Abstract: The shear modulus of a timber beam can be determined
using torsion test or shear field test method. The shear field test
method is based on shear distortion measurement of the beam at the
zone with the constant transverse load in the standardized four-point
bending test. The current code of practice advises using two metallic
arms act as an instrument to measure the diagonal displacement of
the constructing square. The size and the position of the constructing
square might influence the shear modulus determination. This study
aimed to investigate the size and the position effect of the square
in the shear field test method. A binocular stereo vision system has
been employed to determine the 3D displacement of a grid of target
points. Six glue laminated beams were produced and tested. Analysis
of Variance (ANOVA) was performed on the acquired data to evaluate
the significance of the size effect and the position effect of the square.
The results have shown that the size of the square has a noticeable
influence on the value of shear modulus, while, the position of the
square within the area with the constant shear force does not affect
the measured mean shear modulus.
Abstract: The timber beam end effect in the torsion test is
evaluated using binocular stereo vision system. It is recommended by
BS EN 408:2010+A1:2012 to exclude a distance of two to three times
of cross-sectional thickness (b) from ends to avoid the end effect;
whereas, this study indicates that this distance is not sufficiently far
enough to remove this effect in slender cross-sections. The shear
modulus of six timber beams with different aspect ratios is determined
at the various angles and cross-sections. The result of this experiment
shows that the end affected span of each specimen varies depending
on their aspect ratios. It is concluded that by increasing the aspect
ratio this span will increase. However, by increasing the distance
from the ends to the values greater than 6b, the shear modulus trend
becomes constant and end effect will be negligible. Moreover, it is
concluded that end affected span is preferred to be depth-dependent
rather than thickness-dependant.
Abstract: Motion Tracking and Stereo Vision are complicated,
albeit well-understood problems in computer vision. Existing
softwares that combine the two approaches to perform stereo motion
tracking typically employ complicated and computationally expensive
procedures. The purpose of this study is to create a simple and
effective solution capable of combining the two approaches. The
study aims to explore a strategy to combine the two techniques
of two-dimensional motion tracking using Kalman Filter; and depth
detection of object using Stereo Vision. In conventional approaches
objects in the scene of interest are observed using a single camera.
However for Stereo Motion Tracking; the scene of interest is
observed using video feeds from two calibrated cameras. Using two
simultaneous measurements from the two cameras a calculation for
the depth of the object from the plane containing the cameras is made.
The approach attempts to capture the entire three-dimensional spatial
information of each object at the scene and represent it through a
software estimator object. In discrete intervals, the estimator tracks
object motion in the plane parallel to plane containing cameras and
updates the perpendicular distance value of the object from the plane
containing the cameras as depth. The ability to efficiently track
the motion of objects in three-dimensional space using a simplified
approach could prove to be an indispensable tool in a variety of
surveillance scenarios. The approach may find application from high
security surveillance scenes such as premises of bank vaults, prisons
or other detention facilities; to low cost applications in supermarkets
and car parking lots.
Abstract: This paper presents a review on vision aided systems
and proposes an approach for visual rehabilitation using stereo vision
technology. The proposed system utilizes stereo vision, image
processing methodology and a sonification procedure to support
blind navigation. The developed system includes a wearable
computer, stereo cameras as vision sensor and stereo earphones, all
moulded in a helmet. The image of the scene infront of visually
handicapped is captured by the vision sensors. The captured images
are processed to enhance the important features in the scene in front,
for navigation assistance. The image processing is designed as model
of human vision by identifying the obstacles and their depth
information. The processed image is mapped on to musical stereo
sound for the blind-s understanding of the scene infront. The
developed method has been tested in the indoor and outdoor
environments and the proposed image processing methodology is
found to be effective for object identification.
Abstract: This paper presents the results of enhancing images from a left and right stereo pair in order to increase the resolution of a 3D representation of a scene generated from that same pair. A new neural network structure known as a Self Delaying Dynamic Network (SDN) has been used to perform the enhancement. The advantage of SDNs over existing techniques such as bicubic interpolation is their ability to cope with motion and noise effects. SDNs are used to generate two high resolution images, one based on frames taken from the left view of the subject, and one based on the frames from the right. This new high resolution stereo pair is then processed by a disparity map generator. The disparity map generated is compared to two other disparity maps generated from the same scene. The first is a map generated from an original high resolution stereo pair and the second is a map generated using a stereo pair which has been enhanced using bicubic interpolation. The maps generated using the SDN enhanced pairs match more closely the target maps. The addition of extra noise into the input images is less problematic for the SDN system which is still able to out perform bicubic interpolation.
Abstract: Optical 3D measurement of objects is meaningful in
numerous industrial applications. In various cases shape acquisition
of weak textured objects is essential. Examples are repetition parts
made of plastic or ceramic such as housing parts or ceramic bottles as
well as agricultural products like tubers. These parts are often
conveyed in a wobbling way during the automated optical inspection.
Thus, conventional 3D shape acquisition methods like laser scanning
might fail. In this paper, a novel approach for acquiring 3D shape of
weak textured and moving objects is presented. To facilitate such
measurements an active stereo vision system with structured light is
proposed. The system consists of multiple camera pairs and auxiliary
laser pattern generators. It performs the shape acquisition within one
shot and is beneficial for rapid inspection tasks. An experimental
setup including hardware and software has been developed and
implemented.
Abstract: This paper presents a robust method to detect obstacles in stereo images using shadow removal technique and color information. Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle depth using stereo matching and disparity map. The proposed advanced method is divided into three phases, the first phase is detecting obstacles and removing shadows, the second one is matching and the last phase is depth computing. We propose a robust method for detecting obstacles in stereo images using a shadow removal technique based on color information in HIS space, at the first phase. In this paper we use Normalized Cross Correlation (NCC) function matching with a 5 × 5 window and prepare an empty matching table τ and start growing disparity components by drawing a seed s from S which is computed using canny edge detector, and adding it to τ. In this way we achieve higher performance than the previous works [2,17]. A fast stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. The obstacle identified in phase one which appears in the disparity map of phase two enters to the third phase of depth computing. Finally, experimental results are presented to show the effectiveness of the proposed method.
Abstract: This paper proposes a novel stereo vision technique
for top view book scanners which provide us with dense 3d point
clouds of page surfaces. This is a precondition to dewarp bound
volumes independent of 2d information on the page. Our method is
based on algorithms, which normally require the projection of pattern
sequences with structured light. We use image sequences of the
moving stripe lighting of the top view scanner instead of an additional
light projection. Thus the stereo vision setup is simplified without
losing measurement accuracy. Furthermore we improve a surface
model dewarping method through introducing a difference vector
based on real measurements. Although our proposed method is hardly
expensive neither in calculation time nor in hardware requirements
we present good dewarping results even for difficult examples.
Abstract: This paper presents a new feature based dense stereo
matching algorithm to obtain the dense disparity map via dynamic
programming. After extraction of some proper features, we use some
matching constraints such as epipolar line, disparity limit, ordering
and limit of directional derivative of disparity as well. Also, a coarseto-
fine multiresolution strategy is used to decrease the search space
and therefore increase the accuracy and processing speed. The
proposed method links the detected feature points into the chains and
compares some of the feature points from different chains, to
increase the matching speed. We also employ color stereo matching
to increase the accuracy of the algorithm. Then after feature
matching, we use the dynamic programming to obtain the dense
disparity map. It differs from the classical DP methods in the stereo
vision, since it employs sparse disparity map obtained from the
feature based matching stage. The DP is also performed further on a
scan line, between any matched two feature points on that scan line.
Thus our algorithm is truly an optimization method. Our algorithm
offers a good trade off in terms of accuracy and computational
efficiency. Regarding the results of our experiments, the proposed
algorithm increases the accuracy from 20 to 70%, and reduces the
running time of the algorithm almost 70%.
Abstract: An algorithm for estimating the disparity of objects of
interest is proposed. This algorithm uses image shifting and
overlapping area to estimate the disparity value; thereby depth of the
objects of interest can be obtained. The algorithm is able to perform
at different levels of accuracy. However, as the accuracy increases
the processing speed decreases. The algorithm is tested with static
stereo images and sequence of stereo images. The experimental
results are presented in this paper.
Abstract: This paper present an effective method to accurately reconstruct and measure the 3D curve edges of small industrial parts based on stereo vision. To effectively fit the curve of the measured parts using a series of line segments in the images, a strategy from coarse to fine is employed based on multi-scale curve fitting. After reconstructing the 3D curve of a hole through a curved surface, its axis is adjusted so that it is parallel to the Z axis with least squares error and the dimensions of the hole can be calculated on the XY plane easily. Experimental results show that the presented method can accurately measure the dimensions of round holes through a curved surface.
Abstract: The Continuously Adaptive Mean-Shift (CamShift)
algorithm, incorporating scene depth information is combined with
the l1-minimization sparse representation based method to form a
hybrid kernel and state space-based tracking algorithm. We take
advantage of the increased efficiency of the former with the
robustness to occlusion property of the latter. A simple interchange
scheme transfers control between algorithms based upon drift and
occlusion likelihood. It is quantified by the projection of target
candidates onto a depth map of the 2D scene obtained with a low cost
stereo vision webcam. Results are improved tracking in terms of drift
over each algorithm individually, in a challenging practical outdoor
multiple occlusion test case.
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).