@inproceedings{2b066856e72a42e594599abaa60a32e9,
title = "Visual object detection using deformable sparse coding model",
abstract = "Object detection is a challenging task in the field of pattern recognition. The objective of object detection is to locate the target objects in the testing images. In this paper, we use SVM trained active basis model as a sparse coding model for representing objects. The sparse coding model represents each image as the linear superposition of a small number of Gabor wavelets selected from an over-complete Gabor dictionary. We divide the learned template into several parts, and allow each part and each Gabor wavelet to locally shift to account for shape deformation in detection process. The model is trained from the roughly aligned images. The detection is achieved by sliding windows from a multi-scale image pyramid. The experiment shows a good performance of the detection method in some testing images.",
keywords = "Cat detection, Computer vision, Deformable template, Sparse coding model, Support vector machine",
author = "Xueyan Mei",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 ; Conference date: 23-12-2016 Through 25-12-2016",
year = "2017",
month = jun,
day = "15",
doi = "10.1109/PIC.2016.7949489",
language = "English",
series = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "174--178",
editor = "Yinglin Wang and Yaoru Sun",
booktitle = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
address = "United States",
}