Haptic Modeling of textile has attracted significant interest over the last decade. In spite of extensive research, no generic
system has been proposed. The previous work mainly assumes that textile has a 2D planar structure. They also require
time-consuming measurement of textile properties in construction of the mechanical model. A novel approach for haptic
modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure
for the textile, and deploys computational intelligence to estimate the mechanical properties of textile. The approach is
designed primarily for display of textile artifacts in museums. The haptic model is constructed by superimposing the
mechanical model of textile over its geometrical model. Digital image processing is applied to the still image of textile to
identify its pattern and structure through a fuzzy rule-base algorithm. The 3D geometric model of the artifact is
automatically generated in VRML based on the identified pattern and structure obtained from the textile image. Selected
mechanical properties of the textile are estimated by an artificial neural network; deploying the textile geometric
characteristics and yarn properties as inputs. The estimated mechanical properties are then deployed in the construction
of the textile mechanical model. The proposed system is introduced and the developed algorithms are described. The
validation of method indicates the feasibility of the approach and its superiority to other haptic modeling algorithms.
KEYWORDS: 3D modeling, Fuzzy logic, Haptic technology, Algorithm development, Systems modeling, Detection and tracking algorithms, Pattern recognition, Modeling, Data modeling, Visual process modeling
Geometric modeling and haptic rendering of textile has attracted significant interest over the last decade. A haptic representation is created by adding the physical properties of an object to its geometric configuration. While research has been conducted into geometric modeling of fabric, current systems require time-consuming manual recognition of textile specifications and data entry. The development of a generic approach for construction of the 3D geometric model of a woven textile is pursued in this work. The geometric model would be superimposed by a haptic model in the future work. The focus at this stage is on hand-woven textile artifacts for display in museums. A fuzzy rule based algorithm is applied to the still images of the artifacts to generate the 3D model. The derived model is exported as a 3D VRML model of the textile for visual representation and haptic rendering. An overview of the approach is provided and the developed algorithm is described. The approach is validated by applying the algorithm to different textile samples and comparing the produced models with the actual structure and pattern of the samples.
The first stage in the development of a clinically valid surgical simulator for training otologic surgeons in performing
cochlea implantation is presented. For this purpose, a geometric model of the temporal bone has been derived from a
cadaver specimen using the biomedical image processing software package Analyze (AnalyzeDirect, Inc) and its
three-dimensional reconstruction is examined. Simulator construction begins with registration and processing of a
Computer Tomography (CT) medical image sequence. Important anatomical structures of the middle and inner ear are
identified and segmented from each scan in a semi-automated threshold-based approach. Linear interpolation between
image slices produces a three-dimensional volume dataset: the geometrical model. Artefacts are effectively eliminated
using a semi-automatic seeded region-growing algorithm and unnecessary bony structures are removed. Once validated
by an Ear, Nose and Throat (ENT) specialist, the model may be imported into the Reachin Application Programming
Interface (API) (Reachin Technologies AB) for visual and haptic rendering associated with a virtual mastoidectomy.
Interaction with the model is realized with haptics interfacing, providing the user with accurate torque and force
feedback. Electrode array insertion into the cochlea will be introduced in the final stage of design.
The latest trend in computer assisted mammogram analysis is reviewed and two new methods developed by the authors for automatic detection of microcalcifications (MCs) are presented. The first method is based on wavelet neurone feature detectors and ART classifiers while the second method utilized fuzzy rules for detection and grading of MCs.
At present there are approximately 110 million land-mines scattered around the world in 64 countries. The clearance of these mines takes place manually. Unfortunately, on average for every 5000 mines cleared one mine clearer is killed. A Mine Detector Arm (MDA) using mechatronics approach is under development in this work. The robot arm imitates manual hand- prodding technique for mine detection. It inserts a bayonet into the soil and models the dynamics of the manipulator and environment parameters, such as stiffness variation in the soil to control the impact caused by contacting a stiff object. An explicit impact control scheme is applied as the main control scheme, while two different intelligent control methods are designed to deal with uncertainties and varying environmental parameters. Firstly, a neuro-fuzzy adaptive gain controller (NFAGC) is designed to adapt the force gain control according to the estimated environment stiffness. Then, an adaptive neuro-fuzzy plus PID controller is employed to switch from a conventional PID controller to neuro-fuzzy impact control (NFIC), when an impact is detected. The developed control schemes are validated through computer simulation and experimental work.
The research presented in this study primarily proposes a simple but efficient method for the generation of depth maps for a certain class of industrial parts. The suggested technique is based on physical principle that the light is absorbed in a color liquid as it travels along an optical path. Consequently the grey level image of an object immersed in a color liquid contains information about the optical path of rays reflected from the object. In other words, the intensity of the grey level image of the object produced in this way is modulated by the depth of the object.
The application of computer vision in industry has been increasing as greater use is made of flexible automation and robotics. Quality control and sorting can also be heavily dependent on artificial vision interfaced to an intelligent decision making system. Traditionally industrial tasks requiring computer vision are simplified to a 2-D problem in a plane. This permits the use of a single camera and hence reduces the complexity of the procedures of frame grabbing, image processing and decision making. Such a solution is however not suitable when 3-D information is vital in the control or decision making processes. Generation and processing of 3-D images are required for such applications.
The work presented in this paper provides a simple method of deriving a 3-D computer model for a special class of industrial objects and then using this model for machine recognition. The object is immersed in a colour liquid and the intensity of the pixels of the captured image is modulated by the depth of the object along the camera axis. The depth maps generated from the image are represented by parallel layers located in planes normal to the camera axis. The 2-D features of the layers are derived and a 3-D model is constructed for the object based on these features.
The object is distinguished by contour groups which are classified into three types according to their features. These 3-D features include object features and contour group features. Three steps are adopted for object recognition. The object features are first used in a basic test in order to reduce the number of possible models which an unknown object can match. Secondly, the contour features are used to test each of the contour group models. The models with a higher match rate are then selected for verification using chi-squared (?2) statistical methods. Finally the ?2 test is employed to verity the above test results. The object match is governed by both the ?2 test and the contour group test. From these tests, a model which best matches the object can be obtained.
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