Phongsuphap Sukanya

A new operator for image structure analysis

In this paper, we propose a new operator called shape operator for describing image structure. We consider an image function as a surface, then describe a shape of each pixel comparing with its neighbourhood in terms of topographical shapes such as hill, dale, ridge, valley, etc. The shape operator is established by utilizing the eigenvalues of Hessian of an image function. We show how to derive this operator, and its interesting properties. Finally, we illustrate by examples how the shape operator can give the same interpretation of an image although the image is corrupted by shading effects, and how it can give the same interpretation of an image viewed in different viewpoints.

Image classification using the surface-shape operator and multiscale features

This paper introduces new features for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the topographic structure described by the surface-shape operator. Then, illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the multiresolution simultaneous autoregressive features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures (1966).

A new operator for describing topographical image structure

To develop a computer vision system, it is necessary to define a proper image structure representation to be used for interpreting images efficiently. In this paper, we propose a new operator called shape operator for describing topographical image structure. We consider an image function as a surface, then describe a shape of each pixel comparing with its neighbourhood in terms of topographical shapes such as hill, dale, ridge, valley, etc. The shape operator is established by utilizing the eigenvalue of Hessian of an image function. It is expressed in an explicit form in terms of the second order partial derivatives of an image function. We show how to derive this operator, its interesting properties, and an application for texture classification. Experimental results show its good performance for discriminating texture imaged. Especially, it can give the same interpretation of an image reflected in different lighting conditions since it has the invariance properties under linear and monotonic gray tone transformations.

New operator for determining surface shapes

In this paper, we propose a new operator called the Surface- Shape operator (SS-operator) for determining local surface shape, and use it for analyzing image structure. We consider an image function as a bivariate surface, then describe a shape of each pixel comparing with its neighborhood. In a geometry viewpoint, types of surface shapes are elliptic, hyperbolic, parabolic, etc. Here, we label them based on topographical structure and used a statistic approach to measure spatial distribution of these shapes overall the surface. The Surface-Shape operator is established by utilizing the eigenvalues of Hessian of an image function. We show how to derive this operator and its interesting properties, and give examples to demonstrate its usefulness in practical uses. Finally, we illustrate its roles for describing texture images by using co-occurrence matrices, a statistical measure, to represent its numerical description, and show its good performance for suppressing shading effects as well.

The surface-shape operator and its application to image retrieval

This paper proposes an operator called the surface-shape sperator for describing image structures. By considering an image function as a surface, the surface-shape operator is established for describing the shape of each pixel compared with its neighbourhood, In this paper,the surface-shape operator is used as a pre-processing for describing the multiscale topographic structures of images in scale-space representation. Then, image features are extracted from the transformed images instead of the original ones. The surface-shape operator has invariant properties under linear and monotonic gray tone transformations, and is insensitive to additive noises modelled by linear functions, so it has robustness with brightness variations and shading effects. Finally, we show the usefulness of the proposed approach with the image retrieval application. By comparing the Wold features, the multiresolution simultaneous autoregressive features and the proposed features, the proposed features can provide the best pattern retrieval accuracy.

Coherency between kinesthetic and visual sensation for two-handed-input in a virtual environment

This paper is about constructing a virtual work space for performing any tasks by both hands manipulation. We intend to provide a virtual enviornment that can encourage users to accomplish any tasks as they usally act in the real environment. Our approach is using a three dimensional spatial interface device that allows the user to handle virtual objects directly by free hands and be feel-able some physical properties of the virtual objects such as contact, weight, etc. We have investigated the suitable conditions for constructing our virtual work space by simulating some basic assembly work, a Face-and-Fit task. Then select the conditions that the subjects feel most comfortable in performing this task to set up our virtual work space. Finally, we have verified the possibility to perform more complex tasks in virtual work space by providing some simple virtual models then let subjects create new models by assembling these component models together. The subjects can naturally perform assembly operations and assomplish the task. Our evoluation shows that this virtual work space has potential to be used for performing any tasks that need hands manipulation or cooperation between both hands in natural manner.