Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

Fast binary dilation/erosion algorithm using kernel subdivision

Abstract: Numerous algorithms have been proposed in the literature to speed up dilation/erosion operations. The motivation has been to reduce computational complexity by exploiting the structuring element and the image object properties. This paper presents a new algorithm for binary morphological dilation and erosion called the Kernel Sub-Division algorithm and discusses its implementation in the two dimensional case. It decomposes the n-dimensional structuring element, into several subsets and operates on the object contours in the image. The image characteristics are exploited by subdividing the object contours into bins while performing contour processing. The elegance of the algorithm lies in its retaining the correspondence to the output of the classical implementation with massive speed gain. The results of the algorithm on a statistically significant test set of images, showed that it performed five times better than the classical implementation for a 3x3 kernel. It also demonstrated a marginal rise in execution time with increasing size of the kernel.

Noisy Image Edge Detection Based on Multi-scale and Multi-structuring Element Order Morphology Transformation

Abstract: The image edge detection is important tool of image processing and also is the foundation of pattern recognition and computer vision, which will affect the later processes. To noisy image, the edge detection is more important for noise is so common in image. This paper deeply studies the edge detection methods of the noisy images and concentrates on the order morphology method. By analysis and study the theory of order morphology, this paper constructs three edge detection operations and analyzes the specialities of these operations and the structure elements. Based on this, the new edge detection operations for noisy image based on multi-scale and multi-structuring element order morphology are proposed. The operations can obtain clear and exact edge of the noisy images. By simulation and comparing with the traditional edge detection operations and the order morphology operations, the operations are more effective on noise restraining and retaining the image details.

Depth Recovery for Kinect Sensor Using Contour-Guided Adaptive Morphology Filter

Abstract: Consumer-grade RGB-D cameras, such as Kinect sensors, can provide support for much more real-time tasks of 3-D vision than game controllers. However, the inherent depth degradations caused by their infrared ranging will constrain their application potential, but can hardly be avoided through the improvement of the sensor design. Therefore, in this paper, we proposed a contours-guided shape-adaptive morphology filter to efficiently recover the depth of Kinect sensors. First, we put forward a statistical concept to quantitatively evaluate the texture richness of imaging sensors’ data and verify the applicability of morphology filtering on both Kinect 1 and 2 depth data. Then, considering the significance of the semantic contours, a multiresolution RGB-D contour extraction method is introduced to suppress the texture inside objects. Therewith, shape-adaptive structuring element (SASE) for each missing or untrusted depth pixel is created in terms of the contour guidance and the feature of human visual system. Efficient and accurate depth recovery can be finally achieved by combining morphology filtering and the obtained SASEs. Experiments on simulated data set, real Kinect 1, and Kinect 2 data show that our method performs better than many competing state-of-the-art approaches, and avoids the blurring around depth discontinuities.