Pattern Recognition（PR ）的VOLUME 48, ISSUE 2论文列表

卷期号： Volume 48, Issue 2

发布时间： February 2015

卷期年份： 2015

卷期官网： https://www.sciencedirect.com/journal/pattern-recognition/vol/48/issue/2

Editorial Board

Preface of Special Issue on “Graph-based Processing for Pattern Recognition”

A long trip in the charming world of graphs for Pattern Recognition

On the complexity of submap isomorphism and maximum common submap problems

Efficient subgraph matching using topological node feature constraints

Approximation of graph edit distance based on Hausdorff matching

A quantum Jensen–Shannon graph kernel for unattributed graphs

Treelet kernel incorporating cyclic, stereo and inter pattern information in chemoinformatics

Graph-based point drift: Graph centrality on the registration of point-sets

Exact solution to median surface problem using 3D graph search and application to parameter space exploration

An entropy-based persistence barcode

ECDS: An effective shape signature using electrical charge distribution on the shape

Randomized circle detection with isophotes curvature analysis

Determining shape and motion from monocular camera: A direct approach using normal flows

Unsupervised feature selection by regularized self-representation

Effective texture classification by texton encoding induced statistical features

Secure biometric template generation for multi-factor authentication

Noisy and incomplete fingerprint classification using local ridge distribution models

Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains

Learning descriptive visual representation for image classification and annotation

Fast computation of separable two-dimensional discrete invariant moments for image classification

Video summarization via minimum sparse reconstruction

A Dempster–Shafer Theory based combination of handwriting recognition systems with multiple rejection strategies

Efficient segmentation-free keyword spotting in historical document collections

Accurate 3D action recognition using learning on the Grassmann manifold

Trajectory-based human action segmentation

Multi-target tracking by learning local-to-global trajectory models

Quantification-oriented learning based on reliable classifiers

Noise-robust semi-supervised learning via fast sparse coding