Real time non-rigid surface detection based on binary robust independent elementary features

AbstractThe surface deformation detection of an object has been a very popular research project in recent years; in human vision, we can easily detect the location of the target and that scale of the surface rotation, and change of the viewpoint makes the surface deformation, but in a vision of the computer is a challenge. In those backgrounds of questions, we can propose a framework that is the surface deformation, which is based on the detection method of BRIEF to calculate object surface deformation. But BRIEF calculation has some problem that can’t rotate and change character; we also propose a useful calculation method to solve the problem, and the method proved by experiment can overcome the problem, by the way, it's very useful. The average operation time every picture in continuous image is 50∼80ms in 2.5GHz computer, let us look back for some related estimation technology of surface deformation, and there are still a few successful project that is surface deformation detection in the document

Detection of Spectral Signatures in Multispectral MR Images for Classification

[[abstract]]This paper presents a new spectral signature detection approach to magnetic resonance (MR) image classification. It is called constrained energy minimization (CEM) method, which is derived fromthe minimum variance distortionless response in passive sensor array processing. It considers a bank of spectral channels as an array of sensors where each spectral channel represents a sensor and object spectral signature in multispectral MR images are viewed as signals impinging upon the array. The strength of the CEM lies on its ability in detection of spectral signatures of interest without knowing image background. The detected spectral signatures are then used for classification. The CEM makes use of a finite impulse response (FIR) filter to linearly constrain a desired object while minimizing interfering effects caused by other unknown signal sources. Unlike most spatial-based classification techniques, the proposed CEM takes advantage of spectral characteristics to achieve object detection and classification. A series of experiments is conducted and compared with the commonly used-means method for performance evaluation. The results showthat the CEM method is a promising and effective spectral technique for MR image classification

Support vector machine for breast MR image classification

AbstractMR images have been used extensively in clinical trials in recent years because they are harmless to the human body and can obtain detailed information by scanning the same slice with various frequencies and parameters. In this paper, we want to detect the breast tissues within multi-spectral MR images. In the image classification, we apply a support vector machine (SVM) to breast multi-spectral magnetic resonance images to classify the tissues of the breast. In order to verify the feasibility and efficiency of this method, evaluations using classification rate and likelihood ratios are adopted based on manifold assessment and a series of experiments are conducted and compared with the commonly used C-means (CM) for performance evaluation. The results show that the SVM method is a promising and effective spectral technique for MR image classification

A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST and K-means

Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems