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Q. M. Jonathan Wu

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5 papers

2 author rows

JBHI Journal 2014 Journal Article

A Bayesian Bounded Asymmetric Mixture Model With Segmentation Application

Thanh Minh Nguyen
Q. M. Jonathan Wu
Dibyendu Mukherjee
Hui Zhang

Segmentation of a medical image based on the modeling and estimation of the tissue intensity probability density functions via a Gaussian mixture model has recently received great attention. However, the Gaussian distribution is unbounded and symmetrical around its mean. This study presents a new bounded asymmetric mixture model for analyzing both univariate and multivariate data. The advantage of the proposed model is that it has the flexibility to fit different shapes of observed data such as non-Gaussian, nonsymmetric, and bounded support data. Another advantage is that each component of the proposed model has the ability to model the observed data with different bounded support regions, which is suitable for application on image segmentation. Our method is intuitively appealing, simple, and easy to implement. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood function. Numerical experiments are presented where the proposed model is tested in various images from simulated to real 3- $\hbox{D}$ medical ones.

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JBHI Journal 2014 Journal Article

Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval

Subrahmanyam Murala
Q. M. Jonathan Wu

In this paper, a new image indexing and retrieval algorithm using local mesh patterns are proposed for biomedical image retrieval application. The standard local binary pattern encodes the relationship between the referenced pixel and its surrounding neighbors, whereas the proposed method encodes the relationship among the surrounding neighbors for a given referenced pixel in an image. The possible relationships among the surrounding neighbors are depending on the number of neighbors, P. In addition, the effectiveness of our algorithm is confirmed by combining it with the Gabor transform. To prove the effectiveness of our algorithm, three experiments have been carried out on three different biomedical image databases. Out of which two are meant for computer tomography (CT) and one for magnetic resonance (MR) image retrieval. It is further mentioned that the database considered for three experiments are OASIS-MRI database, NEMA-CT database, and VIA/I-ELCAP database which includes region of interest CT images. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, LBP with Gabor transform, and other spatial and transform domain methods.

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IROS Conference 1999 Conference Paper

A fast two dimensional image based grasp planner

Kevin G. Stanley
Q. M. Jonathan Wu
Ali Jerbi
William A. Gruver

This research concerns a grasp-planning algorithm that is fast and capable of determining grasp points for planar nondegenerate objects. We use a novel representation of the target and a quadtree based sampling scheme to generate a set of candidate grasps which are evaluated using a cost function. This function returns the first acceptable grasp point it finds. The resulting system has an execution time of seconds and is suitable for a large number of planar grasp planning problems.

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ICRA Conference 1999 Conference Paper

Neural Network-Based Vision Guided Robotics

Kevin G. Stanley
Q. M. Jonathan Wu
Ali Jerbi
William A. Gruver

An essential problem of image based visual servoing is evaluating the inverse Jacobian which, relates changes in image features to the change in robot position. Neural networks can learn to approximate the inverse feature Jacobian. In addition, neural networks have been used in dimensionality reduction of image input. We show that it is possible to use neural networks for both feature extraction using compression and for feature Jacobian approximation in the visual servoing problem. In our system, we consider the following feature extraction methods: geometric features, averaging compression, vector quantization, and principal component extraction.

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ICRA Conference 1997 Conference Paper

Modular neural-visual servoing using a neural-fuzzy decision network

Q. M. Jonathan Wu
Kevin G. Stanley

Visual servoing is a growing research area. One of the key problems of feature based visual servoing is calculating the inverse Jacobian, relating change in features to change in robot position. Neural networks can learn to approximate the inverse feature Jacobian. However, the neural network approach can only approximate the feature Jacobian for a small workspace. In order to overcome this problem, we propose using a modular approach, where several networks are trained over a small area. Furthermore, we use a neural-fuzzy counterpropagation network to decide which subspace the robot is currently occupying. The neural fuzzy network provides smoother transitions between subspaces than hard switching. Preliminary results of the system's operation are also presented.

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