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The scale invariant feature transform (SIFT) is a very efficient algorithm to extract and describe distinctive invariant features from images, and usually applied for many image applications such as object recognition, robotic mapping, and navigation. In the SIFT computation, the complexity of the feature description is quite high. Hence, it is desirable to have an efficient VLSI architecture to compute...
The grain size is an important quality index of granular materials. The accurate measurement of it has a guiding significance for technical performances and application of subsequent processing. Currently most of particle detection are done by manual operation such as screening method, which causes many problems as time-consuming measurement and lengthy testing steps. The particle size detection technology...
Multiscale Geometry Analysis consistent with the best features of the image representation, is effective for high dimensional function approximation and can use the image of the geometric regularity. In this article, we will mainly discuss the transformation of the Beam let based on Multiscale Geometry Analysis, formulate its theoretical basis, and by Matlab simulation results show the advantages...
A lot of image registration techniques have been developed with great significance for data analysis in medicine, astrophotography, satellite imaging and few other areas. This work proposes a method for medical image registration using Fast Walsh Hadamard transform. This algorithm registers images of the same or different modalities. Each image bit is lengthened in terms of Fast Walsh Hadamard basis...
Mean shift algorithm has grained great success in object tracking domain due to its ease of implementation, real time response and robust tracking performance, however, the fixed kernel bandwidth may cause tracking failure for size changing objects. A novel object tracking algorithm for FLIR imagery is proposed based on mean shift with adaptive bandwidth. The scale invariant feature transform is employed...
Mammography is the most effective method for the early diagnosis and treatment of breast Cancer diseases. However, data sets collected by image sensors are generally contaminated by noise. This ensures the need for image enhancement to aid interpretation. This paper introduces an efficient enhancement algorithm of digital mammograms based on wavelet analysis and modified mathematical morphology. In...
The paper proposes a 3D reconstruction technique suitable in robot vision applications, based on the correspondence of image features established at a training step and the same image features extracted at the execution step. The result is a Euclidean transform to be used by robot head for reorientation at execution step. A closed form solution is proposed for (R, t) transform to initialize the non-linear...
This paper presents a new method for container auto-landing system using stereo vision. The position estimation of the spreader is very important for improving the operating efficiency of the port. A central problem in estimation of container position is that it is difficult to satisfy both the computation time problem and accuracy at the same time. To resolve this problem, we propose detection of...
A rotation, scaling and cropping invariant watermarking scheme using image normalization is proposed in this paper. Watermark is adaptively embedded in discrete wavelet transform. Robust image feature points against rotation, scaling, noise and JPEG compression are obtained by the affine invariant Harris feature point detector and used to make the Delaunay triangle to estimate the affine transform...
Making the semantic description and automatic semantic annotation of the image which contains rich contents and intuitive expression is a research subject that is challenging. It is a key technology of realizing fast and effective image retrieval and a research focusing on cross media mining. Also it has great application value in various kinds of fields. This paper studies and discusses image media...
In this paper, a novel image forensics method is proposed to detect manual blurred edges from a tampered image. Firstly, the image edges are analyzed by using non-subsampled contourlet transform. Then the differences between the normal edge and the blurred edge are extracted by researching phase congruency and prediction-error image. After that, the features are used to train the SVM, by which the...
Recently, a development of the medical instrument using the vision information is brisk. Especially, extracting the 3-dimension information from 2-dimension image is the one of the major research topics. This paper proposes the method to measure tumor size by the 3-dimension information extraction, the triangulation using the extracted 3-dimension information and the camera geometry. To extract the...
We present a new method for adding furry effects for cartoon characters in images and videos. We synthesize furry stylized textures based on 3D texel structure. Given an image or a video as input, realistic fur texels are generated and mapped onto the region of interest(ROI) which is obtained using the Snake method. The 3D texels are rendered using ray marching algorithm on GPU for fast synthesizing...
Video summarization is an efficient and flexible way to represent video data. In this paper, we use the kernel PCA and clustering based key frame extraction to realize multilevel video representation. In order to remove the redundancy caused by large scene changes, SIFT flow scene alignment is performed on the clustering set of key frames. After alignment, one representative frame is chosen from the...
This paper intends to propose a novel clustering method based on ant colony (AC) algorithm. A new approach called TT-transform based time frequency analysis is used in processing the non-stationary power signal disturbances. The time-time transform is the inverse Fourier transform of S-transform. The proposed model is demonstrated using feature vector from the domain of power signal analysis, yielding...
The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features...
In most parts of the world, the quality of the electrical power has become a major concern for many electricity users especially the industrial customers. To the power utility, all power quality disturbances must be detected, classified and diagnosed accurately so that proper mitigation measures can be implemented. This paper presents the application of the S-transform and support vector machine (SVM)...
Offline fingerprints find immense application in the fields of user authentication and criminal identification. But if an insider or a criminal gets unauthorized access to the printed database of fingerprints at a criminology department, then he might tamper or tear them. This may lead to loss of evidence, which could have been useful at the time of post-detection. We feel that no work has been carried...
Recently, Blind Source Separate (BSS) technique has been extended to digital watermarking field. Slowly Feature Analysis (SFA)-a kind of BSS technique-is a new unsupervised learning algorithm to learn nonlinear functions that extract slowly varying signals out of the input data. It expediently can be used to extract image feature and separate the mixed signals. Making use of the advantages of SFA,...
In this paper, we introduce a face recognition approach based on the contourlet transform and support vector machine, which takes technological advantages of both support vector machine and the contourlet transform for feature extraction. The contributions of this paper include the following aspects: (1) support vector machine is successfully applied to face recognition by using the contourlet transform...
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