Wyniki wyszukiwania - BazTech

1

Digital image restoration using SURF algorithm

Tammineni Shanmukhaprasanthi, Rayavarapu Swarajya Madhuri, Gottapu Sasibhushana Rao, Goswami Raj Kumar

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2024

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T. 14, nr 1

37--40

EN

In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.

PL

We współczesnych czasach utrwalanie dorobku naukowego i twórczego często opiera się na wykorzystaniu archiwów filmowych i obrazowych, co podkreśla znaczenie przetwarzania obrazu jako przedsięwzięcia krytycznego. Inpainting odnosi się do procesu cyfrowej zmiany obrazu w sposób, który sprawia, że korekty są niezauważalne dla widza nie znającego oryginalnego obrazu. Inpainting to technika stosowana najczęściej w celu przywracania uszkodzonych obszarów obrazu poprzez wykorzystanie informacji uzyskanych na podstawie dopasowania cech odpowiednich obrazów. Proces ten polega na wypełnieniu uszkodzonych obszarów i usunięciu niepożądanych obiektów. Rozważany algorytm SURF (Speeded Up Robust Feature) dzieli się na trzy główne fazy. Po pierwsze, identyfikowane są podstawowe cechy obrazu zaburzonego i obrazu istotnego. W drugim etapie określa się relację pomiędzy obrazem uszkodzonym a obrazem odpowiednim pod względem translacji, skalowania i rotacji. Ostatecznie zniszczony obszar rekonstruuje się poprzez zastosowanie transformacji odwrotnej. Ocenę jakości renowacji obrazów można ocenić za pomocą wskaźników, takich jak wskaźnik podobieństwa strukturalnego (SSIM), szczytowy stosunek sygnału do szumu (PSNR) i błąd średniokwadratowy (MSE). Wyniki eksperymentów dostarczają dowodów na to, że sugerowana technika renowacji jest skuteczna zarówno pod względem szybkości, jak i jakości.

2

Automatic detection of brain tumors using genetic algorithms with multiple stages in magnetic resonance images

Annam Karthik, Kumar Sunil G., Babu Ashok P., Domala Narsaiah

Journal of Automation Mobile Robotics and Intelligent Systems

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2022

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Vol. 16, No. 4

36--43

EN

The field of biomedicine is still working on a solution to the challenge of diagnosing brain tumors, which is now one of the most significant challenges facing the profession. The possibility of an early diagnosis of brain cancer depends on the development of new technologies or instruments. Automated processes can be made possible thanks to the classification of different types of brain tumors by utilizing patented brain images. In addition, the proposed novel approach may be used to differentiate between different types of brain disorders and tumors, such as those that affect the brain. The input image must first undergo pre-processing before the tumor and other brain regions can be separated. Following this step, the images are separated into their respective colors and levels, and then the Gray Level Co-Occurrence and SURF extraction methods are used to determine which aspects of the photographs contain the most significant information. Through the use of genetic optimization, the recovered features are reduced in size. The cut-down features are utilized in conjunction with an advanced learning approach for the purposes of training and evaluating the tumor categorization. Alongside the conventional approach, the accuracy, inaccuracy, sensitivity, and specificity of the methodology under consideration are all assessed. The approach offers an accuracy rate greater than 90%, with an error rate of less than 2% for every kind of cancer. Last but not least, the specificity and sensitivity of each kind are higher than 90% and 50%, respectively. The usage of a genetic algorithm to support the approach is more efficient than using the other ways since the method that the genetic algorithm utilizes has greater accuracy as well as higher specificity.

3

Robust image forgery detection using point feature analysis

William Youssef, Safwat Sherine, Abdel-Megeed Salem Mohammed

Annals of Computer Science and Information Systems

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2019

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Vol. 18

373–-380

EN

Day for day it becomes easier to temper digital images. Thus, people are in need of various forgery image detection. In this paper, we present forgery image detection techniques for two of the most common image tampering techniques; copy-move and splicing. We use match points technique after feature extraction process using SIFT and SURF. For splicing detection, we extracted the edges of the integral images of Y , Cb, and Cr image components. GLCM is applied for each edge integral image and the feature vector is formed. The feature vector is then fed to a SVM classifier. For the copy-move, the results show that SURF feature extraction can be more efficient than SIFT, where we achieved 80% accuracy of detecting tempered images. On the other hand, processing the image in YCbCr color model is found to give promising results in splicing image detection. We have achieved 99% true positive rate for detecting splicing images.

4

Feature extraction and template matching algorithm classification for PCB fiducial verification

Fonseka C. L. S. C., Jayasinghe J. A. K. S.

Journal of Achievements in Materials and Manufacturing Engineering

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2018

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Vol. 86, nr 1

14--32

EN

Purpose: Automatic Optical Inspection (AOI) systems that are extensively used in the industry of Electronics Manufacturing Services (EMS), performs the inspection of Surface Mount Devices (SMD). One of the main tasks of such an AOI system is to align a given PCB to the parameters of the corresponding PCB positioning system by a process called fiducial alignment. However, no detailed analysis has been carried out so far on the methodologies that can be used to have a very precise identification of PCB fiducial points. In our research, we have implemented an AOI system for the inspection of soldering defects of Through Hole Technology (THT) solder joints, which can be integrated to a desktop soldering robotic platform. Such platforms are used in environments where no specific lighting conditions can be provided within a surrounded atmosphere. Therefore, an AOI system that is capable of performing fiducial alignment of any given PCB under varying lighting condition is highly preferred. In this paper, we have presented a detailed analysis on feature extraction and template matching algorithms that can be used to implement a very precise fiducial verification process under normal lighting condition. Design/methodology/approach: A detailed analysis and performance evaluation have been carried out in this paper on prominent image comparison algorithms that are extensively used in the field of image processing. Findings: According to the analysis carried out in this paper, it could be observed that the combination of feature extraction and template matching algorithms gives the best performance in PCB fiducial verification process. Research limitations/implications: This paper only presents the implementation of the front end of our proposed AOI system. The implemented methodologies for the automatic identification of soldering defects will be discussed in separate research papers. Practical implications: The methodologies presented in this paper can be effectively used to implement a very precise and robust PCB fiducial verification process that can be efficiently integrated to a desktop soldering robotic system. Originality/value: This research proposes a very accurate fiducial verification process that can be used under varying lighting conditions on a wide range of different PCB fiducial points.

5

A binary representation for real-valued, local feature descriptors

Oszust M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2017

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Vol. 11, No. 1

3--9

EN

The usage of real-valued, local descriptors in computer vision applications is ofen constrained by their large memory requirements and long matching time. Typical approaches to the reduction of their vectors map the descriptor space to the Hamming space in which the obtained binary strings can be efficiently stored and compared. In contrary to such techniques, the approach proposed in this paper does not require a data-driven binarisation process, but can be seen as an extension of the floating-point descriptor computation pipeline with a step that allows turning it into a binary descriptor. In this step, binary tests are performed on values determined for pixel blocks from the described image patch. In the paper, the proposed approach is described and applied to two popular real-valued descriptors, SIFT and SURF. The paper also contains a comparison of the approach with state-of-the-art binarisation techniques and popular binary descriptors. The results demonstrate that the proposed representation for real-valued descriptors outperforms other methods on four demanding benchmark image datasets.

6

Localization of component lead inside a THT solder joint for solder defects classification

Fonseka C. L. S. C., Jayasinghe J. A. K. S.

Journal of Achievements in Materials and Manufacturing Engineering

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2017

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Vol. 83, nr 2

57--66

EN

Purpose: Automatic Optical Inspection (AOI) systems, used in electronics industry have been primarily developed to inspect soldering defects of Surface Mount Devices (SMD) on a Printed Circuit Board (PCB). However, no commercially available AOI system exists that can be integrated to a desktop soldering robotic system, which is capable of identifying soldering defects of Through Hole Technology (THT) solder joints along with the soldering process. In our research, we have implemented an AOI platform that is capable of performing automatic quality assurance of THT solder joints in a much efficient way. In this paper, we have presented a novel approach to identify soldering defects of THT solder joints, based on the location of THT component lead top. This paper presents the methodologies that can be used to precisely identify and localize THT component lead inside a solder joint. Design/methodology/approach: We have discussed the importance of lead top localization and presented a detailed description on the methodologies that can be used to precisely segment and localize THT lead top inside the solder joint. Findings: It could be observed that the precise localization of THT lead top makes the soldering quality assurance process more accurate. A combination of template matching algorithms and colour model transformation provide the most accurate outcome in localizing the component lead top inside solder joint, according to the analysis carried out in this paper. Research limitations/implications: When the component lead top is fully covered by the soldering, the implemented methodologies will not be able to identify the actual location of it. In such a case, if the segmented and detected lead top locations are different, a decision is made based on the direction in which the solder iron tip touches the solder pad. Practical implications: The methodologies presented in this paper can be effectively used to have a precise localization of component lead top inside the solder joint. The precise identification of component lead top leads to have a very precise quality assurance capability to the implemented AOI system. Originality/value: This research proposes a novel approach to identify soldering defects of THT solder joints in a much efficient way based on the component lead top. The value of this paper is quite high, since we have taken all the possibilities that may appear on a solder joint in a practical environment.

7

Novel visual object descriptor using surf and clustering algorithms

Grycuk R.

Journal of Applied Mathematics and Computational Mechanics

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2016

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Vol. 15, nr 3

37--46

EN

In this paper we propose a method for object description based on two wellknown clustering algorithms (k-means and mean shift) and the SURF method for keypoints detection. We also perform a comparison of these clustering methods in object description area. Both of these algorithms require one input parameter; k-means (k, number of objects) and mean shift (h, window). Our approach is suitable for images with a non-homogeneous background thus, the algorithm can be used not only on trivial images. In the future we will try to remove non-important keypoints detected by the SURF algorithm. Our method is a part of a larger CBIR system and it is used as a preprocessing stage.

8

Vision analysis system for autonomous landing of micro drone

Skoczylas M.

Acta Mechanica et Automatica

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2014

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Vol. 8, no. 4

199--203

EN

This article describes a concept of an autonomous landing system of UAV (Unmanned Aerial Vehicle). This type of device is equipped with the functionality of FPV observation (First Person View) and radio broadcasting of video or image data. The problem is performance of a system of autonomous drone landing in an area with dimensions of 1m × 1m, based on CCD camera coupled with an image transmission system connected to a base station. Captured images are scanned and landing marker is detected. For this purpose, image features detectors (such as SIFT, SURF or BRISK) are utilized to create a database of keypoints of the landing marker and in a new image keypoints are found using the same feature detector. In this paper results of a framework that allows detection of definedmarker for the purpose of drone landing field positioning will be presented.

9

Aspekty czasowe algorytmu SURF w wersji sekwencyjnej i równoległej zaimplementowanej w technologii CUDA

Szymczyk M., Szymczyk P.

Pomiary Automatyka Robotyka

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2011

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R. 15, nr 12

241-243

PL

W artykule przedstawiono wyniki prac, których celem było zbadanie możliwości implementacji algorytmu wyznaczania punktów charakterystycznych za pomocą metody SURF na platformie CUDA oraz porównanie czasów obliczeń sekwencyjnej i równoległej implementacji tego algorytmu.

EN

This article presents results of our work concerned possibility of implementation of algorithm for assigning key points using SURF algorithm and CUDA technology. The work also compares time of execution of these applications.