Small object detection via fast discrete Curvelet transform

Abstract

The detection of distant objects in an image is relevant to applications in defense, security, and robotics. Successfully detecting objects of interest has been a common problem with respect to intelligent computer vision and has been studied quite thoroughly. Automatic target recognition (ATR) systems have been formulated and employed in numerous ways to tackle the difficulty of unsupervised targeting [1]. We attempt to describe a new approach in the selection of a potential target area or region of interest (ROI) in an image. A region of interest from a particular image is simply a subset of an image whose size is dependent upon the user or is chosen by an adaptive process. Each ROI should contain an object of interest or information that is pertinent to the user or system. Thus, the goal of our framework is reducing the amount of ROIs without the unintended loss of an object of interest. We implement the Fast Discrete Curvelet Transform (FDCT) as the first stage in this system’s framework in order to have a collection of sufficient Curvelet coefficients. The Curvelet coefficients sparsify the input image and by this sparsification, we are able to locate and extract ROIs. Once the ROIs have been extracted, they are then passed to the second stage of our framework which is the classification stage. In the classification stage it is verified whether or not each ROI has an object of interest. After an image has been processed by this framework, the output should be the successful detection of meaningful objects or targets contained in the image. We shall first introduce our previous work and give a brief explanation of sparse representation. Once the concept of sparsity is understood, we will then commence with the summarization of the Curvelet transform. Having established these concepts, the new architecture of our system will be presented, followed by the experimentation and results.