IDENTIFIKASI RUANGAN DENGAN SENSOR KAMERA PADA ROBOT KRPAI BERKAKI

2020 ◽  
Vol 3 (1) ◽  
pp. 45
Author(s):  
Rendi Pambudi ◽  
Totok Winarno ◽  
Sidik Nurcahyo
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Corner Detection

 Saat ini perkembangan teknologi di bidang robotika berkembang pesat. Terutama pada pemilihan sensor yang akan digunakan pada robot. Dengan kamera sebagai sensor maka robot mampu melakukan visualisasi objek dan keadaan sekitarnya. Dari data visual hasil tangkapan sensor kamera tersebut kemudian di lakukan proses pengolahan citra (image processing) sehingga didapatkan sebuah persamaan untuk pola tindakan robot. Melengkapi robot dengan sensor visi / kamera meningkatkan fleksibilitas dari robot tetapi juga menyebabkan kendalinya semakin kompleks. Meskipun tingkat kesulitannya meningkat, sensor visi menjadi semakin menarik untuk navigasi robot karena memberikan informasi yang kaya tentang lingkungan robot. Pada robot KRPAI berkaki, sensor kamera dapat digunakan sebagai sensor untuk melakukan identifikasi ruangan bersama dengan sensor ultrasonic SRF08. Dengan melakukan pengolahan citra menggunakan metode tresholding, edge detection, dan corner detection robot dapat ber-alignment untuk memposisikan diri dalam melakukan proses identifikasi ruangan. Proses alignment dengan mendeteksi sudut pada ruangan. Setelah sudut dari ruangan terdeteksi, sudut terluar akan dibandingkan untuk mengetahui posisi robot pada saat ini menghadap kemana. Selanjutnya dapat ditentukan robot harus bergerak kemana untuk memposisikan dengan bidang pengukuran. Dengan melakukan alignment untuk memposisikan terhadap bidang pengukuran, dapat meningkatkan ketelitian dalam identifikasi ruangan.

IMAGE PROCESSING VIA CNN GENES WITH VON NEUMANN NEIGHBORHOODS

2005 ◽  
Vol 15 (12) ◽  
pp. 3999-4006 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
FANG-YUE CHEN ◽  
GUO-LONG HE
Keyword(s):  
Image Processing ◽  
Computer Simulation ◽  
Edge Detection ◽  
Vertical Line ◽  
Corner Detection ◽  
Line Detection ◽  
Von Neumann ◽  
Center Point ◽  
Better Than

Some image processing research are restudied via CNN genes with five variables, and this include edge detection, corner detection, center point extraction and horizontal-vertical line detection. Although they were implemented with nine variables, the results of computer simulation show that the effect with five variables is identical to or better than that with nine variables.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

scholarly journals New Edge Detection Method for Indonesian Batik

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Adi Purnomo Shidi ◽  
Suyoto Suyoto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Detection Method ◽  
New Method ◽  
Analysis Method ◽  
Unique Pattern ◽  
Detection Analysis ◽  
Edge Detection Method

Abstrak. Metode Baru Deteksi Tepi untuk Batik Indonesia. Didalam paper ini, diusulkan sebuah metode pendeteksi baru untuk motif batik. Deteksi tepi sudah sangat sering digunakan didalam pemrosesan gambar. Batik motif adalah salah satu contoh gambar yang memiliki bentuk yang unik dan menarik untuk dianalisis. Metode yang digunakan pada paper ini adalam metode canny dan prewit dan akan menghasilkan metode baru yaitu metode Thomas. Perbedaan antara metode dan hasil akan dilihat dari sisi ketepatan, qualitas hasil dan kejelasan. Contoh batik yang akan digunakan adalah motif parang, motife lereng dan udan liris. Ketiga batik tersebut memiliki pola  yang unik. Kata kunci : Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris. Abstract. New Edge Detection Method for Indonesian Batik. In this paper, we propose a new edge detection analysis method on batiks motif. Edge detection has been oftenly  used in computer vision and image processing. Indonesian  Batiks motif are some example of graphic picture that has unique pattern that interesting to analyse. The method that used for example on this paper are canny and prewit and produce a new method, thomas method. the different  amongs the method, the result of comparison appears on quality, accuracy and clarity. The example that we use are parang batiks motive, lereng batiks motive, and udan liris batiks motive. Three of batiks motive above are have unique pattern. Keywords: Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris.

Cellular Automata Algorithms for Digital Image Processing

Biometrics ◽  
2017 ◽  
pp. 382-402
Author(s):  
Petre Anghelescu
Keyword(s):  
Image Processing ◽  
Cellular Automata ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Understanding ◽  
Software Application ◽  
Detection Algorithms ◽  
Human Image ◽  
The Impact

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

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