Advanced feature point transformation of corner points for mobile object recognition

• Published in: Multimedia tools and applications Vol. 74; no. 16; pp. 6541 - 6556
• Main Authors: Yin, Xiyuan, Kim, Dae-Hwan, Hong, Chung-Pyo, Kim, Cheong-Ghil, Kim, Kuinam J., Kim, Shin-Dug
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2015; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Analysis; Cameras; Color; Computation; Computer Communication Networks; Computer Science; Corners; Data Structures and Information Theory; Feature extraction; Feature recognition; Image processing systems; Methods; Multimedia Information Systems; Object recognition; Pattern recognition; Pixels; Real time; Sensors; Smartphones; Special Purpose and Application-Based Systems; Studies; Mobile systems; OpenCV; Corner detection; Rotated object recognition; Smart phone
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-014-2241-6

In this paper, a new method is proposed for recognizing rotated objects based on extracted feature points from the Harris corner detector using object corner points at real time in a mobile environment. With this proposed method, corner points can be rapidly extracted from the input image and object recognition can be conducted by comparing existing feature points. Color values for corner pixels in the feature points of rotated objects are generally varied in accordance with the degree of object rotation. The color values of the feature points are affected by the degree of rotated objects; therefore, there is a possibility that these values can be mixed with nearby pixel values. By analyzing the color values of the rotated pixels, we can extract feature points of rotated objects and use them for object detection to be effectively applied for rotated object detection. Although overall recognition rate is somewhat degraded, using the proposed object recognition with corner information of feature points makes it possible to execute in real time in mobile environments by reducing the amount of computation. Accordingly, this method can be applied to recognizing rotated objects, even in a mobile environment with limited computing capabilities. Experimental results show that the proposed method can provide approximately 96 % accuracy and high performance compared to other methods. Therefore, the proposed method can be adapted to recognize any rotated object with performance and accuracy.