Srimal Jayawardena

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The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, and actuators, as well as other instruments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen a plethora of IoT solutions making their way into the industry(More)
The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions(More)
The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods. It does not require prior training, knowledge of the camera parameters, explicit point(More)
The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for process control work in a manufacturing plant and identifying parts of a car from a photo for automatic damage(More)
The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods: It does neither require prior training nor(More)
The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods: It does neither require prior training nor(More)
We explore an approach to use simple classification models to solve complex problems by partitioning the input domain into smaller regions that are more amenable to the classifier. For this purpose weinvestigate two variants of partitioning based on energy, as measured by the variance. We argue that restricting the energy of the input domain limits the(More)
Computer vision techniques such as Structurefrom- Motion (SfM) and object recognition tend to fail on scenes with highly reflective objects because the reflections behave differently to the true geometry of the scene. Such image sequences may be treated as two layers superimposed over each other - the nonreflection scene source layer and the reflection(More)
Common Structure from Motion (SfM) tasks require reliable point correspondences in images taken from different views to subsequently estimate model parameters which describe the 3D scene geometry. For example when estimating the fundamental matrix from point correspondences using RANSAC. The amount of noise in the point correspondences drastically affect(More)
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