Local Feature Based Multiple Object Instance Identification Using Scale and Rotation Invariant Implicit Shape Model

• Published in: Computer Vision - ACCV 2014 Workshops pp. 600 - 614
• Main Authors: Bao, Ruihan, Higa, Kyota, Iwamoto, Kota
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• Subjects: Feature Match; Local Descriptor; Local Feature; Object Center; Query Image
• ISBN: 9783319166278; 3319166271
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-16628-5_43

In this paper, we propose a Scale and Rotation Invariant Implicit Shape Model (SRIISM), and develop a local feature matching based system using the model to accurately locate and identify large numbers of object instances in an image. Due to repeated instances and cluttered background, conventional methods for multiple object instance identification suffer from poor identification results. In the proposed SRIISM, we model the joint distribution of object centers, scale, and orientation computed from local feature matches in Hough voting, which is not only invariant to scale changes and rotation of objects, but also robust to false feature matches. In the multiple object instance identification system using SRIISM, we apply a fast 4D bin search method in Hough space with complexity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n)$$\end{document}, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} is the number of feature matches, in order to segment and locate each instance. Furthermore, we apply maximum likelihood estimation (MLE) for accurate object pose detection. In the evaluation, we created datasets simulating various industrial applications such as pick-and-place and inventory management. Experiment results on the datasets show that our method outperforms conventional methods in both accuracy (5 %–30 % gain) and speed (2x speed up).