Automatic mantispid egg detection and counting using image nature

2020 ◽  
Vol 34 (22n24) ◽  
pp. 2040138
Author(s):  
Pei-Ying Yang ◽  
Chin-Dar Tseng ◽  
Tai-Lin Huang ◽  
Chao-Hong Liu ◽  
I-Hsing Tsai ◽  
...  
Keyword(s):  
Image Processing ◽  
Background Subtraction ◽  
Counting System ◽  
Insect Eggs ◽  
Background Estimation ◽  
Morphological Image Processing ◽  
Automatic Counting ◽  
Manual Counting ◽  
Detection And Counting ◽  
Morphological Image

Mantispids are small brown bugs about 1.5 cm in length. Mantispid eggs are produced in large quantities, with about 1000 eggs per spawning, and are tiny and densely packed. Traditionally, mantispid eggs are counted manually. However, counting such a large quantity of eggs is difficult. To provide accurate data for researchers, we detail methods to accurately detect and count the number of mantispid eggs using image processing. The following methods were used to count the mantispid eggs: background estimation, morphological image processing, background subtraction, stretching, image thresholding, gray-level transformation, labeling and counting. The results of automated counting were compared with the results of manual counting. The segmentation results were verified, and the accuracy of the mantispid egg counts was determined to be 100%. This provides a useful resource for mantispid egg counting. The automatic counting system cannot only count mantispid eggs, it can also be used to count other similar insect eggs.

Automatic Counting System for Nuclear Track Based on DSP and Mathematical Morphology Algorithm

2014 ◽  
Vol 610 ◽  
pp. 287-290
Author(s):  
Yi Pan ◽  
Liang Jun Liu
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
Nuclear Technology ◽  
Counting System ◽  
Automatic Counting ◽  
Manual Counting ◽  
Important Position ◽  
Processing Techniques ◽  
Morphology Algorithm ◽  
Visual Error

Parameter measurement of the solid state nuclear track occupies an extremely important position in the field of nuclear technology while limitation of the traditional manual counting method is very large. In recent years, DSP and image processing techniques are increasingly applied in the field of nuclear technology. This paper describes an automatic counting system for nuclear track based on DSP image processing platform which uses DSP hardware platform and mathematical morphology algorithm. This system can effectively separate the track point from the background and remove noise, and also accurately count helping to reduce the visual error of manual counting.

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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