Abstract:Problem statement: Segmentation of 3D range images is widely used in computer vision as an essential pre-processing step before the methods of high-level vision can be applied. Segmentation aims to study and recognize the features of range image such as 3D edges, connected surfaces and smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D range images based on edge detection technique. The main idea is to apply a gradient edge detector in three different directions of… Show more
“…Classical edge detectors usually compute the gradient vector in each edge voxel to estimate edge parameters. This gradient vector is generally estimated by means of convolutional masks for partial derivatives, like classical Sobel or Prewitt operators for 2D images, that can be extended to 3D using 3 × 3 × 3 masks, as proposed in [37]. Nevertheless, neither the direction of the edge nor the change in intensity can be accurately obtained with these gradient estimations.…”
Section: Error Analysis With Traditional Derivative Operatorsmentioning
“…Classical edge detectors usually compute the gradient vector in each edge voxel to estimate edge parameters. This gradient vector is generally estimated by means of convolutional masks for partial derivatives, like classical Sobel or Prewitt operators for 2D images, that can be extended to 3D using 3 × 3 × 3 masks, as proposed in [37]. Nevertheless, neither the direction of the edge nor the change in intensity can be accurately obtained with these gradient estimations.…”
Section: Error Analysis With Traditional Derivative Operatorsmentioning
“…In the research work [4], an approach to image segmentation in the 3D range through a gradient method was proposed. Based on this approach, you can improve the segmentation of a 3D advertising image based on edge detection techniques.…”
Section: Literature Review and Problem Statementmentioning
“…The general image segmentation approach is to find a certain index then convert the grey level image of the index to binary image using a proper threshold (Foong et al, 2013;Hafiz et al, 2011;Mustafa and Zhu, 2013). The colour indices used for green plant detection can be generally classified into three categories.…”
Section: The Green Plant Detection Methodsmentioning
More than half of the Australian cropping land is no-tillage and weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefore more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the crop has closed. The design of the methods focuses on overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. The experiment results show that the proposed method are more suitable for the weed detection in the notillage background than the existing methods and could be used as a powerful tool for the weed control.
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