Abstract: Characterization of radio communication signals aims
at automatic recognition of different characteristics of radio signals in
order to detect their modulation type, the central frequency, and the
level. Our purpose is to apply techniques used in image processing in
order to extract pertinent characteristics. To the single analysis, we
add several rules for checking the consistency of hypotheses using
fuzzy logic. This allows taking into account ambiguity and
uncertainty that may remain after the extraction of individual
characteristics. The aim is to improve the process of radio
communications characterization.
Abstract: Digital watermarking has become an important technique for copyright protection but its robustness against attacks remains a major problem. In this paper, we propose a normalizationbased robust image watermarking scheme. In the proposed scheme, original host image is first normalized to a standard form. Zernike transform is then applied to the normalized image to calculate Zernike moments. Dither modulation is adopted to quantize the magnitudes of Zernike moments according to the watermark bit stream. The watermark extracting method is a blind method. Security analysis and false alarm analysis are then performed. The quality degradation of watermarked image caused by the embedded watermark is visually transparent. Experimental results show that the proposed scheme has very high robustness against various image processing operations and geometric attacks.
Abstract: Nowadays, with the emerging of the new applications
like robot control in image processing, artificial vision for visual
servoing is a rapidly growing discipline and Human-machine
interaction plays a significant role for controlling the robot. This
paper presents a new algorithm based on spatio-temporal volumes for
visual servoing aims to control robots. In this algorithm, after
applying necessary pre-processing on video frames, a spatio-temporal
volume is constructed for each gesture and feature vector is extracted.
These volumes are then analyzed for matching in two consecutive
stages. For hand gesture recognition and classification we tested
different classifiers including k-Nearest neighbor, learning vector
quantization and back propagation neural networks. We tested the
proposed algorithm with the collected data set and results showed the
correct gesture recognition rate of 99.58 percent. We also tested the
algorithm with noisy images and algorithm showed the correct
recognition rate of 97.92 percent in noisy images.
Abstract: Real-time object tracking is a problem which involves extraction of critical information from complex and uncertain imagedata. In this paper, we present a comprehensive methodology to design an artificial neural network (ANN) for a real-time object tracking application. The object, which is tracked for the purpose of demonstration, is a specific airplane. However, the proposed ANN can be trained to track any other object of interest. The ANN has been simulated and tested on the training and testing datasets, as well as on a real-time streaming video. The tracking error is analyzed with post-regression analysis tool, which finds the correlation among the calculated coordinates and the correct coordinates of the object in the image. The encouraging results from the computer simulation and analysis show that the proposed ANN architecture is a good candidate solution to a real-time object tracking problem.
Abstract: In this paper a fast motion estimation method for
H.264/AVC named Triplet Search Motion Estimation (TS-ME) is
proposed. Similar to some of the traditional fast motion estimation
methods and their improved proposals which restrict the search points
only to some selected candidates to decrease the computation
complexity, proposed algorithm separate the motion search process to
several steps but with some new features. First, proposed algorithm try
to search the real motion area using proposed triplet patterns instead of
some selected search points to avoid dropping into the local minimum.
Then, in the localized motion area a novel 3-step motion search
algorithm is performed. Proposed search patterns are categorized into
three rings on the basis of the distance from the search center. These
three rings are adaptively selected by referencing the surrounding
motion vectors to early terminate the motion search process. On the
other hand, computation reduction for sub pixel motion search is also
discussed considering the appearance probability of the sub pixel
motion vector. From the simulation results, motion estimation speed
improved by a factor of up to 38 when using proposed algorithm than
that of the reference software of H.264/AVC with ignorable picture
quality loss.
Abstract: The colors of the human skin represent a special
category of colors, because they are distinctive from the colors of
other natural objects. This category is found as a cluster in color
spaces, and the skin color variations between people are mostly due
to differences in the intensity. Besides, the face detection based on
skin color detection is a faster method as compared to other
techniques. In this work, we present a system to track faces by
carrying out skin color detection in four different color spaces: HSI,
YCbCr, YES and RGB. Once some skin color regions have been
detected for each color space, we label each and get some
characteristics such as size and position. We are supposing that a face
is located in one the detected regions. Next, we compare and employ
a polling strategy between labeled regions to determine the final
region where the face effectively has been detected and located.
Abstract: In recent five decades, textured yarns of polyester fiber produced by false twist method are the most
important and mass-produced manmade fibers. There are
many parameters of cross section which affect the physical and mechanical properties of textured yarns. These parameters
are surface area, perimeter, equivalent diameter, large
diameter, small diameter, convexity, stiffness, eccentricity, and hydraulic diameter. These parameters were evaluated by
digital image processing techniques. To find trends between production criteria and evaluated parameters of cross section, three criteria of production line have been adjusted and different types of yarns were produced. These criteria are
temperature, drafting ratio, and D/Y ratio. Finally the relations between production criteria and cross section parameters were
considered. The results showed that the presented technique can recognize and measure the parameters of fiber cross section in acceptable accuracy. Also, the optimum condition
of adjustments has been estimated from results of image analysis evaluation.
Abstract: Bleeding in the digestive duct is an important diagnostic parameter for patients. Blood in the endoscopic image can be determined by investigating the color tone of blood due to the degree of oxygenation, under- or over- illumination, food debris and secretions, etc. However, we found that how to pre-process raw images obtained from the capsule detectors was very important. We applied various image process methods suitable for the capsule endoscopic image in order to remove noises and unbalanced sensitivities for the image pixels. The results showed that much improvement was achieved by additional pre-processing techniques on the algorithm of determining bleeding areas.
Abstract: World has entered in 21st century. The technology of
computer graphics and digital cameras is prevalent. High resolution
display and printer are available. Therefore high resolution images
are needed in order to produce high quality display images and high
quality prints. However, since high resolution images are not usually
provided, there is a need to magnify the original images. One
common difficulty in the previous magnification techniques is that of
preserving details, i.e. edges and at the same time smoothing the data
for not introducing the spurious artefacts. A definitive solution to this
is still an open issue. In this paper an image magnification using
adaptive interpolation by pixel level data-dependent geometrical
shapes is proposed that tries to take into account information about
the edges (sharp luminance variations) and smoothness of the image.
It calculate threshold, classify interpolation region in the form of
geometrical shapes and then assign suitable values inside
interpolation region to the undefined pixels while preserving the
sharp luminance variations and smoothness at the same time.
The results of proposed technique has been compared qualitatively
and quantitatively with five other techniques. In which the qualitative
results show that the proposed method beats completely the Nearest
Neighbouring (NN), bilinear(BL) and bicubic(BC) interpolation. The
quantitative results are competitive and consistent with NN, BL, BC
and others.
Abstract: Two algorithms are proposed to reduce the storage requirements for mammogram images. The input image goes through a shrinking process that converts the 16-bit images to 8-bits by using pixel-depth conversion algorithm followed by enhancement process. The performance of the algorithms is evaluated objectively and subjectively. A 50% reduction in size is obtained with no loss of significant data at the breast region.
Abstract: One of the main environmental problems which affect extensive areas in the world is soil salinity. Traditional data collection methods are neither enough for considering this important environmental problem nor accurate for soil studies. Remote sensing data could overcome most of these problems. Although satellite images are commonly used for these studies, however there are still needs to find the best calibration between the data and real situations in each specified area. Neyshaboor area, North East of Iran was selected as a field study of this research. Landsat satellite images for this area were used in order to prepare suitable learning samples for processing and classifying the images. 300 locations were selected randomly in the area to collect soil samples and finally 273 locations were reselected for further laboratory works and image processing analysis. Electrical conductivity of all samples was measured. Six reflective bands of ETM+ satellite images taken from the study area in 2002 were used for soil salinity classification. The classification was carried out using common algorithms based on the best composition bands. The results showed that the reflective bands 7, 3, 4 and 1 are the best band composition for preparing the color composite images. We also found out, that hybrid classification is a suitable method for identifying and delineation of different salinity classes in the area.
Abstract: In image processing and visualization, comparing two
bitmapped images needs to be compared from their pixels by matching
pixel-by-pixel. Consequently, it takes a lot of computational time
while the comparison of two vector-based images is significantly
faster. Sometimes these raster graphics images can be approximately
converted into the vector-based images by various techniques. After
conversion, the problem of comparing two raster graphics images
can be reduced to the problem of comparing vector graphics images.
Hence, the problem of comparing pixel-by-pixel can be reduced to
the problem of polynomial comparisons. In computer aided geometric
design (CAGD), the vector graphics images are the composition of
curves and surfaces. Curves are defined by a sequence of control
points and their polynomials. In this paper, the control points will be
considerably used to compare curves. The same curves after relocated
or rotated are treated to be equivalent while two curves after different
scaled are considered to be similar curves. This paper proposed an
algorithm for comparing the polynomial curves by using the control
points for equivalence and similarity. In addition, the geometric
object-oriented database used to keep the curve information has also
been defined in XML format for further used in curve comparisons.
Abstract: Histogram plays an important statistical role in digital
image processing. However, the existing quantum image models are
deficient to do this kind of image statistical processing because
different gray scales are not distinguishable. In this paper, a novel
quantum image representation model is proposed firstly in which the
pixels with different gray scales can be distinguished and operated
simultaneously. Based on the new model, a fast quantum algorithm of
constructing histogram for quantum image is designed. Performance
comparison reveals that the new quantum algorithm could achieve an
approximately quadratic speedup than the classical counterpart. The
proposed quantum model and algorithm have significant meanings for
the future researches of quantum image processing.
Abstract: We proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color blinds. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them.
Abstract: The development of many measurement and inspection systems of products based on real-time image processing can not be carried out totally in a laboratory due to the size or the temperature of the manufactured products. Those systems must be developed in successive phases. Firstly, the system is installed in the production line with only an operational service to acquire images of the products and other complementary signals. Next, a recording service of the image and signals must be developed and integrated in the system. Only after a large set of images of products is available, the development of the real-time image processing algorithms for measurement or inspection of the products can be accomplished under realistic conditions. Finally, the recording service is turned off or eliminated and the system operates only with the real-time services for the acquisition and processing of the images. This article presents a systematic performance evaluation of the image compression algorithms currently available to implement a real-time recording service. The results allow establishing a trade off between the reduction or compression of the image size and the CPU time required to get that compression level.
Abstract: this paper gives a novel approach towards real-time speed estimation of multiple traffic vehicles using fuzzy logic and image processing techniques with proper arrangement of camera parameters. The described algorithm consists of several important steps. First, the background is estimated by computing median over time window of specific frames. Second, the foreground is extracted using fuzzy similarity approach (FSA) between estimated background pixels and the current frame pixels containing foreground and background. Third, the traffic lanes are divided into two parts for both direction vehicles for parallel processing. Finally, the speeds of vehicles are estimated by Maximum a Posterior Probability (MAP) estimator. True ground speed is determined by utilizing infrared sensors for three different vehicles and the results are compared to the proposed algorithm with an accuracy of ± 0.74 kmph.
Abstract: The purpose of this research is to compare the original
intra-oral digital dental radiograph images with images that are
enhanced using a combination of image processing algorithms. Intraoral
digital dental radiograph images are often noisy, blur edges and
low in contrast. A combination of sharpening and enhancement
method are used to overcome these problems. Three types of
proposed compound algorithms used are Sharp Adaptive Histogram
Equalization (SAHE), Sharp Median Adaptive Histogram
Equalization (SMAHE) and Sharp Contrast adaptive histogram
equalization (SCLAHE). This paper presents an initial study of the
perception of six dentists on the details of abnormal pathologies and
improvement of image quality in ten intra-oral radiographs. The
research focus on the detection of only three types of pathology
which is periapical radiolucency, widen periodontal ligament space
and loss of lamina dura. The overall result shows that SCLAHE-s
slightly improve the appearance of dental abnormalities- over the
original image and also outperform the other two proposed
compound algorithms.
Abstract: Image watermarking has proven to be quite an
efficient tool for the purpose of copyright protection and
authentication over the last few years. In this paper, a novel image
watermarking technique in the wavelet domain is suggested and
tested. To achieve more security and robustness, the proposed
techniques relies on using two nested watermarks that are embedded
into the image to be watermarked. A primary watermark in form of a
PN sequence is first embedded into an image (the secondary
watermark) before being embedded into the host image. The
technique is implemented using Daubechies mother wavelets where
an arbitrary embedding factor α is introduced to improve the
invisibility and robustness. The proposed technique has been applied
on several gray scale images where a PSNR of about 60 dB was
achieved.
Abstract: In modern human computer interaction systems
(HCI), emotion recognition is becoming an imperative characteristic.
The quest for effective and reliable emotion recognition in HCI has
resulted in a need for better face detection, feature extraction and
classification. In this paper we present results of feature space analysis
after briefly explaining our fully automatic vision based emotion
recognition method. We demonstrate the compactness of the feature
space and show how the 2d/3d based method achieves superior features
for the purpose of emotion classification. Also it is exposed that
through feature normalization a widely person independent feature
space is created. As a consequence, the classifier architecture has
only a minor influence on the classification result. This is particularly
elucidated with the help of confusion matrices. For this purpose
advanced classification algorithms, such as Support Vector Machines
and Artificial Neural Networks are employed, as well as the simple k-
Nearest Neighbor classifier.
Abstract: Although Face detection is not a recent activity in the
field of image processing, it is still an open area for research. The
greatest step in this field is the work reported by Viola and its recent
analogous is Huang et al. Both of them use similar features and also
similar training process. The former is just for detecting upright
faces, but the latter can detect multi-view faces in still grayscale
images using new features called 'sparse feature'. Finding these
features is very time consuming and inefficient by proposed methods.
Here, we propose a new approach for finding sparse features using a
genetic algorithm system. This method requires less computational
cost and gets more effective features in learning process for face
detection that causes more accuracy.