hence the name adaptive spatial finite mixture model. The proposed approach is compared with the spatially variant finite mixture model for pixel labeling. The experimental results with synthetic and Berkeley dataset demonstrate that the proposed method is effective in improving the segmentation and it can be employed in different practical image content understanding applications.
Abstract: In this work, we developed the concept of
supercompression, i.e., compression above the compression standard
used. In this context, both compression rates are multiplied. In fact,
supercompression is based on super-resolution. That is to say,
supercompression is a data compression technique that superpose
spatial image compression on top of bit-per-pixel compression to
achieve very high compression ratios. If the compression ratio is very
high, then we use a convolutive mask inside decoder that restores the
edges, eliminating the blur. Finally, both, the encoder and the
complete decoder are implemented on General-Purpose computation
on Graphics Processing Units (GPGPU) cards. Specifically, the
mentio-ned mask is coded inside texture memory of a GPGPU.
Abstract: Intelligent traffic surveillance technology is an issue in
the field of traffic data analysis. Therefore, we need the technology to
detect moving objects in real-time while there are variations in background and natural light. In this paper, we proposed a Weighted-Center Surround Difference
method for object detection in outdoor environments. The proposed system detects objects using the saliency map that is obtained by
analyzing the weight of each layers of Gaussian pyramid. In order to validate the effectiveness of our system, we implemented the proposed
method using a digital signal processor, TMS320DM6437.
Experimental results show that blurred noisy around objects was effectively eliminated and the object detection accuracy is improved.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: This paper describes a method of modeling to model
shadow play puppet using sophisticated computer graphics techniques
available in OpenGL in order to allow interactive play in real-time
environment as well as producing realistic animation. This paper
proposes a novel real-time method is proposed for modeling of puppet
and its shadow image that allows interactive play of virtual shadow
play using texture mapping and blending techniques. Special effects
such as lighting and blurring effects for virtual shadow play
environment are also developed. Moreover, the use of geometric
transformations and hierarchical modeling facilitates interaction
among the different parts of the puppet during animation. Based on the
experiments and the survey that were carried out, the respondents
involved are very satisfied with the outcomes of these techniques.
Abstract: Crucial information barely visible to the human eye is
often embedded in a series of low resolution images taken of the
same scene. Super resolution reconstruction is the process of
combining several low resolution images into a single higher
resolution image. The ideal algorithm should be fast, and should add
sharpness and details, both at edges and in regions without adding
artifacts. In this paper we propose a super resolution blind
reconstruction technique for linearly degraded images. In our
proposed technique the algorithm is divided into three parts an image
registration, wavelets based fusion and an image restoration. In this
paper three low resolution images are considered which may sub
pixels shifted, rotated, blurred or noisy, the sub pixel shifted images
are registered using affine transformation model; A wavelet based
fusion is performed and the noise is removed using soft thresolding.
Our proposed technique reduces blocking artifacts and also
smoothens the edges and it is also able to restore high frequency
details in an image. Our technique is efficient and computationally
fast having clear perspective of real time implementation.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: This paper presents an improved image segmentation
model with edge preserving regularization based on the
piecewise-smooth Mumford-Shah functional. A level set formulation
is considered for the Mumford-Shah functional minimization in
segmentation, and the corresponding partial difference equations are
solved by the backward Euler discretization. Aiming at encouraging
edge preserving regularization, a new edge indicator function is
introduced at level set frame. In which all the grid points which is used
to locate the level set curve are considered to avoid blurring the edges
and a nonlinear smooth constraint function as regularization term is
applied to smooth the image in the isophote direction instead of the
gradient direction. In implementation, some strategies such as a new
scheme for extension of u+ and u- computation of the grid points and
speedup of the convergence are studied to improve the efficacy of the
algorithm. The resulting algorithm has been implemented and
compared with the previous methods, and has been proved efficiently
by several cases.