This paper proposes an increased modularity created contour detection algorithm. Given an over segmented image that entails of many small regions, our algorithm automatically combines those neighboring regions that produce the largest increase in modularity index. When the modularity of the segmented image is increased, the method stops merging and produces the final segmented image. To preserve the repetitive patterns in a homogeneous region, we propose a feature on the basis of the histogram of states of image gradients and use it together with the color feature to characterize the similarity of two regions. By building the similarity matrix in an adaptive manner, the over segmentation problem can be successfully avoided.

ABSTRACThere this work is introducing the new technique using the improved texture enhanced framework for image denoising. This technique is fast as compared to the higher order singular value decomposition (HOSVD) as we have in the previous work. The HOSVD technique simply compose in a cluster, alike Patches of noisy image in 3D heap, work out HOSVD factors of this heap, handles these factors by stiff thresholding, and turn upside down the HOSVD transmute to yield the final resultant image. Whereas improved texture enhanced image denoising have proven to be effective and robust in many image denoising tasks. It is experimentally demonstrating approximately 5 percent improved PSNR characteristics of ITEID technique on gray scale images. The ITEID process yields state-of-the-art outcomes on gray images, than HOSVD image data denoising process at moderately great noise stages.

This paper compares the basic contour detection algorithms. A contour detection algorithm which jointly tracks at two scales small pieces of edges called edgelets. This multiscaleedgelet structure naturally embeds semi-local information and is the basic element of the recursive Bayesian modeling. The underlying model is estimated using a sequential Monte Carlo approach, and the soft contour detection map is retrieved from the approximated trajectory distribution. The winding number constrained contour detection (WNCCD) is an energy minimization framework based on winding number constraints. In this framework, both region cues, such as color/texture homogeneity, and contour cues, such as local contrast and continuity, are represented in a joint objective function, which has both region and contour labels. This technique is based on the topological concept of winding number. Using a fast method for winding number computation, a small number of linear constraints are derived to ensure label consistency. Experiments conducted on the Berkeley Segmentation data sets show that the Multi Scale Particle Filter Contour Detector method performs a comparable result with the winding number constrained contour detection method.

In this work, there is a comparison related to image denoising techniques between center pixel weights (CPW) in Non-Local Means (NLM) and smart patch-based, modern technique using the higher order singular value decomposition (HOSVD). The HOSVD technique simply compose in a cluster, alike Patches of noisy image in 3D heap, work out HOSVD factors of this heap, handles these factors by stiff thresholding, and turn upside down the HOSVD transmute to yield the final resultant image. Whereas (NLM) and its variants have proven to be effective and robust in many image denoising tasks. It is experimentally demonstrating approximately 12 percent improved PSNR characteristics of HOSVD technique on gray scale images. The HOSVD process yields state-of-the-art outcomes on gray images, than the center pixel weights (CPW) in NLM image data denoising process at moderately great noise stages.