Abstract: Addis Ababa is a seat of African Union (AU), United
Nations Economic Commission for Africa (UN-ECA) and hundreds of
embassies and consular representatives. Addis Ababa is one of the
highest capitals in the world with an average 2400 meters above sea
level. It is dichotomous city with a blend of modern high-rise and
deteriorating slum quarters. Water supply and sanitation, waste
management and housing are continuing to be serious problems.
Forest wood based domestic energy use as well as uncontrolled
emissions from mobile and fixed sources has endangered the state of
the urban environment. Analysis based on satellite imagery has
revealed the deteriorating urban environment within the last three
decades. The recently restructured city administration has brought
improvements in the condition of the urban environment. However,
the overwhelming size of the challenges faced by the city dwarfed
their fairly good results.
Abstract: Image registration plays an important role in the
diagnosis of dental pathologies such as dental caries, alveolar bone
loss and periapical lesions etc. This paper presents a new wavelet
based algorithm for registering noisy and poor contrast dental x-rays.
Proposed algorithm has two stages. First stage is a preprocessing
stage, removes the noise from the x-ray images. Gaussian filter has
been used. Second stage is a geometric transformation stage.
Proposed work uses two levels of affine transformation. Wavelet
coefficients are correlated instead of gray values. Algorithm has been
applied on number of pre and post RCT (Root canal treatment)
periapical radiographs. Root Mean Square Error (RMSE) and
Correlation coefficients (CC) are used for quantitative evaluation.
Proposed technique outperforms conventional Multiresolution
strategy based image registration technique and manual registration
technique.
Abstract: In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for classifying fingerprints into four common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.
Abstract: Locality Sensitive Hashing (LSH) is one of the most
promising techniques for solving nearest neighbour search problem in
high dimensional space. Euclidean LSH is the most popular variation
of LSH that has been successfully applied in many multimedia
applications. However, the Euclidean LSH presents limitations that
affect structure and query performances. The main limitation of the
Euclidean LSH is the large memory consumption. In order to achieve
a good accuracy, a large number of hash tables is required. In this
paper, we propose a new hashing algorithm to overcome the storage
space problem and improve query time, while keeping a good
accuracy as similar to that achieved by the original Euclidean LSH.
The Experimental results on a real large-scale dataset show that the
proposed approach achieves good performances and consumes less
memory than the Euclidean LSH.
Abstract: Combining classifiers is a useful method for solving
complex problems in machine learning. The ECOC (Error Correcting
Output Codes) method has been widely used for designing combining
classifiers with an emphasis on the diversity of classifiers. In this
paper, in contrast to the standard ECOC approach in which individual
classifiers are chosen homogeneously, classifiers are selected
according to the complexity of the corresponding binary problem. We
use SATIMAGE database (containing 6 classes) for our experiments.
The recognition error rate in our proposed method is %10.37 which
indicates a considerable improvement in comparison with the
conventional ECOC and stack generalization methods.
Abstract: Naive Bayes Nearest Neighbor (NBNN) and its variants, i,e., local NBNN and the NBNN kernels, are local feature-based classifiers that have achieved impressive performance in image classification. By exploiting instance-to-class (I2C) distances (instance means image/video in image/video classification), they avoid quantization errors of local image descriptors in the bag of words (BoW) model. However, the performances of NBNN, local NBNN and the NBNN kernels have not been validated on video analysis. In this paper, we introduce these three classifiers into human action recognition and conduct comprehensive experiments on the benchmark KTH and the realistic HMDB datasets. The results shows that those I2C based classifiers consistently outperform the SVM classifier with the BoW model.
Abstract: Cellular automata have been used for design of cryptosystems. Recently some secret sharing schemes based on linear memory cellular automata have been introduced which are used for both text and image. In this paper, we illustrate that these secret sharing schemes are vulnerable to dishonest participants- collusion. We propose a cheating model for the secret sharing schemes based on linear memory cellular automata. For this purpose we present a novel uniform model for representation of all secret sharing schemes based on cellular automata. Participants can cheat by means of sending bogus shares or bogus transition rules. Cheaters can cooperate to corrupt a shared secret and compute a cheating value added to it. Honest participants are not aware of cheating and suppose the incorrect secret as the valid one. We prove that cheaters can recover valid secret by removing the cheating value form the corrupted secret. We provide methods of calculating the cheating value.
Abstract: Human pose estimation can be executed using Active Shape Models. The existing techniques for applying to human-body research using Active Shape Models, such as human detection, primarily take the form of silhouette of human body. This technique is not able to estimate accurately for human pose to concern two arms and legs, as the silhouette of human body represents the shape as out of round. To solve this problem, we applied the human body model as stick-figure, “skeleton". The skeleton model of human body can give consideration to various shapes of human pose. To obtain effective estimation result, we applied background subtraction and deformed matching algorithm of primary Active Shape Models in the fitting process. The images which were used to make the model were 600 human bodies, and the model has 17 landmark points which indicate body junction and key features of human pose. The maximum iteration for the fitting process was 30 times and the execution time was less than .03 sec.
Abstract: This paper presents an online method that learns the
corresponding points of an object from un-annotated grayscale images
containing instances of the object. In the first image being
processed, an ensemble of node points is automatically selected
which is matched in the subsequent images. A Bayesian posterior
distribution for the locations of the nodes in the images is formed.
The likelihood is formed from Gabor responses and the prior assumes
the mean shape of the node ensemble to be similar in a translation
and scale free space. An association model is applied for separating
the object nodes and background nodes. The posterior distribution is
sampled with Sequential Monte Carlo method. The matched object
nodes are inferred to be the corresponding points of the object
instances. The results show that our system matches the object nodes
as accurately as other methods that train the model with annotated
training images.
Abstract: A multimedia presentation system refers to the integration of a multimedia database with a presentation manager which has the functionality of content selection, organization and playout of multimedia presentations. It requires high performance of involved system components. Starting from multimedia information capture until the presentation delivery, high performance tools are required for accessing, manipulating, storing and retrieving these segments, for transferring and delivering them in a presentation terminal according to a playout order. The organization of presentations is a complex task in that the display order of presentation contents (in time and space) must be specified. A multimedia presentation contains audio, video, images and text media types. The critical decisions for presentation construction include what the contents are, how the contents are organized, and once the decision is made on the organization of the contents of the presentation, it must be conveyed to the end user in the correct organizational order and in a timely fashion. This paper introduces a framework for specification of multimedia presentations and describes the design of sample presentations using this framework from a multimedia database.
Abstract: This text studies glass bottle intelligent inspector
based machine vision instead of manual inspection. The system
structure is illustrated in detail in this paper. The text presents the
method based on watershed transform methods to segment the
possible defective regions and extract features of bottle wall by rules.
Then wavelet transform are used to exact features of bottle finish
from images. After extracting features, the fuzzy support vector
machine ensemble is putted forward as classifier. For ensuring that
the fuzzy support vector machines have good classification ability,
the GA based ensemble method is used to combining the several
fuzzy support vector machines. The experiments demonstrate that
using this inspector to inspect glass bottles, the accuracy rate may
reach above 97.5%.
Abstract: The intention of this lessons is to assess the probability
of optical coherence tomography (OCT) for biometric recognition.
The OCT is the foundation on an optical signal acquisition and
processing method and has the micrometer-resolution. In this study,
we used the porcine skin for verifying the abovementioned means. The
porcine tissue was sound acknowledged for structural and
immunohistochemical similarity with human skin, so it could be
suitable for pre-clinical trial as investigational specimen. For this
reason, it was tattooed by the tattoo machine with the tattoo-pigment.
We detected the pattern of the tattooed skin by the OCT according to
needle speed. The result was consistent with the histology images.
This result showed that the OCT was effective to examine the tattooed
skin section noninvasively. It might be available to identify
morphological changes inside the skin.
Abstract: Early detection of breast cancer is considered as a
major public health issue. Breast cancer screening is not generalized
to the entire population due to a lack of resources, staff and
appropriate tools. Systematic screening can result in a volume of data
which can not be managed by present computer architecture, either in
terms of storage capabilities or in terms of exploitation tools. We
propose in this paper to design and develop a data warehouse system
in radiology-senology (DWRS). The aim of such a system is on one
hand, to support this important volume of information providing from
multiple sources of data and images and for the other hand, to help
assist breast cancer screening in diagnosis, education and research.
Abstract: An effective visual error concealment method has been presented by employing a robust rotation, scale, and translation (RST) invariant partial patch matching model (RSTI-PPMM) and
exemplar-based inpainting. While the proposed robust and inherently
feature-enhanced texture synthesis approach ensures the generation
of excellent and perceptually plausible visual error concealment results, the outlier pruning property guarantees the significant quality improvements, both quantitatively and qualitatively. No intermediate
user-interaction is required for the pre-segmented media and the
presented method follows a bootstrapping approach for an automatic
visual loss recovery and the image and video error concealment.
Abstract: Both image steganography and image encryption have
advantages and disadvantages. Steganograhy allows us to hide a
desired image containing confidential information in a covered or
host image while image encryption is decomposing the desired image
to a non-readable, non-comprehended manner. The encryption
methods are usually much more robust than the steganographic ones.
However, they have a high visibility and would provoke the attackers
easily since it usually is obvious from an encrypted image that
something is hidden! The combination of steganography and
encryption will cover both of their weaknesses and therefore, it
increases the security. In this paper an image encryption method
based on sinc-convolution along with using an encryption key of 128
bit length is introduced. Then, the encrypted image is covered by a
host image using a modified version of JSteg steganography
algorithm. This method could be applied to almost all image formats
including TIF, BMP, GIF and JPEG. The experiment results show
that our method is able to hide a desired image with high security and
low visibility.
Abstract: The objective of the paper was to understand the use
of an important element of design, namely color in a Semiotic
system. Semiotics is the study of signs and sign processes, it is often
divided into three branches namely (i) Semantics that deals with the
relation between signs and the things to which they refer to mean, (ii)
Syntactics which addresses the relations among signs in formal
structures and (iii) Pragmatics that relates between signs and its
effects on they have on the people who use them to create a plan for
an object or a system referred to as design. Cubism with its versatility
was the key design tool prevalent across the 20th century. In order to
analyze the user's understanding of interaction and appreciation of
color through the movement of Cubism, an exercise was undertaken
in Dept. of Design, IIT Guwahati. This included tasks to design a
composition using color and sign process to the theme 'Between the
Lines' on a given tessellation where the users relate their work to the
world they live in, which in this case was the college campus of IIT
Guwahati. The findings demonstrate impact of the key design
element color on the principles of visual perception based on image
analysis of specific compositions.
Abstract: For a variety of safety and economic reasons, engineering undergraduates in Australia have experienced diminishing access to the real hardware that is typically the embodiment of their theoretical studies. This trend will delay the development of practical competence, decrease the ability to model and design, and suppress motivation. The author has attempted to address this concern by creating a software tool that contains both photographic images of real machinery, and sets of graphical modeling 'tools'. Academics from a range of disciplines can use the software to set tutorial tasks, and incorporate feedback comments for a range of student responses. An evaluation of the software demonstrated that students who had solved modeling problems with the aid of the electronic tutor performed significantly better in formal examinations with similar problems. The 2-D graphical diagnostic routines in the Tutor have the potential to be used in a wider range of problem-solving tasks.
Abstract: Recent developments in automotive technology are focused on economy, comfort and safety. Vehicle tracking and collision detection systems are attracting attention of many investigators focused on safety of driving in the field of automotive mechatronics. In this paper, a vision-based vehicle detection system is presented. Developed system is intended to be used in collision detection and driver alert. The system uses RGB images captured by a camera in a car driven in the highway. Images captured by the moving camera are used to detect the moving vehicles in the image. A vehicle ahead of the camera is detected in daylight conditions. The proposed method detects moving vehicles by subtracting successive images. Plate height of the vehicle is determined by using a plate recognition algorithm. Distance of the moving object is calculated by using the plate height. After determination of the distance of the moving vehicle relative speed of the vehicle and Time-to-Collision are calculated by using distances measured in successive images. Results obtained in road tests are discussed in order to validate the use of the proposed method.
Abstract: An Advance Driver Assistance System (ADAS) is a computer system on board a vehicle which is used to reduce the risk of vehicular accidents by monitoring factors relating to the driver, vehicle and environment and taking some action when a risk is identified. Much work has been done on assessing vehicle and environmental state but there is still comparatively little published work that tackles the problem of driver state. Visual attention is one such driver state. In fact, some researchers claim that lack of attention is the main cause of accidents as factors such as fatigue, alcohol or drug use, distraction and speeding all impair the driver-s capacity to pay attention to the vehicle and road conditions [1]. This seems to imply that the main cause of accidents is inappropriate driver behaviour in cases where the driver is not giving full attention while driving. The work presented in this paper proposes an ADAS system which uses an image based template matching algorithm to detect if a driver is failing to observe particular windscreen cells. This is achieved by dividing the windscreen into 24 uniform cells (4 rows of 6 columns) and matching video images of the driver-s left eye with eye-gesture templates drawn from images of the driver looking at the centre of each windscreen cell. The main contribution of this paper is to assess the accuracy of this approach using Receiver Operating Characteristic analysis. The results of our evaluation give a sensitivity value of 84.3% and a specificity value of 85.0% for the eye-gesture template approach indicating that it may be useful for driver point of regard determinations.
Abstract: The survival of publicly listed companies largely
depends on their stocks being liquidly traded. This goal can be
achieved when new investors are attracted to invest on companies-
stocks. Among different groups of investors, individual investors are
generally less able to objectively evaluate companies- risks and
returns, and tend to be emotionally biased in their investing
decisions. Therefore their decisions may be formed as a result of
perceived risks and returns, and influenced by companies- images.
This study finds that perceived risk, perceived returns and trust
directly affect individual investors- trading decisions while attitude
towards brand partially mediates the relationships. This finding
suggests that, in courting individual investors, companies still need to
perform financially while building a good image can result in their
stocks being accepted quicker than the stocks of good performing
companies with hidden images.