Abstract: High-voltage power transmission lines are the back
bone of electrical power utilities. The stability and continuous
monitoring of this critical infrastructure is pivotal. Nine-Sigma
representing Eskom Holding SOC limited, South Africa has a major
problem on proactive detection of fallen power lines and real time
sagging measurement together with slipping of such conductors. The
main objective of this research is to innovate RFID technology to
solve this challenge. Various options and technologies such as GPS,
PLC, image processing, MR sensors and etc., have been reviewed
and draw backs were made. The potential of RFID to give precision
measurement will be observed and presented. The future research
will look at magnetic and electrical interference as well as corona
effect on the technology.
Abstract: Hand gesture is an active area of research in the vision
community, mainly for the purpose of sign language recognition and
Human Computer Interaction. In this paper, we propose a system to
recognize alphabet characters (A-Z) and numbers (0-9) in real-time
from stereo color image sequences using Hidden Markov Models
(HMMs). Our system is based on three main stages; automatic segmentation
and preprocessing of the hand regions, feature extraction
and classification. In automatic segmentation and preprocessing stage,
color and 3D depth map are used to detect hands where the hand
trajectory will take place in further step using Mean-shift algorithm
and Kalman filter. In the feature extraction stage, 3D combined features
of location, orientation and velocity with respected to Cartesian
systems are used. And then, k-means clustering is employed for
HMMs codeword. The final stage so-called classification, Baum-
Welch algorithm is used to do a full train for HMMs parameters.
The gesture of alphabets and numbers is recognized using Left-Right
Banded model in conjunction with Viterbi algorithm. Experimental
results demonstrate that, our system can successfully recognize hand
gestures with 98.33% recognition rate.
Abstract: This paper presents a system for tracking the movement of laparoscopic instruments which is based on an orthogonal system of webcams and video image processing. The movements are captured with two webcams placed orthogonally inside of the physical trainer. On the image, the instruments were detected by using color markers placed on the distal tip of each instrument. The 3D position of the tip of the instrument within the work space was obtained by linear triangulation method. Preliminary results showed linearity and repeatability in the motion tracking with a resolution of 0.616 mm in each axis; the accuracy of the system showed a 3D instrument positioning error of 1.009 ± 0.101 mm. This tool is a portable and low-cost alternative to traditional tracking devices and a trustable method for the objective evaluation of the surgeon’s surgical skills.
Abstract: The fuzzy technique is an operator introduced in order
to simulate at a mathematical level the compensatory behavior in
process of decision making or subjective evaluation. The following
paper introduces such operators on hand of computer vision
application.
In this paper a novel method based on fuzzy logic reasoning
strategy is proposed for edge detection in digital images without
determining the threshold value. The proposed approach begins by
segmenting the images into regions using floating 3x3 binary matrix.
The edge pixels are mapped to a range of values distinct from each
other. The robustness of the proposed method results for different
captured images are compared to those obtained with the linear Sobel
operator. It is gave a permanent effect in the lines smoothness and
straightness for the straight lines and good roundness for the curved
lines. In the same time the corners get sharper and can be defined
easily.
Abstract: In this paper, we propose a novel concept of relative
distance measurement using Stereo Vision Technology and discuss
its implementation on a FPGA based real-time image processor. We
capture two images using two CCD cameras and compare them.
Disparity is calculated for each pixel using a real time dense disparity
calculation algorithm. This algorithm is based on the concept of
indexed histogram for matching. Disparity being inversely
proportional to distance (Proved Later), we can thus get the relative
distances of objects in front of the camera. The output is displayed on
a TV screen in the form of a depth image (optionally using pseudo
colors). This system works in real time on a full PAL frame rate (720
x 576 active pixels @ 25 fps).
Abstract: Morphological operators transform the original image
into another image through the interaction with the other image of
certain shape and size which is known as the structure element.
Mathematical morphology provides a systematic approach to analyze
the geometric characteristics of signals or images, and has been
applied widely too many applications such as edge detection,
objection segmentation, noise suppression and so on. Fuzzy
Mathematical Morphology aims to extend the binary morphological
operators to grey-level images. In order to define the basic
morphological operations such as fuzzy erosion, dilation, opening
and closing, a general method based upon fuzzy implication and
inclusion grade operators is introduced. The fuzzy morphological
operations extend the ordinary morphological operations by using
fuzzy sets where for fuzzy sets, the union operation is replaced by a
maximum operation, and the intersection operation is replaced by a
minimum operation.
In this work, it consists of two articles. In the first one, fuzzy set
theory, fuzzy Mathematical morphology which is based on fuzzy
logic and fuzzy set theory; fuzzy Mathematical operations and their
properties will be studied in details. As a second part, the application
of fuzziness in Mathematical morphology in practical work such as
image processing will be discussed with the illustration problems.
Abstract: Gesture recognition is a challenging task for extracting
meaningful gesture from continuous hand motion. In this paper, we propose an automatic system that recognizes isolated gesture,
in addition meaningful gesture from continuous hand motion for Arabic numbers from 0 to 9 in real-time based on Hidden Markov Models (HMM). In order to handle isolated gesture, HMM using
Ergodic, Left-Right (LR) and Left-Right Banded (LRB) topologies is applied over the discrete vector feature that is extracted from stereo
color image sequences. These topologies are considered to different
number of states ranging from 3 to 10. A new system is developed to recognize the meaningful gesture based on zero-codeword detection
with static velocity motion for continuous gesture. Therefore, the
LRB topology in conjunction with Baum-Welch (BW) algorithm for
training and forward algorithm with Viterbi path for testing presents the best performance. Experimental results show that the proposed system can successfully recognize isolated and meaningful gesture and achieve average rate recognition 98.6% and 94.29% respectively.
Abstract: Needs of an efficient information retrieval in recent
years in increased more then ever because of the frequent use of
digital information in our life. We see a lot of work in the area of
textual information but in multimedia information, we cannot find
much progress. In text based information, new technology of data
mining and data marts are now in working that were started from the
basic concept of database some where in 1960.
In image search and especially in image identification,
computerized system at very initial stages. Even in the area of image
search we cannot see much progress as in the case of text based
search techniques. One main reason for this is the wide spread roots
of image search where many area like artificial intelligence,
statistics, image processing, pattern recognition play their role. Even
human psychology and perception and cultural diversity also have
their share for the design of a good and efficient image recognition
and retrieval system.
A new object based search technique is presented in this paper
where object in the image are identified on the basis of their
geometrical shapes and other features like color and texture where
object-co-relation augments this search process.
To be more focused on objects identification, simple images are
selected for the work to reduce the role of segmentation in overall
process however same technique can also be applied for other
images.
Abstract: This comparison of valuation techniques for bone age
assessment is a work carried out by the Telemedicine Research Group
of the Military University - TIGUM, as a preliminary to the Design
and development a treatment system of hand and wrist radiological
images for children aged 0-6 years to bone age assessment . In this
paper the techniques mentioned for decades have been the most
widely used and the statistically significant.
Althought, initially with the current project, it wants to work with
children who have limit age, this comparison and evaluation
techniques work will help in the future to expand the study subject in
the system to bone age assessment, implementing more techniques,
tools and deeper analysis to accomplish this purpose.
Abstract: As the world changes more rapidly, the demand for update information for resource management, environment monitoring, planning are increasing exponentially. Integration of Remote Sensing with GIS technology will significantly promote the ability for addressing these concerns. This paper presents an alternative way of update GIS applications using image processing and high resolution images. We show a method of high-resolution image segmentation using graphs and morphological operations, where a preprocessing step (watershed operation) is required. A morphological process is then applied using the opening and closing operations. After this segmentation we can extract significant cartographic elements such as urban areas, streets or green areas. The result of this segmentation and this extraction is then used to update GIS applications. Some examples are shown using aerial photography.
Abstract: A real time image-guided electroplating system is
proposed in this paper. Unlike previous electroplating systems, instead
of using the intermittent mode to electroplate 500um long copper
specimen, a CCD camera and a motion controller are used to adjust
anode-cathode distance to obtain better results. Since the image of the
gap distance is highly deteriorated due to complex chemical-electrical
operation inside the electrolyte, to determine the gap distance, an
image processing algorithm is developed and mainly based on the
entropy and energy values. In addition, the color and incidence
direction of light source are also discussed to help the image process in
this paper. From the experiment results, the specimens created by the
proposed system show better structure, better uniformity and better
finishing surface compared to those by previous intermittent
electroplating setup.
Abstract: Particle detection in very noisy and low contrast images
is an active field of research in image processing. In this article, a
method is proposed for the efficient detection and sizing of subsurface
spherical particles, which is used for the processing of softly fused
Au nanoparticles. Transmission Electron Microscopy is used for
imaging the nanoparticles, and the proposed algorithm has been
tested with the two-dimensional projected TEM images obtained.
Results are compared with the data obtained by transmission optical
spectroscopy, as well as with conventional circular object detection
algorithms.
Abstract: In this paper, we propose a method to extract the road
signs. Firstly, the grabbed image is converted into the HSV color space
to detect the road signs. Secondly, the morphological operations are
used to reduce noise. Finally, extract the road sign using the geometric
property. The feature extraction of road sign is done by using the color
information. The proposed method has been tested for the real
situations. From the experimental results, it is seen that the proposed
method can extract the road sign features effectively.
Abstract: Vision-based intelligent vehicle applications often require large amounts of memory to handle video streaming and image processing, which in turn increases complexity of hardware and software. This paper presents an FPGA implement of a vision-based blind spot warning system. Using video frames, the information of the blind spot area turns into one-dimensional information. Analysis of the estimated entropy of image allows the detection of an object in time. This idea has been implemented in the XtremeDSP video starter kit. The blind spot warning system uses only 13% of its logic resources and 95k bits block memory, and its frame rate is over 30 frames per sec (fps).
Abstract: In this paper, an artificial intelligent technique for
robust digital image watermarking in multiwavelet domain is
proposed. The embedding technique is based on the quantization
index modulation technique and the watermark extraction process
does not require the original image. We have developed an
optimization technique using the genetic algorithms to search for
optimal quantization steps to improve the quality of watermarked
image and robustness of the watermark. In addition, we construct a
prediction model based on image moments and back propagation
neural network to correct an attacked image geometrically before the
watermark extraction process begins. The experimental results show
that the proposed watermarking algorithm yields watermarked image
with good imperceptibility and very robust watermark against various
image processing attacks.
Abstract: This paper describes an algorithm to estimate realtime vehicle velocity using image processing technique from the known camera calibration parameters. The presented algorithm involves several main steps. First, the moving object is extracted by utilizing frame differencing technique. Second, the object tracking method is applied and the speed is estimated based on the displacement of the object-s centroid. Several assumptions are listed to simplify the transformation of 2D images from 3D real-world images. The results obtained from the experiment have been compared to the estimated ground truth. From this experiment, it exhibits that the proposed algorithm has achieved the velocity accuracy estimation of about ± 1.7 km/h.
Abstract: Clustering in high dimensional space is a difficult
problem which is recurrent in many fields of science and
engineering, e.g., bioinformatics, image processing, pattern
reorganization and data mining. In high dimensional space some of
the dimensions are likely to be irrelevant, thus hiding the possible
clustering. In very high dimensions it is common for all the objects in
a dataset to be nearly equidistant from each other, completely
masking the clusters. Hence, performance of the clustering algorithm
decreases.
In this paper, we propose an algorithmic framework which
combines the (reduct) concept of rough set theory with the k-means
algorithm to remove the irrelevant dimensions in a high dimensional
space and obtain appropriate clusters. Our experiment on test data
shows that this framework increases efficiency of the clustering
process and accuracy of the results.
Abstract: Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.
Abstract: The counting and analysis of blood cells allows the
evaluation and diagnosis of a vast number of diseases. In particular,
the analysis of white blood cells (WBCs) is a topic of great interest to
hematologists. Nowadays the morphological analysis of blood cells is
performed manually by skilled operators. This involves numerous
drawbacks, such as slowness of the analysis and a nonstandard
accuracy, dependent on the operator skills. In literature there are only
few examples of automated systems in order to analyze the white
blood cells, most of which only partial. This paper presents a
complete and fully automatic method for white blood cells
identification from microscopic images. The proposed method firstly
individuates white blood cells from which, subsequently, nucleus and
cytoplasm are extracted. The whole work has been developed using
MATLAB environment, in particular the Image Processing Toolbox.
Abstract: Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Maxima with threshold of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.