Abstract: The use of machine vision to inspect the outcome of
surgical tasks is investigated, with the aim of incorporating this
approach in robotic surgery systems. Machine vision is a non-contact
form of inspection i.e. no part of the vision system is in direct contact
with the patient, and is therefore well suited for surgery where
sterility is an important consideration,. As a proof-of-concept, three
primary surgical tasks for a common neurosurgical procedure were
inspected using machine vision. Experiments were performed on
cadaveric pig heads to simulate the two possible outcomes i.e.
satisfactory or unsatisfactory, for tasks involved in making a burr
hole, namely incision, retraction, and drilling. We identify low level
image features to distinguish the two outcomes, as well as report on
results that validate our proposed approach. The potential of using
machine vision in a surgical environment, and the challenges that
must be addressed, are identified and discussed.
Abstract: Robots- visual perception is a field that is gaining
increasing attention from researchers. This is partly due to emerging
trends in the commercial availability of 3D scanning systems or
devices that produce a high information accuracy level for a variety of
applications. In the history of mining, the mortality rate of mine workers
has been alarming and robots exhibit a great deal of potentials to
tackle safety issues in mines. However, an effective vision system
is crucial to safe autonomous navigation in underground terrains.
This work investigates robots- perception in underground terrains
(mines and tunnels) using statistical region merging (SRM) model.
SRM reconstructs the main structural components of an imagery
by a simple but effective statistical analysis. An investigation is
conducted on different regions of the mine, such as the shaft, stope
and gallery, using publicly available mine frames, with a stream of
locally captured mine images. An investigation is also conducted on a
stream of underground tunnel image frames, using the XBOX Kinect
3D sensors. The Kinect sensors produce streams of red, green and
blue (RGB) and depth images of 640 x 480 resolution at 30 frames per
second. Integrating the depth information to drivability gives a strong
cue to the analysis, which detects 3D results augmenting drivable and
non-drivable regions in 2D. The results of the 2D and 3D experiment
with different terrains, mines and tunnels, together with the qualitative
and quantitative evaluation, reveal that a good drivable region can be
detected in dynamic underground terrains.
Abstract: In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.