Detection of small targets and their characterization based on their formation using an image feature network-based object recognition algorithm
暂无分享,去创建一个
[1] Jeremy Straub. Detection of obscured and partially covered objects using partial network matching and an image feature network-based object recognition algorithm , 2014, Defense + Security Symposium.
[2] S. P. Shinde,et al. IMPLEMENTATION OF PATTERN RECOGNITION TECHNIQUES AND OVERVIEW OF ITS APPLICATIONS IN VARIOUS AREAS OF ARTIFICIAL INTELLIGENCE , 2011 .
[3] Christophe Cruz,et al. From 3D Point Clouds to Semantic Objects - An Ontology-based Detection Approach , 2011, KEOD.
[4] Luo Juan,et al. A comparison of SIFT, PCA-SIFT and SURF , 2009 .
[5] Matthijs C. Dorst. Distinctive Image Features from Scale-Invariant Keypoints , 2011 .
[6] Nicholas Roy,et al. Indoor scene recognition by a mobile robot through adaptive object detection , 2013, Robotics Auton. Syst..
[7] Andrew Zisserman,et al. Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[8] Tomaso Poggio,et al. Models of object recognition , 2000, Nature Neuroscience.
[9] Stephen Gould,et al. Discriminative Learning with Latent Variables for Cluttered Indoor Scene Understanding , 2010, ECCV.
[10] Sai Ravela,et al. Deformation invariant image matching by spectrally controlled diffeomorphic alignment , 2009, 2009 IEEE 12th International Conference on Computer Vision.