We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Near color recognition based on residual vector and SVM.
- Authors
Zhang, Hehu; Wang, Xiushan; Jiang, Lintao; Xu, Yibo; Jiang, Guoqiang
- Abstract
With the extensive application of machine vision in Agriculture, plant recognition is becoming an important research territory. Due to illumination change, occlusion problem, green background and other factors, the image segmentation quality of plant is uneven. When there are significantly different colors, such as red, green and blue, between plant target and background, classical image processing methods are up to the task. However, in near color scene, for example dark green target and bright green background, plant target recognition is still a very challenging task. To segment plants in above scene, the near color recognition method based on residual vector and SVM has been developed. Firstly, the color vectors were projected to the plane that crosses the point Origin(0, 0, 0) and is perpendicular to reference color vector B0(255, 255, 255). After projection, the significantly different color vectors were distributed in different polar angle ranges, while the near color vectors were concentrated in the same polar angle range. For near color vectors, the small polar angle difference, namely roll, was regarded as redundant information. Then, the angle θ between target color vector A(r, g, b) and B0, along with the norm of A, namely A → was calculated. As a result, the three-dimensional target color vector A was converted to the two-dimensional residual vector R A → θ . Finally, SVM classifier is used to identify the residual vector. The results show that the linear recognition rate is 90.25%, the average recognition speed 0.243 s, the nonlinear recognition rate 87.47%, and the average recognition speed 0.254 s. This study provides theoretical reference for plant target recognition in the near color scene.
- Subjects
COMPUTER vision; GREEN roofs; FOOD color; TEXT recognition; IMAGE segmentation; COLORS; IMAGE processing
- Publication
Multimedia Tools & Applications, 2019, Vol 78, Issue 24, p35313
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-019-08164-1