Salient region detection using contrast-based saliency and watershed segmentation

Salient region detection is useful for many applications such as image segmentation, compression, image retrieval, object tracking, and machine vision systems.In this paper, an approach to detect salient regions in a visual scene using contrast-based saliency and watershed segmentation is presented.The approach allows salient objects to be detected and extracted for analysis while preserving the actual boundaries of the salient objects. The approach can be executed in parallel making it efficient for real time applications

Multi camera visual saliency using image stitching

This paper presents and investigates two models for a multi camera configuration with visual saliency capability. Applications in various imaging fields each have a different set of detection parameters and requirements which would result in the necessity of software changes. The visual saliency capability offered by this multi camera model allows generic detection of conspicuous objects be it human or nonhuman based on simple low level features. As multiple cameras are used, an image stitching technique is employed to allow combination of Field-of-View (FoV) from different camera captures to provide a panoramic detection field. The stitching technique is also used to complement the visual saliency model in this work. In the first model, image stitching is applied to individual captures to provide a wider FoV, whereby the visual saliency algorithm would able to operate on a wide area. For the second model, visual saliency is applied to individual captures. Then, the maps are recombined based on a set of stitching parameters to reinforced salient features present in objects at the FoV overlap regions. Simulations of the two models are conducted and demonstrated for performance evaluation

Efficient Processing of a Rainfall Simulation Watershed on an FPGA-Based Architecture with Fast Access to Neighbourhood Pixels

This paper describes a hardware architecture to implement the watershed algorithm using rainfall simulation. The speed of the architecture is increased by utilizing a multiple memory bank approach to allow parallel access to the neighbourhood pixel values. In a single read cycle, the architecture is able to obtain all five values of the centre and four neighbours for a 4-connectivity watershed transform. The storage requirement of the multiple bank implementation is the same as a single bank implementation by using a graph-based memory bank addressing scheme. The proposed rainfall watershed architecture consists of two parts. The first part performs the arrowing operation and the second part assigns each pixel to its associated catchment basin. The paper describes the architecture datapath and control logic in detail and concludes with an implementation on a Xilinx Spartan-3 FPGA