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基本矩陣運算 : + - *, power, transpose, trace, determinant, minor, matrix of minor, cofactor, matrix of co

基本矩陣運算 : + - *, power, transpose, trace, determinant, minor, matrix of minor, cofactor, matrix of cofactor, adjoint, inverse, gauss, gaussjordan, linear transformation, LU decomposition , Gram-Schmidt process, Similarity. b) Basic vectors functions : norm, distance, innerproduct,coldim, rowdim, rank, nullity. *

標簽： matrix minor determinant transpose

上傳時間： 2013-12-09

上傳用戶：541657925
FISMAT accommodates different arithmetic operators, fuzzification and defuzzification algorithm, imp

FISMAT accommodates different arithmetic operators, fuzzification and defuzzification algorithm, implication relations, and different method of approximate reasoning such as Compositional Rule of Inference (CRI) and Approximate Analogical Reasoning Scheme based on Similarity Measure.

標簽： defuzzification fuzzification accommodates arithmetic

上傳時間： 2013-12-30

上傳用戶：dongbaobao
This paper introduces an affine invariant of trapezia, and the explicit constraint equation between

This paper introduces an affine invariant of trapezia, and the explicit constraint equation between the intrinsic matrix of a camera and the Similarity invariants of a trapezium are established using the affine invariant. By this constraint, the inner parameters, motion parameters of the cameras and the Similarity invariants of trapezia can be linearly determined using some prior knowledge on the cameras or the trapezia. The proposed algorithms have wide applicability since parallel lines are not rare in many scenes. Experimental results validate the proposed approaches. This work presents a unifying framework based on the parallelism constraint, and the previous methods based on the parallelograms or the parallelepipeds can be integrated into this framework. Key words: invariant parallelism constraint camera calibration 3D reconstruction

標簽： introduces constraint invariant explicit

上傳時間： 2014-01-16

上傳用戶：6546544
The last step in training phase is refinement of the clusters found above. Although DynamicClusteri

The last step in training phase is refinement of the clusters found above. Although DynamicClustering counters all the basic k-means disadvantages, setting the intra-cluster Similarity r may require experimentation. Also, a cluster may have a lot in common with another, i.e., sequences assigned to it are as close to it as they are to another cluster. There may also be denser sub-clusters within the larger ones.

標簽： DynamicClusteri refinement Although clusters

上傳時間： 2014-01-04

上傳用戶：watch100
The existence of numerous imaging modalities makes it possible to present different data present in

The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a Similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.

標簽： present modalities existence different

上傳時間： 2017-04-03

上傳用戶：qunquan
-The existence of numerous imaging modalities makes it possible to present different data present in

-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. Mutual Information is the Similarity measure used in this case for optimizing the two images. This method requires estimating joint histogram of the two images. The fusion of images is the process of combining two or more images into a single image retaining important features from each. The Discrete Wavelet Transform (DWT) has become an attractive tool for fusing multimodal images. In this work it has been used to segment the features of the input images to produce a region map. Features of each region are calculated and a region based approach is used to fuse the images in the wavelet domain.

標簽： present modalities existence different

上傳時間： 2014-03-04

上傳用戶：15736969615
A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of

A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole,"[1] a property called self-Similarity. The term was coined by Benoî t Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion.[2]

標簽： fragmented generally geometric fractal

上傳時間： 2014-01-18

上傳用戶：as275944189