Image segmentation has a crucial role in image processing. Classical segmentation techniques based on thresholding have been extensively used but they fail drastically for noisy or non-uniformly illuminated images. Several alternatives presented over the time have filled this void but with increased complexity. In this paper we present an algorithm to address the above issues with minimum complexity. We propose normalized self correlation function (NSCF) which forms a basis for the progress of the algorithm. We also introduce relative error function (REF) which is used for qualitative assessment of the algorithm and its comparison with other algorithms. We also propose a second algorithm named piecewise image segmentation (PIS) which is a generalized edge-based method able to generate any desired edge map. The results show that the proposed algorithms are able to perform well for different scenarios and at the same time better than traditional algorithms. 

A parametric approach algorithm based on maximum likelihood estimation (MLE) method is proposed which can be exploited for high-resolution parameter estimation in the domain of signal processing applications. The array signal model turns out to be a superposition of two-dimensional sinusoids with the first component of each frequency doublet corresponding to the direction of the target and second component to the velocity. Numerical simulations are presented to illustrate the validity of the proposed algorithm and its various aspects. Also, the presented algorithm is compared with a subspace based technique, multiple signal classification (MUSIC) to highlight the key differences in performance under different circumstances. It is observed that the developed algorithm has satisfactory performance and is able to determine the direction of arrival (DOA) as well as the velocity of multiple moving targets and at the same time it performs better than MUSIC under correlated noise.