Chathura De Silva | University of Moratuwa (original) (raw)
Papers by Chathura De Silva
2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 2014
In this paper we present work in progress on SiFEB, a simple, interactive and extensible robot pl... more In this paper we present work in progress on SiFEB, a simple, interactive and extensible robot playmate for kids intended to nurture the engagement in STEM (Science, Technology, Engineering and Mathematics) related activities through playing. SiFEB's simplified and interactive hardware and software modules present users without prior experience, with the ability to build robots without going deep in either aspect. Kids can build robots of their interest using attachable hardware modules and related programming components using an easy to understand visual programming language that resembles natural commands. The potential developers may utilize the framework provided, to build hardware and software components to the system expanding the existing capabilities. Preliminary results from the prototypes show that SiFEB has the potential of being an effective learning platform for young children in STEM related areas.
According to psychologists there are six types of universal facial expressions namely, "fear", "s... more According to psychologists there are six types of universal facial expressions namely, "fear", "surprise", "anger", "sad", "disgust" and "happy". Holistic recognition of these facial expressions from static images requires nonlinear classifiers capable of operating on noisy high-dimensional feature spaces. Often radial basis function networks (RBFN) are used for classification in these applications. Conventional RBF networks however, in spite of their capabilities in working with high-dimensional feature spaces, often fail to deliver satisfactory performance in these scenarios due to small training sample sets, noisy features and/or features not following the required class structure. This paper presents an improved RBFN architecture that overcomes these problems through asymmetrical scaling of feature axes according to specific requirements of the class structure of the classification problem. The scaling factors are computed automatically from the available training samples, without any explicit analysis of their multivariate statistical properties. The proposed network yielded an overall recognition rate of over 92% for the 6 expression classes, and a smaller network size compared to other types of RBFN classifiers.
Pattern Recognition, 2008
A new edge detection scheme based on radial basis function networks is proposed. It is a two-tier... more A new edge detection scheme based on radial basis function networks is proposed. It is a two-tiered scheme where, in the first stage, each pixel in the input image is classified according to its potential for being part of an edge. The second stage then combines these pixels into true edges in the input image. Both stages use radial basis function networks. The scheme illustrates how the input space of edge patterns can be used to train the neural network with a minimum number of parameters. Compared with other neural network paradigms, the proposed scheme is simpler in terms of network size and computational requirements, and provides better results even in low-contrast images
Abstract This paper presents a new algorithm for Image Registration and Stitching. The algorithm ... more Abstract This paper presents a new algorithm for Image Registration and Stitching. The algorithm is designed to be extremely efficient and fast in its execution and is intended for use in stitching images extracted from a video stream of a camera. This algorithm is not ...
2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 2014
In this paper we present work in progress on SiFEB, a simple, interactive and extensible robot pl... more In this paper we present work in progress on SiFEB, a simple, interactive and extensible robot playmate for kids intended to nurture the engagement in STEM (Science, Technology, Engineering and Mathematics) related activities through playing. SiFEB's simplified and interactive hardware and software modules present users without prior experience, with the ability to build robots without going deep in either aspect. Kids can build robots of their interest using attachable hardware modules and related programming components using an easy to understand visual programming language that resembles natural commands. The potential developers may utilize the framework provided, to build hardware and software components to the system expanding the existing capabilities. Preliminary results from the prototypes show that SiFEB has the potential of being an effective learning platform for young children in STEM related areas.
According to psychologists there are six types of universal facial expressions namely, "fear", "s... more According to psychologists there are six types of universal facial expressions namely, "fear", "surprise", "anger", "sad", "disgust" and "happy". Holistic recognition of these facial expressions from static images requires nonlinear classifiers capable of operating on noisy high-dimensional feature spaces. Often radial basis function networks (RBFN) are used for classification in these applications. Conventional RBF networks however, in spite of their capabilities in working with high-dimensional feature spaces, often fail to deliver satisfactory performance in these scenarios due to small training sample sets, noisy features and/or features not following the required class structure. This paper presents an improved RBFN architecture that overcomes these problems through asymmetrical scaling of feature axes according to specific requirements of the class structure of the classification problem. The scaling factors are computed automatically from the available training samples, without any explicit analysis of their multivariate statistical properties. The proposed network yielded an overall recognition rate of over 92% for the 6 expression classes, and a smaller network size compared to other types of RBFN classifiers.
Pattern Recognition, 2008
A new edge detection scheme based on radial basis function networks is proposed. It is a two-tier... more A new edge detection scheme based on radial basis function networks is proposed. It is a two-tiered scheme where, in the first stage, each pixel in the input image is classified according to its potential for being part of an edge. The second stage then combines these pixels into true edges in the input image. Both stages use radial basis function networks. The scheme illustrates how the input space of edge patterns can be used to train the neural network with a minimum number of parameters. Compared with other neural network paradigms, the proposed scheme is simpler in terms of network size and computational requirements, and provides better results even in low-contrast images
Abstract This paper presents a new algorithm for Image Registration and Stitching. The algorithm ... more Abstract This paper presents a new algorithm for Image Registration and Stitching. The algorithm is designed to be extremely efficient and fast in its execution and is intended for use in stitching images extracted from a video stream of a camera. This algorithm is not ...