According to the given source,this is a fixed system which uses a frame grabber as input and employs color, geometry and surface features to identify all pills that a given machine may process. Prior knowledge is used to simplify the problem which gives better results. Ubiquitous mobile devices are used for various tasks in computer vision. Wagner et al. perform robust 6DOF natural feature tracking using modified SIFT and Ferns as descriptors [11]. In order to allow computation on mobile devices, extensive modifications are carried out to the basic concepts of SIFTand Ferns,followed by a instructive evaluation of system performance. Despite severe limitations in processing speed and memory bandwidth, they achieve real-time performance when using textured planar targets on current-generation phones. Klein and Murray present a system for parallel tracking and mapping on camera phones [7]. They implement a key-frame based SLAM system that is capable of generating and augmenting small maps. Limited computational resources and problems in image acquisition such as a rolling shutter a respecifically accounted for to allow computation on mobile devices (iPhone 3G). In the system at hand robust estimation of object features from a single input image must be performed. Due to the nature of the problem, it seems justified to run through a separated segmentation, feature extraction and classification step (depending on the feature), in which tasks may be optimised independently. For each of the steps needed in our application, a vast amount of literature is available, but self-contained work on how to perform these steps efficiently on mobile devices is not available. Thus, we propose a series of solutions which make the problem computable on current mobile phones in instant time.