Abstract: In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.
Abstract: Liver segmentation is the first significant process for
liver diagnosis of the Computed Tomography. It segments the liver
structure from other abdominal organs. Sophisticated filtering techniques
are indispensable for a proper segmentation. In this paper, we
employ a 3D anisotropic diffusion as a preprocessing step. While
removing image noise, this technique preserve the significant parts
of the image, typically edges, lines or other details that are important
for the interpretation of the image. The segmentation task is done
by using thresholding with automatic threshold values selection and
finally the false liver region is eliminated using 3D connected component.
The result shows that by employing the 3D anisotropic filtering,
better liver segmentation results could be achieved eventhough simple
segmentation technique is used.
Abstract: In this paper we propose segmentation approach based
on Vector Quantization technique. Here we have used Kekre-s fast
codebook generation algorithm for segmenting low-altitude aerial
image. This is used as a preprocessing step to form segmented
homogeneous regions. Further to merge adjacent regions color
similarity and volume difference criteria is used. Experiments
performed with real aerial images of varied nature demonstrate that
this approach does not result in over segmentation or under
segmentation. The vector quantization seems to give far better results
as compared to conventional on-the-fly watershed algorithm.
Abstract: Interaction Model plays an important role in Modelbased
Intelligent Interface Agent Architecture for developing
Intelligent User Interface. In this paper we are presenting some
improvements in the algorithms for development interaction model of
interface agent including: the action segmentation algorithm, the
action pair selection algorithm, the final action pair selection
algorithm, the interaction graph construction algorithm and the
probability calculation algorithm. The analysis of the algorithms also
presented. At the end of this paper, we introduce an experimental
program called “Personal Transfer System".
Abstract: This paper proposes new enhancement models to the
methods of nonlinear anisotropic diffusion to greatly reduce speckle
and preserve image features in medical ultrasound images. By
incorporating local physical characteristics of the image, in this case
scatterer density, in addition to the gradient, into existing tensorbased
image diffusion methods, we were able to greatly improve the
performance of the existing filtering methods, namely edge
enhancing (EE) and coherence enhancing (CE) diffusion. The new
enhancement methods were tested using various ultrasound images,
including phantom and some clinical images, to determine the
amount of speckle reduction, edge, and coherence enhancements.
Scatterer density weighted nonlinear anisotropic diffusion
(SDWNAD) for ultrasound images consistently outperformed its
traditional tensor-based counterparts that use gradient only to weight
the diffusivity function. SDWNAD is shown to greatly reduce
speckle noise while preserving image features as edges, orientation
coherence, and scatterer density. SDWNAD superior performances
over nonlinear coherent diffusion (NCD), speckle reducing
anisotropic diffusion (SRAD), adaptive weighted median filter
(AWMF), wavelet shrinkage (WS), and wavelet shrinkage with
contrast enhancement (WSCE), make these methods ideal
preprocessing steps for automatic segmentation in ultrasound
imaging.
Abstract: Automatic segmentation of skin lesions is the first step
towards the automated analysis of malignant melanoma. Although
numerous segmentation methods have been developed, few studies
have focused on determining the most effective color space for
melanoma application. This paper proposes an automatic segmentation
algorithm based on color space analysis and clustering-based histogram
thresholding, a process which is able to determine the optimal
color channel for detecting the borders in dermoscopy images. The
algorithm is tested on a set of 30 high resolution dermoscopy images.
A comprehensive evaluation of the results is provided, where borders
manually drawn by four dermatologists, are compared to automated
borders detected by the proposed algorithm, applying three previously
used metrics of accuracy, sensitivity, and specificity and a new metric
of similarity. By performing ROC analysis and ranking the metrics,
it is demonstrated that the best results are obtained with the X and
XoYoR color channels, resulting in an accuracy of approximately
97%. The proposed method is also compared with two state-of-theart
skin lesion segmentation methods.
Abstract: The class of geometric deformable models, so-called
level sets, has brought tremendous impact to medical imagery. In
this paper we present yet another application of level sets to medical
imaging. The method we give here will in a way modify the speed
term in the standard level sets equation of motion. To do so we
build a potential based on the distance and the gradient of the
image we study. In turn the potential gives rise to the force field:
F~F(x, y) = P
∀(p,q)∈I
((x, y) - (p, q)) |ÔêçI(p,q)|
|(x,y)-(p,q)|
2 . The direction
and intensity of the force field at each point will determine the
direction of the contour-s evolution. The images we used to test
our method were produced by the Univesit'e de Sherbrooke-s PET
scanners.
Abstract: Understanding the number of people and the flow of
the persons is useful for efficient promotion of the institution
managements and company-s sales improvements. This paper
introduces an automated method for counting passerby using virtualvertical
measurement lines. The process of recognizing a passerby is
carried out using an image sequence obtained from the USB camera.
Space-time image is representing the human regions which are
treated using the segmentation process. To handle the problem of
mismatching, different color space are used to perform the template
matching which chose automatically the best matching to determine
passerby direction and speed. A relation between passerby speed and
the human-pixel area is used to distinguish one or two passersby. In
the experiment, the camera is fixed at the entrance door of the hall in
a side viewing position. Finally, experimental results verify the
effectiveness of the presented method by correctly detecting and
successfully counting them in order to direction with accuracy of
97%.
Abstract: Optical character recognition of cursive scripts
presents a number of challenging problems in both segmentation and
recognition processes in different languages, including Persian. In
order to overcome these problems, we use a newly developed Persian
word segmentation method and a recognition-based segmentation
technique to overcome its segmentation problems. This method is
robust as well as flexible. It also increases the system-s tolerances to
font variations. The implementation results of this method on a
comprehensive database show a high degree of accuracy which meets
the requirements for commercial use. Extended with a suitable pre
and post-processing, the method offers a simple and fast framework
to develop a full OCR system.
Abstract: In this paper we have proposed three and two
stage still gray scale image compressor based on BTC. In our
schemes, we have employed a combination of four techniques
to reduce the bit rate. They are quad tree segmentation, bit
plane omission, bit plane coding using 32 visual patterns and
interpolative bit plane coding. The experimental results show
that the proposed schemes achieve an average bit rate of 0.46
bits per pixel (bpp) for standard gray scale images with an
average PSNR value of 30.25, which is better than the results
from the exiting similar methods based on BTC.
Abstract: In this paper a new concept of partial complement of a graph G is introduced and using the same a new graph parameter, called completion number of a graph G, denoted by c(G) is defined. Some basic properties of graph parameter, completion number, are studied and upperbounds for completion number of classes of graphs are obtained , the paper includes the characterization also.
Abstract: In this paper three different approaches for person
verification and identification, i.e. by means of fingerprints, face and
voice recognition, are studied. Face recognition uses parts-based
representation methods and a manifold learning approach. The
assessment criterion is recognition accuracy. The techniques under
investigation are: a) Local Non-negative Matrix Factorization
(LNMF); b) Independent Components Analysis (ICA); c) NMF with
sparse constraints (NMFsc); d) Locality Preserving Projections
(Laplacianfaces). Fingerprint detection was approached by classical
minutiae (small graphical patterns) matching through image
segmentation by using a structural approach and a neural network as
decision block. As to voice / speaker recognition, melodic cepstral
and delta delta mel cepstral analysis were used as main methods, in
order to construct a supervised speaker-dependent voice recognition
system. The final decision (e.g. “accept-reject" for a verification
task) is taken by using a majority voting technique applied to the
three biometrics. The preliminary results, obtained for medium
databases of fingerprints, faces and voice recordings, indicate the
feasibility of our study and an overall recognition precision (about
92%) permitting the utilization of our system for a future complex
biometric card.
Abstract: This study aims to segment objects using the K-means
algorithm for texture features. Firstly, the algorithm transforms color
images into gray images. This paper describes a novel technique for
the extraction of texture features in an image. Then, in a group of
similar features, objects and backgrounds are differentiated by using
the K-means algorithm. Finally, this paper proposes a new object
segmentation algorithm using the morphological technique. The
experiments described include the segmentation of single and multiple
objects featured in this paper. The region of an object can be
accurately segmented out. The results can help to perform image
retrieval and analyze features of an object, as are shown in this paper.
Abstract: The increasing demand for sufficient and clean
energy forces industrial and service companies to align their strategies towards efficient consumption. This trend refers also to the
residential building sector. There, large amounts of energy consumption are caused by house and facility heating. Many of the
operated hot water heating systems lack hydraulic balanced working
conditions for heat distribution and –transmission and lead to
inefficient heating. Through hydraulic balancing of heating systems,
significant energy savings for primary and secondary energy can be
achieved. This paper addresses the use of KNX-technology (Smart
Buildings) in residential buildings to ensure a dynamic adaption of
hydraulic system's performance, in order to increase the heating
system's efficiency. In this paper, the procedure of heating system
segmentation into hydraulically independent units (meshes) is
presented. Within these meshes, the heating valve are addressed and
controlled by a central facility server. Feasibility criteria towards
such drivers will be named. The dynamic hydraulic balance is
achieved by positioning these valves according to heating loads, that
are generated from the temperature settings in the corresponding
rooms. The energetic advantages of single room heating control
procedures, based on the application FacilityManager, is presented.
Abstract: This paper addresses the development of an intelligent vision system for human-robot interaction. The two novel contributions of this paper are 1) Detection of human faces and 2) Localizing the eye. The method is based on visual attributes of human skin colors and geometrical analysis of face skeleton. This paper introduces a spatial domain filtering method named ?Fuzzily skewed filter' which incorporates Fuzzy rules for deciding the gray level of pixels in the image in their neighborhoods and takes advantages of both the median and averaging filters. The effectiveness of the method has been justified over implementing the eye tracking commands to an entertainment robot, named ''AIBO''.
Abstract: Electrocardiogram (ECG) segmentation is necessary
to help reduce the time consuming task of manually annotating
ECG-s. Several algorithms have been developed to segment the ECG
automatically. We first review several of such methods, and then
present a new single lead segmentation method based on Adaptive
piecewise constant approximation (APCA) and Piecewise derivative
dynamic time warping (PDDTW). The results are tested on the QT
database. We compared our results to Laguna-s two lead method. Our
proposed approach has a comparable mean error, but yields a slightly
higher standard deviation than Laguna-s method.
Abstract: In this paper we propose a new knowledge model using
the Dempster-Shafer-s evidence theory for image segmentation and
fusion. The proposed method is composed essentially of two steps.
First, mass distributions in Dempster-Shafer theory are obtained from
the membership degrees of each pixel covering the three image
components (R, G and B). Each membership-s degree is determined by
applying Fuzzy C-Means (FCM) clustering to the gray levels of the
three images. Second, the fusion process consists in defining three
discernment frames which are associated with the three images to be
fused, and then combining them to form a new frame of discernment.
The strategy used to define mass distributions in the combined
framework is discussed in detail. The proposed fusion method is
illustrated in the context of image segmentation. Experimental
investigations and comparative studies with the other previous methods
are carried out showing thus the robustness and superiority of the
proposed method in terms of image segmentation.
Abstract: This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, to effectively extract spotlight of interest, a segmentation process based on automatic multi-level threshold method is applied on the road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process based on light tracking and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with near infrared mono-camera and tested it in the urban and rural roads. Through the test, classification performances are above 97% of true positive rate evaluated on real-time environment. Our method also has good performance in the case of clear, fog and rain weather.
Abstract: Detection and recognition of the Human Body Composition and extraction their measures (width and length of human body) in images are a major issue in detecting objects and the important field in Image, Signal and Vision Computing in recent years. Finding people and extraction their features in Images are particularly important problem of object recognition, because people can have high variability in the appearance. This variability may be due to the configuration of a person (e.g., standing vs. sitting vs. jogging), the pose (e.g. frontal vs. lateral view), clothing, and variations in illumination. In this study, first, Human Body is being recognized in image then the measures of Human Body extract from the image.
Abstract: This paper presents a new color face image database
for benchmarking of automatic face detection algorithms and human
skin segmentation techniques. It is named the VT-AAST image
database, and is divided into four parts. Part one is a set of 286 color
photographs that include a total of 1027 faces in the original format
given by our digital cameras, offering a wide range of difference in
orientation, pose, environment, illumination, facial expression and
race. Part two contains the same set in a different file format. The
third part is a set of corresponding image files that contain human
colored skin regions resulting from a manual segmentation
procedure. The fourth part of the database has the same regions
converted into grayscale. The database is available on-line for
noncommercial use. In this paper, descriptions of the database
development, organization, format as well as information needed for
benchmarking of algorithms are depicted in detail.