scholarly journals Lab Scale Model Experiment of Smart Hopper System to Remove Blockages Using Machine Vision and Collaborative Robot

2022 ◽  
Vol 12 (2) ◽  
pp. 579
Author(s):  
Heonmoo Kim ◽  
Yosoon Choi
Keyword(s):  
Machine Vision ◽  
Recognition Accuracy ◽  
Scale Model ◽  
Robot Arm ◽  
Mining Sites ◽  
Image Processing Techniques ◽  
Depth Images ◽  
Proposed Model ◽  
Processing Techniques ◽  
Collaborative Robot

In this study, we propose a smart hopper system that automatically unblocks obstructions caused by rocks dropped into hoppers at mining sites. The proposed system captures RGB (red green blue) and D (depth) images of the upper surfaces of hopper models using an RGB-D camera and transmits them to a computer. Then, a virtual hopper system is used to identify rocks via machine vision-based image processing techniques, and an appropriate motion is simulated in a robot arm. Based on the simulation, the robot arm moves to the location of the rock in the real world and removes it from the actual hopper. The recognition accuracy of the proposed model is evaluated in terms of the quantity and location of rocks. The results confirm that rocks are accurately recognized at all positions in the hopper by the proposed system.

scholarly journals Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

2021 ◽  
Vol 7 ◽  
pp. e405
Author(s):  
Adi Alhudhaif ◽  
Zafer Cömert ◽  
Kemal Polat
Keyword(s):  
Tympanic Membrane ◽  
Otitis Media ◽  
Visual Inspection ◽  
Learning Algorithm ◽  
Decision Support Model ◽  
Image Processing Techniques ◽  
Proposed Model ◽  
Infection And Inflammation ◽  
Decision Making Processes ◽  
Processing Techniques

Background Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. Methods In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. Results The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.

Certain Aspects of Machine Vision in the Arts

2011 ◽  
pp. 236-256
Author(s):  
Marc Bohlen
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Digital Imagery ◽  
Art Practice ◽  
Short Introduction ◽  
Computational Approaches ◽  
Image Processing Techniques ◽  
The Arts ◽  
Processing Techniques

This chapter attempts to consider the consequences of machine vision technologies for the role of the image in the visual arts. After a short introduction, the text gives a practical overview of image processing techniques that are relevant in surveillance, installation, and information art practice. Example work by practitioners in the field contextualizes these more technical descriptions and shows how computational approaches to digital imagery can radically expand the use of the image in the arts. A final note on possible future areas of investigation is included.

A New Measurement System of Yarn Appearance Based on Machine Vision

2014 ◽  
Vol 945-949 ◽  
pp. 1810-1814
Author(s):  
Jun Juan Li ◽  
Chen Wang ◽  
Wen Xiao Tu ◽  
Bao Qi Zuo
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Measurement System ◽  
Coefficient Of Variation ◽  
Suitable Method ◽  
Main Body ◽  
Image Processing Techniques ◽  
Image Acquisition System ◽  
Processing Techniques ◽  
Morphological Opening

In this paper, a new yarn appearance measurement system based on machine vision is introduced. The yarn images are continuously captured by image acquisition system. To extract the main body of the yarn accurately, the yarn images are processed sequentially with threshold segmentation and morphological opening operation. Then the coefficient of variation (CV value) of diameter is calculated to characterize the yarn evenness. The measurement process achieves result (CV value) which can be compared with USTER evenness tester by image processing techniques. By comparing different methods which use different algorithms, a suitable method is chosen to be used in this new measurement system. Then a more accurate, more efficient and faster measurement system will meet requirements in the future.

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Rice Panicle Blast Detection and Grading Based on Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 165-168 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Swaraj Kumar Sahu ◽  
Nalini Kanta Barpanda ◽  
Amiya Kumar Rath
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Panicle Blast ◽  
Processing Techniques
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