Real-time digital image stabilization based on regional field image gray projection

Digital image stabilization technique plays important roles in video surveillance and object acquisition.Many useful electronic image stabilization algorithms have been studied.A real-time algorithm is proposed based on field image gray projection which enables the regional odd and even field image to be projected into x and y directions and thus to get the regional gray projection curves in x and y directions,respectively.For the odd field image channel,motion parameters can be estimated via iterative minimum absolute difference based on two successive field image regional gray projection curves.Then motion compensations can be obtained after using the Kalman filter method.Finally,the odd field image is adjusted according to the compensations.In the mean time,motion compensation is applied to the even field image channel with the odd field image gray projection curves of the current frame.By minimizing absolute difference between odd and even field image gray projection curves of the current frame,the inter-field motion parameters can be estimated.Therefore,the even field image can be adjusted by combining the inter-field motion parameters and the odd field compensations.Finally,the stabilized image sequence can be obtained by synthesizing the adjusted odd and even field images.Experimental results show that the proposed algorithm can run in real-time and have a good stabilization performance.In addition,image blurring can be improved.

Local information enhanced LBP

Based on the observation that there exists multiple information in a pixel neighbor,such as distance sum and gray difference sum,local information enhanced LBP(local binary pattern)approach,i.e.LE-LBP,is presented.Geometric information of the pixel neighborhood is used to compute minimum distance sum.Gray variation information is used to compute gray difference sum.Then,both the minimum distance sum and the gray difference sum are used to build a feature space.Feature spectrum of the image is computed on the feature space.Histogram computed from the feature spectrum is used to characterize the image.Compared with LBP,rotation invariant LBP,uniform LBP and LBP with local contrast,it is found that the feature spectrum image from LE-LBP contains more details,however,the feature vector is more discriminative.The retrieval precision of the system using LE-LBP is91.8%when recall is 10%for bus images.

Modified Gray Level Difference-Based Thresholding Segmentation and its Application in X-Ray Welding Image

Thresholding is a popular image segmentation method that often requires as a preliminary and indis- pensable stage in the computer aided image process, particularly in the analysis of X-ray welding images. In this paper, a modified gray level difference-based transition region extraction and thresholding algorithm is presented for segmentation of the images that have been corrupted by intensity inhomogeneities or noise. Classical gray level difference algorithm is improved by selective output of the result of the maximum or the minimum of the gray level with the pixels in the surrounding, and multi-structuring of neighborhood window is used to represent the essence of transition region. The proposed algorithm could robustly measure the gray level changes, and accurately extract transition region of an image. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. K

Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].

Cellular automata-based approach for digital image scrambling

Purpose–The purpose of this paper is to investigate two-dimensional outer totalistic cellular automata(2D-OTCA)rules other than the Game of Life rule for image scrambling.This paper presents a digital image scrambling(DIS)technique based on 2D-OTCA for improving the scrambling degree.The comparison of scrambling performance and computational effort of proposed technique with existing CA-based image scrambling techniques is also presented.Design/methodology/approach–In this paper,a DIS technique based on 2D-OTCA with von Neumann neighborhood(NvN)is proposed.Effect of three important cellular automata(CA)parameters on gray difference degree(GDD)is analyzed:first the OTCA rules,afterwards two different boundary conditions and finally the number of CA generations(k)are tested.The authors selected a random sample of gray-scale images from the Berkeley Segmentation Data set and Benchmark,BSDS300(www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)for the experiments.Initially,the CA is setup with a random initial configuration and the GDD is computed by testing all OTCA rules,one by one,for CA generations ranging from 1 to 10.A subset of these tested rules produces high GDD values and shows positive correlation with the k values.Subsequently,this sample of rules is used with different boundary conditions and applied to the sample image data set to analyze the effect of these boundary conditions on GDD.Finally,in order to compare the scrambling performance of the proposed technique with the existing CA-based image scrambling techniques,the authors use same initial CA configuration,number of CA generations,k紏10,periodic boundary conditions and the same test images.Findings–The experimental results are evaluated and analyzed using GDD parameter and then compared with existing techniques.The technique results in better GDD values with 2D-OTCA rule 171 when compared with existing techniques.The CPU running time of the proposed algorithm is also considerably small as compared to existing techniques.Originality/value–In this paper,the authors focused on using von Neumann neighborhood(NvN)to evolve the CA for image scrambling.The use of NvN reduced the computational effort on one hand,and reduced the CA rule space to 1,024 as compared to about 2.62 lakh rule space available with Moore neighborhood(NM)on the other.The results of this paper are based on original analysis of the proposed work.