scholarly journals PERANCANGAN SISTEM DETEKSI OBJEK PADA ROBOT KRSBI BERBASIS MINI PC RASPBERRY PI 3

2019 ◽  
Vol 12 (1) ◽  
pp. 56-64
Author(s):  
Ilfan Sugianda ◽  
Thamrin Thamrin
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Raspberry Pi ◽  
Process Data ◽  
Single Board Computer ◽  
Object Based ◽  
Football Match ◽  
Omni Wheel ◽  
Python Programming ◽  
Camera Sensor

KRSBI Wheeled is One of the competitions on the Indonesian Robot Contest,. It is a football match that plays 3 robot full autonomous versus other teams. The robot uses a drive in the form of wheels that are controlled in such a way, to be able to do the work the robot uses a camera sensor mounted on the front of the robot, while for movement in the paper author uses 3 omni wheel so the robot can move in all directions to make it easier towards the ball object. For the purposes of image processing and input and output processing the author uses a Single Board Computer Raspberry PI 3 are programmed using the Python programming language with OpenCV image processing library, to optimize the work of Single Board Computer(SBC) Raspberry PI 3 Mini PC assisted by the Microcontroller Arduino Mega 2560. Both devices are connected serially via the USB port. Raspberry PI will process the image data obtained webcam camera input. Next, If the ball object can be detected the object's position coordinates will be encoded in character and sent to the Microcontroller Arduino Mega 2560. Furthermore, Arduino mega 2560 will process data to drive the motors so that can move towards the position of the ball object. Based on the data from the maximum distance test results that can be read by the camera sensor to be able to detect a ball object is �5 meters with a maximum viewing angle of 120 �.

scholarly journals DESENVOLVIMENTO DE UM DISPOSITIVO PARA APOIO AO ENSINO DE COMPUTAÇÃO E ROBÓTICA

2014 ◽  
Vol 6 (2) ◽  
pp. 71-85
Author(s):  
Rafael de Oliveira Maia ◽  
Francisco Assis da Silva ◽  
Mário Augusto Pazoti ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira
Keyword(s):  
Image Processing ◽  
Computer Science ◽  
Programming Language ◽  
Raspberry Pi ◽  
Writing Programs ◽  
Python Programming Language ◽  
Python Programming ◽  
And Robotics

In this work we proposed the development of an alternative device as a motivating element to learn computer science and robotics using the Raspberry PI and Arduino boards. The connections of all hardware used to build the device called Betabot are presented and are also reported the technologies used for programming the Betabot. An environment for writing programs to run at Betabot was developed. With this environment it is possible to write programs in the Python programming language, using libraries with functions specific to the device. With the Betabot using a webcam and through image processing search for patterns like faces, circles, squares and colors. The device also has functions to move servos and motors, and capture values returned by some kindsof sensors connected to communication ports. From this work, it was possible to develop a device that is easy to be manipulated and programmed, which can be used to support the teaching of computer science and robotics.

scholarly journals Deteksi Objek Menggunakan Metode Single Shot Multibox Detector Pada Alat Bantu Tongkat Tunanetra Berbasis Kamera

2020 ◽  
Vol 3 (2) ◽  
pp. 39
Author(s):  
Samratul Fuady ◽  
Nehru Nehru ◽  
Gina Anggraeni
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Extraction Process ◽  
Raspberry Pi ◽  
Single Shot ◽  
Blind People ◽  
Inanimate Object ◽  
Object Based ◽  
Limited Sensing ◽  
Indoor And Outdoor

Blind people have difficulty in navigating due to the limited sensing they are capable of. In this research, we design a stick tool that can distinguish objects in the form of humans, animals and inanimate object based on camera. Processing is carried out with the Raspberry Pi with a webcam camera as input and indicators in the form of a buzzer and vibrator. The feature extraction process is carried out by deep learning using the tensorflow library and image processing using the Single Shot MultiBox Detector (SSD) method. Tests were carried out on human objects, animals (cats), and inanimate objects (chairs and tables) for indoor and outdoor conditions and obtained an accuracy of 92%, a sensitivity of 83%, and a specificity of 100%.

scholarly journals Embedded image processing on Raspberry Pi connected to the industrial control system

2019 ◽  
Vol 2 (1) ◽  
pp. 62-71
Author(s):  
Jozef Hrbček ◽  
Emília Bubeníková
Keyword(s):  
Image Processing ◽  
Control System ◽  
Real Time ◽  
Image Data ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Industrial Control System ◽  
Industrial Control ◽  
Industrial Automation ◽  
Embedded Image

Abstract This paper deals with the image processing from the camera for Raspberry Pi connected with real-time communication network to the control system (PLC). The low time delay for receiving and sending commands, data, etc. is very important in the automating production processes. This can be provided by industrial real-time network based on Ethernet. The Ethernet POWERLINK, which is supported on B&R PLCs, is one of them. It is a simple solution for a variety of applications because the POWERLINK is publicly available as the open source. Connecting the PLC and Raspberry Pi with Ethernet POWERLINK opens up many applications in industrial automation. For example, image data obtained using a camera attached to Raspberry Pi can be used to sense image of manufacturing processes and products and evaluate their quality in industrial automation. This article focuses on an image processing unit and the PLC system with CPU redundancy used in the industrial application. Vision systems are often used to improve products quality control, saving costs and time.

scholarly journals Converting 2D-Medical Image Files “DICOM” into 3D- Models, Based on Image Processing, and Analysing Their Results with Python Programming

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Mixed Reality ◽  
Image Data ◽  
3D Models ◽  
Language Program ◽  
Donor Liver Transplantation ◽  
Before And After ◽  
Conversion Stage ◽  
Python Programming

This paper presents the possibility of converting (2D) medical image data (Digital Imaging and Communications in Medicine (DICOM) files) to 3D model. Medical data and image processing software’s, namely Seg3D2 and ImageVis3D, were used to analyze images, create 3D models of the liver and export them in OBJ images for performing a range of surgical procedures, and measure the accuracy of the size and weight of the liver, kidneys and arteries with their conformity to DICOM file. It is compared to the image processing before and after the conversion stage of medical image using the Python language program to ensure the integrity of the images after the conversion process is identical to the original pictures of DICOM without causing any distortions or changes to it. We reduce file size while maintaining the model’s highest quality, while employing mixed reality techniques, applied on Liver Surgical Operation [living donor liver Transplantation (LDLT)].

scholarly journals Raspberry Pi Powered Imaging for Plant Phenotyping

2017 ◽  
Author(s):  
Jose C. Tovar ◽  
J. Steen Hoyer ◽  
Andy Lin ◽  
Allison Tielking ◽  
Monica Tessman ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
High Throughput ◽  
Plant Diversity ◽  
Low Cost ◽  
Image Acquisition ◽  
Image Data ◽  
Raspberry Pi ◽  
Source Image ◽  
Plant Image

ABSTRACTPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.Methods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.Conclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Integration of opencv raspberry pi 3b+ and camera sensor in access control of vehicle ignition key system

2020 ◽  
Vol 909 ◽  
pp. 012002
Author(s):  
Lukman Medriavin Silalahi ◽  
Imelda Uli Vistalina Simanjuntak ◽  
Freddy Artadima Silaban ◽  
Setiyo Budiyanto ◽  
Heryanto ◽  
...  
Keyword(s):  
Access Control ◽  
Raspberry Pi ◽  
Camera Sensor
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