Abstract: Biometrics, which refers to identifying an individual
based on his or her physiological or behavioral characteristics, has
the capability to reliably distinguish between an authorized person
and an imposter. Signature verification systems can be categorized as
offline (static) and online (dynamic). This paper presents a neural
network based recognition of offline handwritten signatures system
that is trained with low-resolution scanned signature images.
Abstract: Many problems in computer vision and image
processing present potential for parallel implementations through one
of the three major paradigms of geometric parallelism, algorithmic
parallelism and processor farming. Static process scheduling
techniques are used successfully to exploit geometric and algorithmic
parallelism, while dynamic process scheduling is better suited to
dealing with the independent processes inherent in the process
farming paradigm. This paper considers the application of parallel or
multi-computers to a class of problems exhibiting spatial data
characteristic of the geometric paradigm. However, by using
processor farming paradigm, a dynamic scheduling technique is
developed to suit the MIMD structure of the multi-computers. A
hybrid scheme of scheduling is also developed and compared with
the other schemes. The specific problem chosen for the investigation
is the Hough transform for line detection.
Abstract: The paper describes a knowledge based system for
analysis of microscopic wear particles. Wear particles contained in
lubricating oil carry important information concerning machine
condition, in particular the state of wear. Experts (Tribologists) in the
field extract this information to monitor the operation of the machine
and ensure safety, efficiency, quality, productivity, and economy of
operation. This procedure is not always objective and it can also be
expensive. The aim is to classify these particles according to their
morphological attributes of size, shape, edge detail, thickness ratio,
color, and texture, and by using this classification thereby predict
wear failure modes in engines and other machinery. The attribute
knowledge links human expertise to the devised Knowledge Based
Wear Particle Analysis System (KBWPAS). The system provides an
automated and systematic approach to wear particle identification
which is linked directly to wear processes and modes that occur in
machinery. This brings consistency in wear judgment prediction
which leads to standardization and also less dependence on
Tribologists.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear particle
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear particle. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear debris
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self-organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear debris. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.