Detection of corners is an important task in computer vision to capture discontinuous boundaries of objects of interest. Present operators designed to detect boundaries having sharp corners often produce unsatisfactory results because the points detected can also be an isolated point, ending of a thin line or a maximum curvature region of a planar curve. A novel corner detection operator, capable of detecting corner points that exist only on the boundary of an object, is presented in this paper. Initially, candidate corner points are detected by exploiting intensity information of the local neighborhood and associated connectivity pattern around the center point within a local window.  Further verification is done to confirm whether the detected corner point is on the boundary of the targeted object. As the proposed operator is isotropic, it covers all the orientations and corner angles by performing a single computation step within the local window. The performance of the operator is tested with both synthetic and real images and the results are compared with other major corner detectors

Active contours are a form of curves that deforms according to an energy minimising function and are widely used in computer vision and image processing applications to extract features of interests from raw images acquired using an image capturing device. One of the major limitations in active contours is its inability to converge accurately when the object of interest exhibits sharp corners. In this paper, a new technique of active contour model to extract boundaries of objects having sharp corners is presented. By incorporating a priori knowledge of significant corners of the object into the deforming contour, the proposed active contour is able to deform towards the boundaries of the object without surpassing the corners. The ability of the new technique to accurately extract features of interest of anatomical structures in medical X-ray images having sharp corners is demonstrated. 

Applications that involve monitoring of water quality parameters require measuring devices to be placed at different geographical locations but are controlled centrally at a remote site. The measuring devices in such applications need to be small, consume low power, and must be capable of local processing tasks facilitating the mobility to span the measuring area in a vast geographic area. This paper presents the design of a generalized, low-cost and re-configurable, re-programmable smart sensor node using a Zigbee with a Field-Programmable Gate Array (FPGA) that embeds all processing and communication functionalities based on the IEEE 1451 family of standards. Design of the sensor nodes includes communication, processing and transducer control functionalities in a single core increasing the speedup of processing power due to inter-process communication taking place within the chip itself. Results obtained by measuring the pH value and temperature of water samples verify the performance of the proposed sensor node