An Introduction to Object Detection

Digital image processing refers to processing of a two-dimensional picture by digital computer. It implies digital processing of two dimensional data. A digital image is an array of real or complex numbers represented by a finite number of bits. Image segmentation is a key step in digital image processing. It was developed in 1960’s for image analysis. It is the process of grouping together pixels which are semantically linked. Segmentation divides image into its constituent regions or objects. The level to which segmentation is carried out depends upon the problem being solved i.e. segmentation should stop when the objects of interest in an application have been isolated. Segmentation accuracy determines the eventual success or failure of computerized analysis procedures. For this reason considerable care is taken to improve the probability of rugged segmentation . In some situations such as industrial inspection applications, at least some measure of control over the environment is possible at times. In others, as in remote sensing, user control over image acquisition is limited principally to the choice of image sensors.

Image segmentation is a tool used for precise image analysis. An object input image is taken and is preprocessed. Preprocessing is done to convert the image in more suitable form and to remove the noise. Image smoothing and binarizing are the two stages of preprocessing. Various filters such as median filter, spatial average filter, linear filter and Gaussian filters are used for image smoothing. In few cases noise is multiplicative. Noise smoothing filters are also designed for such images. Binarized image has only two levels i.e. black and white and is obtained by thresholding. The next step of image segmentation is feature extraction. Feature extraction generally refers to the extraction of discontinuities such as point, line and edge, and pixels forming homogeneous regions. Such features have difference in gray level when compared to the background area. Region growing is based on similarity criteria. Region growing is an iterative process by which regions are merged starting from individual pixels or initial segmentation and grow iteratively until every pixel is processed. Selection of edge or region depends upon the type of data being analyzed and on the application area.

 Image Analysis Therefore, the final output image is a segmented image in which the features of the objects in foreground are extracted so precisely that they are separated from the background. In human visual system, edges are more sensitive than other picture elements. As a result, if one uses either region-growing or edge detection technique alone, one may lose some information of interested objects. For example, if one uses region-growing technique alone, the lack of edge information would terminate region-growing process at wrong place. If the similarity criteria were too strict, many false edges would be generated. In other words, the region-growing process may not stop at the contour of object. In order to improve segmentation results, combination of region growing and edge detection techniques is a good research issue. The integrated method can exploit the edge information obtained by using edge detection techniques to help the region growing process determine where and when to stop the growing process. In this way, objects separated could have accurate contour on the true edges. Edge based and region based approaches are complementary to each other and use ancillary information to guide the image segmentation procedure. The early researchers used the edge information to check the boundary produced by performing region growing process on the raw input image. 

Any region growing technique may produce false boundaries because the uniformity criterion may not be satisfied over a given area even if there is no clear line where a transition occurs. Furthermore, it is likely that such boundaries will reflect the data structures and traversal strategies used during region growing.
 The application of any region growing process can lead to three kinds of errors:
 a) A boundary is not an edge and there are no edges nearby.
b) A boundary corresponds to an edge but it does not coincide with it. 
c) There exist edges with no boundaries near them. 

The probability of third type errors mentioned above can be reduced significantly, if not eliminated altogether, by the proper selection of parameters. This results in an over segmented image because such parameter settings cause the errors of first type to increase. In order to achieve a meaningful segmentation, low level features must be extracted first and subsequently linked together using a series of opportunistic grouping algorithms. At the lowest level the only information is similarity. The main goal of segmentation scheme presented is to combine edge and region information to achieve a stable segmentation. The segmentation scheme presented is designed to operate on general home and stock photographs. It returns comprehensive region based description of the visual content of an image. This segmentation scheme is designed to facilitate image retrieval and has been tested on several images and has been found to be robust, rapid and free of tuning parameters. The background noise is removed and reliability and accuracy of image segmentation is increased. It offers precise segmentation in detecting multiple objects of different sizes and non rigid targets. It improves static image segmentation and the computational load is low. Stable segmentation of satellite images is achieved by this process.