Now showing items 1-17 of 17

  • Action recognition via sequence embedding 

    Gong, Wei (University of Missouri--Columbia, 2011)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] A comb structural exemplar embedding based approach is introduced for action recognition. We propose a new framework to represent an action as a weak ...
  • Depth sensor based object detection using surface curvature 

    Liu, Yang (Electrical engineer) (University of Missouri--Columbia, 2014)
    An object detection system finds objects from an image or video sequence of the real world. The good performance of object detection has been largely driven by the development of well-established robust feature sets. By ...
  • A detection based sport player tracker 

    Li, Yan (University of Missouri--Columbia, 2014)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Currently, the sliding windows framework dominates the object detection task for its good performance. But the computational cost of this approach is ...
  • Face recognition via sparse representation 

    Ren, Zhixin (University of Missouri--Columbia, 2015)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Sparse representation has been successfully used for face recognition. The sparse representation classifier (SRC) can solve illumination variations, ...
  • Fast object detection on raw depth images 

    Chen, Guang (University of Missouri--Columbia, 2015)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] We propose an effective object detection system which is able to localize object in real time and outperforms the state-of-the-art results on standard ...
  • Geometric image segmentation via transform invariant rank cuts 

    Abdulhussein, Hussein (University of Missouri--Columbia, 2012)
    This research propose a novel image segmentation algorithm, named as Transform Invariant Rank Cuts (TIRC). Based on salient 3D geometric information of natural scenes. The segmentation algorithm unities an emerging robust ...
  • How zenith angle of cross product affects NVP feature 

    Fan, Haipei (University of Missouri--Columbia, 2014)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recent dramatic advance in visual recognition hardware has made some promising progress. For instance, the impact of the recent emerging low-cost depth ...
  • An image-classification leveraged object detector 

    Sun, Miao (Engineer) (University of Missouri--Columbia, 2014)
  • Investigation and analysis of image classification on large-scale benchmark datasets 

    Yang, Weihuan (University of Missouri--Columbia, 2015)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Image Classification nowadays, which including object recognition and scene classification, remains to be a major challenging task among computer ...
  • Large scale image classification and object detection 

    Sun, Miao (Engineer) (University of Missouri--Columbia, 2016)
    Significant advancement of research on image classification and object detection has been achieved in the past decade. Deep convolutional neural networks have exhibited superior performance in many visual recognition tasks ...
  • A novel method of face verification based on EM algorithm 

    Pan, Ran (University of Missouri--Columbia, 2014)
    In this paper, we implement a novel joint Bayesian method based on the classical Bayesian face recognition method by Baback Moghaddam et al and a creative paper "Bayesian Face Revisited: A Joint Formulation". One face is ...
  • Object detection for big data 

    Chen, Guang (Computer scientist) (University of Missouri--Columbia, 2014)
    We have observed significant advances in object detection over the past few decades and gladly seen the related research has began to contribute to the world: Vehicles could automatically stop before hitting any pedestrian; ...
  • Object detection with Kinect sensor 

    Zhu, Hua (University of Missouri--Columbia, 2014)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.]
  • Object detection with large intra-class variation 

    Chen, Guang (University of Missouri--Columbia, 2011)
    For object detection, the state-of-the-art performance is achieved through supervised learning. The performances of object detectors of this kind are mainly determined by two factors: features and underlying classification ...
  • People re-identification in a camera network 

    Shaaya, Ghadeer (University of Missouri--Columbia, 2012)
    In this research, we present an appearance based method for people re-identification. It consists in the extraction of two types of features related to human appearance, color histograms and SIFT features. Images are ...
  • People re-identification over non-overlapping camera views 

    Liu, Yang (Engineer) (University of Missouri--Columbia, 2013)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Person re-identification is a computer vision task of recognizing an individual from similar background across non-overlapping camera views. In this ...
  • Robust classification with convolutional neural networks 

    Alradad, Muhind (University of Missouri--Columbia, 2015)
    For image classification, I achieved the current state-of-the-art performance by using methods based on Convolutional Neural Networks (CNNs). Face image analysis requires both effective feature extraction and classifier ...