Point pattern matchingisanimportantproblem inthefieldsofcomputervision and patternrecognition.In this paper,new algorithms based onirreducible matrix andrelativeinvariantfor matchingtwosets ofpoints withthe same ca...Point pattern matchingisanimportantproblem inthefieldsofcomputervision and patternrecognition.In this paper,new algorithms based onirreducible matrix andrelativeinvariantfor matchingtwosets ofpoints withthe same cardinality are proposed.Theirfundamentalideaistransformingthetwo dimensionalpointsets with n points intothe vectorsin n dimensional space. Considering these vectors as one dimensional point patterns,these new algorithms aim atreducingthe point matching problem to thatofsorting vectorsin n dimensionalspace aslong asthe sensornoise does notalterthe order ofthe elementsinthe vectors.Theoreticalanalysis and simulationresults show thatthe new algorithms are effective .展开更多
The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SI...The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.展开更多
A new concept of characteristic scanning radial (CSR) is proposed for thesegmented image on the basis of two shape-specific points of its shape-objects. Subsequently, twocharacteristic attribute sequences (CAS) of rel...A new concept of characteristic scanning radial (CSR) is proposed for thesegmented image on the basis of two shape-specific points of its shape-objects. Subsequently, twocharacteristic attribute sequences (CAS) of relative distance and relative direction are derived torepresent the spatial orientation relationships among objects of the image. A novel image retrievalalgorithm is presented using these two CASs. The proposed retrieval approach not only satisfies thetransformational invariance, butalso attains the quantitative comparison of matching. Experimentsidentify the effectiveness and efficiency of the algorithm adequately.展开更多
文摘Point pattern matchingisanimportantproblem inthefieldsofcomputervision and patternrecognition.In this paper,new algorithms based onirreducible matrix andrelativeinvariantfor matchingtwosets ofpoints withthe same cardinality are proposed.Theirfundamentalideaistransformingthetwo dimensionalpointsets with n points intothe vectorsin n dimensional space. Considering these vectors as one dimensional point patterns,these new algorithms aim atreducingthe point matching problem to thatofsorting vectorsin n dimensionalspace aslong asthe sensornoise does notalterthe order ofthe elementsinthe vectors.Theoreticalanalysis and simulationresults show thatthe new algorithms are effective .
基金Supported by the National Natural Science Foundation of China (No.61571222)the Natural Science Research Program of Higher Education Jiangsu Province (No.19KJD520005)+1 种基金Qing Lan Project of Jiangsu Province (Su Teacher’s Letter 2021 No.11)Jiangsu Graduate Scientific Research Innovation Program (No.KYCX21_1944)。
文摘The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.
文摘A new concept of characteristic scanning radial (CSR) is proposed for thesegmented image on the basis of two shape-specific points of its shape-objects. Subsequently, twocharacteristic attribute sequences (CAS) of relative distance and relative direction are derived torepresent the spatial orientation relationships among objects of the image. A novel image retrievalalgorithm is presented using these two CASs. The proposed retrieval approach not only satisfies thetransformational invariance, butalso attains the quantitative comparison of matching. Experimentsidentify the effectiveness and efficiency of the algorithm adequately.
