Abstract: Using bottom-up image processing algorithms to predict human eye fixations and extract the relevant embedded information in images has been widely applied in the design of active machine vision systems. Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. This paper proposes an edge-based text region extraction algorithm, which is robust with respect to font sizes, styles, color/intensity, orientations, and effects of illumination, reflections, shadows, perspective distortion, and the complexity of image backgrounds. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.
Abstract: We developed a GPS-based navigation device for the
blind, with audio guidance in Thai language. The device is composed
of simple and inexpensive hardware components. Its user interface is
quite simple. It determines optimal routes to various landmarks in our
university campus by using heuristic search for the next waypoints.
We tested the device and made note of its limitations and possible
extensions.
Abstract: This paper presents a novel template-based method to
detect objects of interest from real images by shape matching. To
locate a target object that has a similar shape to a given template
boundary, the proposed method integrates three components: contour
grouping, partial shape matching, and boundary verification. In the
first component, low-level image features, including edges and
corners, are grouped into a set of perceptually salient closed contours
using an extended ratio-contour algorithm. In the second component,
we develop a partial shape matching algorithm to identify the
fractions of detected contours that partly match given template
boundaries. Specifically, we represent template boundaries and
detected contours using landmarks, and apply a greedy algorithm to
search the matched landmark subsequences. For each matched
fraction between a template and a detected contour, we estimate an
affine transform that transforms the whole template into a hypothetic
boundary. In the third component, we provide an efficient algorithm
based on oriented edge lists to determine the target boundary from
the hypothetic boundaries by checking each of them against image
edges. We evaluate the proposed method on recognizing and
localizing 12 template leaves in a data set of real images with clutter
back-grounds, illumination variations, occlusions, and image noises.
The experiments demonstrate the high performance of our proposed
method1.
Abstract: Detection and tracking of the lip contour is an important
issue in speechreading. While there are solutions for lip tracking
once a good contour initialization in the first frame is available,
the problem of finding such a good initialization is not yet solved
automatically, but done manually. We have developed a new tracking
solution for lip contour detection using only few landmarks (15
to 25) and applying the well known Active Shape Models (ASM).
The proposed method is a new LMS-like adaptive scheme based on
an Auto regressive (AR) model that has been fit on the landmark
variations in successive video frames. Moreover, we propose an extra
motion compensation model to address more general cases in lip
tracking. Computer simulations demonstrate a fair match between
the true and the estimated spatial pixels. Significant improvements
related to the well known LMS approach has been obtained via a
defined Frobenius norm index.
Abstract: In the past years a lot of effort has been made in the
field of face detection. The human face contains important features
that can be used by vision-based automated systems in order to
identify and recognize individuals. Face location, the primary step of
the vision-based automated systems, finds the face area in the input
image. An accurate location of the face is still a challenging task.
Viola-Jones framework has been widely used by researchers in order
to detect the location of faces and objects in a given image. Face
detection classifiers are shared by public communities, such as
OpenCV. An evaluation of these classifiers will help researchers to
choose the best classifier for their particular need. This work focuses
of the evaluation of face detection classifiers minding facial
landmarks.
Abstract: Realistic 3D face model is more precise in representing
pose, illumination, and expression of face than 2D face model so that it
can be utilized usefully in various applications such as face recognition,
games, avatars, animations, and etc.
In this paper, we propose a 3D face modeling method based on 3D
dense morphable shape model. The proposed 3D modeling method
first constructs a 3D dense morphable shape model from 3D face scan
data obtained using a 3D scanner. Next, the proposed method extracts
and matches facial landmarks from 2D image sequence containing a
face to be modeled, and then reconstructs 3D vertices coordinates of
the landmarks using a factorization-based SfM technique. Then, the
proposed method obtains a 3D dense shape model of the face to be
modeled by fitting the constructed 3D dense morphable shape model
into the reconstructed 3D vertices. Also, the proposed method makes a
cylindrical texture map using 2D face image sequence. Finally, the
proposed method generates a 3D face model by rendering the 3D dense
face shape model using the cylindrical texture map. Through building
processes of 3D face model by the proposed method, it is shown that
the proposed method is relatively easy, fast and precise.
Abstract: The Cluster Dimension of a network is defined as, which is the minimum cardinality of a subset S of the set of nodes having the property that for any two distinct nodes x and y, there exist the node Si, s2 (need not be distinct) in S such that ld(x,s1) — d(y, s1)1 > 1 and d(x,s2) < d(x,$) for all s E S — {s2}. In this paper, strictly non overlap¬ping clusters are constructed. The concept of LandMarks for Unique Addressing and Clustering (LMUAC) routing scheme is developed. With the help of LMUAC routing scheme, It is shown that path length (upper bound)PLN,d < PLD, Maximum memory space requirement for the networkMSLmuAc(Az) < MSEmuAc < MSH3L < MSric and Maximum Link utilization factor MLLMUAC(i=3) < MLLMUAC(z03) < M Lc