scholarly journals The method of increasing the information content of microfocus X-ray images

2021 ◽  
Vol 9 (6) ◽  
pp. 57-63
Author(s):  
N. E. Staroverov ◽  
A. Y. Gryaznov ◽  
I. G. Kamyshanskaya ◽  
N. N. Potrakhov ◽  
E. D. Kholopova
Keyword(s):  
Image Processing ◽  
Information Content ◽  
High Frequency ◽  
Noise Suppression ◽  
Projection Data ◽  
Frequency Filtering ◽  
X Ray ◽  
Morphological Image Processing ◽  
Morphological Image ◽  
Image Details

A method for processing microfocus X-ray images is described. It is based on high-frequency filtration and morphological image processing, which increases the contrast of the X-ray details. One of the most informative X-ray techniques is microfocus X-ray. In some cases, microfocus X-ray images cannot be reliably analyzed due to the peculiarities of the shooting method. So, the main disadvantages of microfocus X-ray images are most often an uneven background, distorted brightness characteristics and the presence of noise. The proposed method for enhancing the contrast of fine image details is based on the idea of combining high-frequency filtering and morphological image processing. The method consists of the following steps: noise suppression in the image, high-frequency filtering, morphological image processing, obtaining the resulting image. As a result of applying the method, the brightness of the contours in the image is enhanced. In the resulting image, all objects will have double outlines. The method was tested in the processing of 50 chest radiographs of patients with various pathologies. Radiographs were performed at the Mariinsky Hospital of St. Petersburg using digital stationary and mobile X-ray machines. In most of the radiographs, it was possible to improve the images contrast, to highlight the objects boundaries. Besides, the method was applied in microfocus X-ray tomography to improve the information content of projection data and improve the reconstruction of the 3D image of the research object. In both the first and second cases, the method showed satisfactory results. The developed method makes it possible to significantly increase the information content of microfocus X-ray images. The obtained practical results make it possible to count on broad prospects for the method application, especially in microfocus X-ray.

scholarly journals Robotic grasp detection based on image processing and random forest

2019 ◽  
Vol 79 (3-4) ◽  
pp. 2427-2446 ◽  
Author(s):  
Jiahao Zhang ◽  
Miao Li ◽  
Ying Feng ◽  
Chenguang Yang
Keyword(s):  
Image Processing ◽  
Random Forest ◽  
Main Idea ◽  
Morphological Image Processing ◽  
Segmentation Approach ◽  
Novel Objects ◽  
Set Up ◽  
The One ◽  
Objects Detection ◽  
Morphological Image

AbstractReal-time grasp detection plays a key role in manipulation, and it is also a complex task, especially for detecting how to grasp novel objects. This paper proposes a very quick and accurate approach to detect robotic grasps. The main idea is to perform grasping of novel objects in a typical RGB-D scene view. Our goal is not to find the best grasp for every object but to obtain the local optimal grasps in candidate grasp rectangles. There are three main contributions to our detection work. Firstly, an improved graph segmentation approach is used to do objects detection and it can separate objects from the background directly and fast. Secondly, we develop a morphological image processing method to generate candidate grasp rectangles set which avoids us to search grasp rectangles globally. Finally, we train a random forest model to predict grasps and achieve an accuracy of 94.26%. The model is mainly used to score every element in our candidate grasps set and the one gets the highest score will be converted to the final grasp configuration for robots. For real-world experiments, we set up our system on a tabletop scene with multiple objects and when implementing robotic grasps, we control Baxter robot with a different inverse kinematics strategy rather than the built-in one.

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