Proceedings Article | 29 November 2007
KEYWORDS: Hough transforms, Image segmentation, Optical tracking, Image processing, Visualization, Servomechanisms, Detection and tracking algorithms, Image quality, Light sources and illumination, Feature extraction
Object-identification using edge extraction techniques from background function in uncontrolled lighting environments
containing more object pose information and have many applications. In order to depressing noise, identify aim body
robustly and rapidly, in this paper, We take cuboid as model and present a new strategy for edge extraction and object-identification
based on object inherent features. This strategy includes the following steps. Firstly, pre-processing is
applied to the raw image, in which Canny operator was used to extract edges pixels, then, image was divided into a grid
of overlapping windows and noise was suppressed by regression grid windows in which the number of pixels is less than
a threshold. Secondly, as model contour's geometry characters known already, the cuboids upright edges was used as
their existence evidence to estimate model's existence area and so the lines failed spatial constraints are eliminated, then,
object edges was extracted within the finite ranges of orientation in Hough transform space. Thirdly, the intersections of
the component extracted edges are taken, the candidate edges extraction and matches was assessed based on the
intersections, rather than the component extracted edges. After a series of matching tests the aim body is extracted.
The proposed method makes three major contributions. Firstly, on the base of study the correspondence between model's
boundary edges parameters in image space and Hough space we extract edges in finite area in Hough transform space,
the aimless computations and searching is reduced greatly, its efficiency improved. Secondly, as Canny operator can
extract aim lines with single pixel width, the edges extraction strategy of combining Canny operator with Hough
transform extractor could avoid error impact of edges pixels numbers to Hough extractor. Thirdly, after fusion model's
knowledge in image space, Hough space, global space, learning from others strong points to offset one's weakness, we
extract model's edges from complex noise background without regarding to regression caused by the errors due to
spurious or missing pixels because edge extraction is imperfect for real images.
The results of experiments demonstrated that the proposed method could suppress noise effectively, identified and
extracted target from complex backgrounds robustly. This new strategy may have potential application in visual servo,
object tracking, port AGV and robots fields etc.
