This study introduces a glaucoma screening system which is suitable for both normal eyesight and myopia. For myopia eye, only one clinical indicator is not sufficient. Therefore, cup-to-disc ratio and rim width based on ISNT rule are used as indicators in this screening system. First, image segmentation is performed to obtain optic cup, optic disc and rim width. These information will be used as features in glaucoma classification step. The support vector machine (SVM) is selected as a classifier. According to the results, our proposed method is an efficient system for screening glaucoma.

We present a method for the learning and detection of multiple rigid texture-less 3D objects intended to operate at frame rate speeds for video input. The method is geared for fast and scalable learning and detection by combining tractable extraction of edgelet constellations with library lookup based on rotation- and scale-invariant descriptors. The approach learns object views in real-time, and is generative- enabling more objects to be learnt without the need for re-training. During testing, a random sample of edgelet constellations is tested for the presence of known objects. We perform testing of single and multi-object detection on a 30 objects dataset showing detections of any of them within milliseconds from the object’s visibility. The results show the scalability of the approach and its framerate performance. 1

This paper presents a complete system on a camera phone that integrates a texture less 3D object detector together with in-situ modelling and tracking. The result is a suite intended for AR applications on the move where objects can be captured, tracked and with the automated detection providing a bridge to either initialize tracking or resume it after measurement loss. The object detector training is online and in-situ and benefits from tight integration with the modelling and tracking processes.

This paper presents a system that allows online building of 3D wireframe models through a combination of user interaction and automated methods from a handheld camera-mouse. Crucially, the model being built is used to concurrently compute camera pose, permitting extendable tracking while enabling the user to edit the model interactively. In contrast to other model building methods that are either off-line and/or automated but computationally intensive, the aim here is to have a system that has low computational requirements and that enables the user to define what is relevant (and what is not) at the time the model is being built. OutlinAR hardware is also developed which simply consists of the combination of a camera with a wide field of view lens and a wheeled computer mouse.