Image processing has certainly brought visual world closer to programming world. In imaging science, image processing is form of signal processing for which the input is an image, such as a photograph or video; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.
Image processing usually refers to digital image processing, but optical and analog image processing also are possible. The acquisition of images (producing the input image in the first place) is referred to as imaging. Here we will be particulary interested in Digital Image Processing. Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing.
So we have selected this particualar project to detect salient object in a static image. On future scope the project can be extended to detect salient object on video as well. The abstract for the project is mentioned below to provide you with some input regarding the project.
DETECTION OF SALIENT OBJECT IN A STATIC IMAGE
The selection of project was to use the human visual attention concept to maximum benefit in the domain of image processing and thus extract important or the most prominent object in a static image. The research uptil now in this area has been limited to indicating just the Focus of Attention (FOA) and not the entire region of interest. Here we deal with the salient object detection problem for static images. We formulate salient object detection as one which separates a salient object from the background. We propose a bottom up approach to compute the saliency map analogous to one calculated by human brain. The mathematical model is prepared, based on study of anatomy of physiology of visual system primates. We extract low level features based on colour, contrast and intensity .After normalization and linear combination, a master map or a saliency map is computed to represent the saliency of each image pixel. Finally, the image is segmented out from the background. We manually selected about 30 images, from a large database of images from Microsoft Asia which contains a salient object or a distinctive foreground object.
This blog will contain the chapters in the thesis for the project. For source code and other information do contact me on my mentioned email id.