Ming-Jung Seow

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This paper describes a face detection and recognition system in color image sequences with a novel scheme to model skin color in the RGB color-space using neural networks. In our approach, there are no limitations regarding human skin color. This method eliminates the difficulty of describing non-skin samples by approximating non-skin color from skin(More)
We propose a linear attractor network based on the observation that similar patterns form a pipeline in the state space, which can be used for pattern association. To model the pipeline in the state space, we present a learning algorithm using a recurrent neural network. A least-squares estimation approach utilizing the interdependency between neurons(More)
Homomorphic filter is an illumination-reflectance model that can be used to develop a frequency domain procedure for improving the appearance of an image by simultaneous gray-level range compression and contrast enhancement. Many previously reported methods on homomorphic filter for color images shows that the homomorphic filter consistently provides(More)
A robust and efficient image enhancement technique has been developed to improve the visual quality of digital images that exhibit dark shadows due to the limited dynamic ranges of imaging and display devices which are incapable of handling high dynamic range scenes. The proposed technique processes images in two separate steps: dynamic range compression(More)
A method to embed N dimensional, multi-valued patterns into an auto-associative memory represented as a nonlinear line of attraction in a fully connected recurrent neural network is presented in this paper. The curvature of the nonlinear attractor is defined by the Kth degree polynomial line which best fits the training data in N dimensional state space.(More)