A fast binary-image comparison method with local-dissimilarity quantification

2006 ◽  
Author(s):  
E. Baudrier ◽  
G. Millon ◽  
F. Nicolier ◽  
Su Ruan
Keyword(s):  
Comparison Method ◽  
Binary Image ◽  
Image Comparison

Research on the Paper Splicing Recovery

2014 ◽  
Vol 602-605 ◽  
pp. 3443-3446
Author(s):  
Yu Xuan Jiang
Keyword(s):  
Neural Network ◽  
Euclidean Distance ◽  
Comparison Method ◽  
Binary Image ◽  
Network Data ◽  
Grey Level ◽  
Binary Matrix ◽  
Som Neural Network ◽  
Restoration Problem

This paper focuses on scraps of paper splicing recovery issues. To deal with complex scraps restoration problem, first the image should be changed as pixel grey level binary image, generating binary matrix, and then classify the scraps using SOM neural network data and feature comparison method. Combining with the Euclidean distance discriminant method, we match the scraps of paper with same edges and obtain a complete stitching schematic finally.

scholarly journals DETEKSI GERAK OBJEK BERBASIS PENGOLAHAN CITRA MENGGUNAKAN METODE BINARY-IMAGE COMPARISON

2020 ◽  
Vol 6 (1) ◽  
pp. 109-116
Author(s):  
Abdul Jalil
Keyword(s):  
Operating System ◽  
Data Distribution ◽  
Binary Image ◽  
Image Comparison ◽  
Robot Operating System ◽  
Data Message ◽  
System 2 ◽  
Data Distribution Service

Tujuan penelitian ini adalah membangun sistem deteksi gerak objek berbasis teknik pengolahan citra menggunakan metode Binary-Image Comparison (BIC). Fungsi metode BIC pada penelitian ini adalah sebagai pengambil keputusan pada saat sistem mengirim data message sebagai hasil dari deteksi gerak objek. Adapun gerak objek yang dideteksi pada penelitian ini adalah objek dengan warna merah, kuning, hijau, dan biru. Pada penelitian ini, proses segmentasi citra biner diproses menggunakan perangkat lunak Library OpenCV yang dieksekusi didalam node Robot Operating System 2 (ROS2). Terdapat beberapa node ROS2 yang digunakan untuk membangun sistem deteksi gerak objek pada penelitian ini, yaitu node untuk membaca input kamera RGB, node untuk mendeteksi gerak objek warna merah, node untuk mendeteksi gerak objek warna kuning, node untuk mendeteksi gerak objek warna hijau, node untuk mendeteksi gerak objek berwarna biru, dan node untuk menerima hasil proses deteksi warna. Setiap node pada sistem tersebut dapat saling terhubung melalui topic untuk dapat saling bertukar data message menggunakan protokol Data Distribution Service (DDS) yang ada pada ROS2. Hasil dari penelitian ini adalah sistem dapat mendeteksi gerak objek warna merah, kuning, hijau, dan biru kemudian mengirimnya sebagai data message berdasarkan hasil keputusan dari metode BIC.

scholarly journals On the Role of Context-Awareness in Binary Image Comparison

2022 ◽  
Author(s):  
Sara Iglesias-Rey ◽  
Aitor Castillo-Lopez ◽  
Carlos Lopez-Molina ◽  
Bernard De Baets
Keyword(s):  
Context Awareness ◽  
Binary Image ◽  
Image Comparison

scholarly journals Binary-image comparison with local-dissimilarity quantification

2008 ◽  
Vol 41 (5) ◽  
pp. 1461-1478 ◽  
Author(s):  
Étienne Baudrier ◽  
Frédéric Nicolier ◽  
Gilles Millon ◽  
Su Ruan
Keyword(s):  
Binary Image ◽  
Image Comparison

Study on Recognition of Black Insects on Dark Background by Computer Vision

2013 ◽  
Vol 756-759 ◽  
pp. 4685-4689
Author(s):  
Sa Liu ◽  
Yan Yang ◽  
Xiao Dong Zhu ◽  
Huai Wei Wang ◽  
Shi Bin Lian
Keyword(s):  
Computer Vision ◽  
Comparison Method ◽  
Color Images ◽  
Binary Image ◽  
Color Channel ◽  
Gray Image ◽  
Dark Background ◽  
Average Brightness ◽  
Image Pixels ◽  
Pixel Brightness

Improved color channel comparison method (ICCCM) is an effective method to transformcolor images into gray-scale ones. Based on the ICCCM, black or white insects could be effectively extracted and recognized from the real color images with bright background. Howeverit is difficult to use the ICCCM to extract and recognize the black insects from the realcolorimage with dark background. In this paper, the ICCCM is modified to transformthe color images into the gray ones, extracting and recognizing the black insectson the dark background. The ICCCM is modified as follows: (1) A threshold of the gray image is an average brightness value ofred (R), green (G) and blue (B) in all the image pixels.(2) The bright pixels and the color pixels have the highest brightness value 255 in the gray image.(3) A pixel brightness value of the dark area in the gray image equals to a minimum of R, G and B in the pixel. (4) After deleted all the pixels with a brightness value of 255, a threshold of the binary image is determined by Otsus theory. The modified ICCCM more effectively extracts and recognizes the black insects from the realcolorimages with dark background compared with the ICCCM.

scholarly journals Strip Adjustment of Airborne LiDAR Data in Urban Scenes Using Planar Features by the Minimum Hausdorff Distance

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5131 ◽  
Author(s):  
Liu ◽  
Ma ◽  
Zhang ◽  
Cai ◽  
Ma
Keyword(s):  
Hausdorff Distance ◽  
Region Growing ◽  
Comparison Method ◽  
Binary Image ◽  
Airborne Lidar ◽  
Connected Components ◽  
Lidar Data ◽  
Building Roof ◽  
Dual Channel ◽  
Normal Vectors

In Airborne Light Detection and Ranging (LiDAR) data acquisition practice, discrepancies exist between adjacent strips even though careful system calibrations have been performed. A strip adjustment method using planar features acquired by the Minimum Hausdorff Distance (MHD) is proposed to eliminate these discrepancies. First, semi-suppressed fuzzy C-means and restricted region growing algorithms are used to extract buildings. Second, a binary image is generated from the minimum bounding rectangle that covers overlapping regions. Then, connected components labeling algorithm is applied to process the binary image to extract individual buildings. After that, building matching is performed based on MHD. Third, a coarse-to-fine approach is used to segment building roof planes. Then, plane matching is conducted under the constraints of MHD and normal vectors similarity. The last step is the calculation of the parameters based on Euclidean distance minimization between matched planes. Two different types of datasets, one of which was acquired by a dual-channel LiDAR system Trimble AX80, were selected to verify the proposed method. Experimental results show that the corresponding planar features that meet adjustment requirements can be successfully detected without any manual operations or auxiliary data or transformation of raw data, while the discrepancies between strips can be effectively eliminated. Although adjustment results of the proposed method slightly outperform the comparison alternative, the proposed method also has the advantage of processing the adjustment in a more automatic manner than the comparison method.

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