Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting

Cai, Fei; Chen, Honghui; Ma, Jianwei

doi:10.5815/ijitcs.2011.02.02

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…The procedure is rep until the algorithm has converged, i.e., until the change of coefficients between two tions is no more than threshold. Authors in [36] proposed using an approach bas extracting texture and geometry structure features to detect objects like planes, tank vehicles in natural background using FCM. Object detection and recognition is a ve k (X) = p(k|X) in accordance with the Bayes' theorem that will be used in further calculations:…”

Section: Defining An Example Variablementioning

confidence: 99%

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Bistroń

Piotrowski

2021

Electronics

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The paper presents an overview of current and expected prospects for the development of artificial intelligence algorithms, especially in military applications, and conducted research regarding applications in the area of civilian life. Attention was paid mainly to the use of AI algorithms in cybersecurity, object detection, military logistics and robotics. It discusses the problems connected with the present solutions and how artificial intelligence can help solve them. It briefly presents also mathematical structures and descriptions for ART, CNN and SVM networks as well as Expectation–Maximization and Gaussian Mixture Model algorithms that are used in solving of discussed problems. The third chapter discusses the attitude of society towards the use of neural network algorithms in military applications. The basic problems related to ethics in the application of artificial intelligence and issues of responsibility for errors made by autonomous systems are discussed.

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Section: Defining An Example Variablementioning

confidence: 99%

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Bistroń

Piotrowski

2021

Electronics

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“…The dataset Seg-VHR-10 has been used to train the model, which is easier to segment and further detect object from Geospatial Images. Rafflesia Khan Rameswar Debnath in [22] where an efficient approach to detect fruits using a proposed Fruit Detection and Recognition Method (FDR) based on an improved Convolutional Neural Networks. Fei Cai et al in [23] to detect man-made objects based on Texture Clustering and Geometric Structure Feature Extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Underwater Fish Detection Using an Enhanced Convolutional Neural Network

Gomes¹,

Saif²

2021

IJIGSP

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Underwater Object Detection is one of the most challenging and unexplored domains in this area of Computer Vision. The proposed research refines the image enhancement of under-water imagery by proposing an improvement of already existing tools for underwater Object detection. The comparative study clearly depicts the enhancement of the proposed method with respect to the existing methods for underwater object detection. Moreover, a framework for detection of underwater organisms such as fishes are proposed, which will act as the steppingstone for reserving the ecosystem of the whole fish community. Mostly the object detection using deep learning has been the prime goal to this research and the comparison between other preexisting methods are compared at the end. As a result, techniques that are already well established will be used for overall enhancement of those images. Through this enhancement and through finding a healthy environment for their breeding ground, the extinction of selected species of fishes is can be diminished and decreased. All this is carried out by overcoming difficulties underwater through a novel technique that can be integrated into an Underwater Autonomous Vehicle and can be classified as robust in nature. Robustness will depend on three important factors in this research, first is accuracy, then fast and lastly being upgradeable. The proposed method is a modified VGGNet-16, which is trained using the ImageCLEF FISH_TS dataset. The overall result provides an accuracy of 96.4% which surpasses all its predecessors.

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“…Results are shown on only 21 high resolution images of optical band. Cai et al [13] designed a man-made object detection methodology by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected, by extracting texture and geometry structure features.…”

Section: Related Workmentioning

confidence: 99%

Detecting aircrafts from satellite images using saliency and conical pyramid based template representation

et al. 2016

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Automatic target localization in satellite images still remains as a challenging problem in the field of computer vision. The issues involved in locating targets in satellite images are viewpoint, spectral (intensity) and scale variations. Diversity in background texture and target clutter also adds up to the complexity of the problem of localizing aircrafts in satellite images. Failure of modern feature extraction and object detection methods highlight the complexity of the problem. In the proposed work, pre-processing techniques, viz. denoising and contrast enhancement, are first used to improve the quality of the images. Then, the concept of unsupervised saliency is used to detect the potential regions of interest, which reduces the search space. Parts from the salient regions are further processed using clustering and morphological processing to get the probable regions of isolated aircraft targets. Finally, a novel conical pyramid based framework for template representation of the target samples is proposed for matching. Experimental results shown on a few satellite images exhibit the superior performance of the proposed methods.

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Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting

Cited by 7 publications

References 25 publications

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Robust Underwater Fish Detection Using an Enhanced Convolutional Neural Network

Detecting aircrafts from satellite images using saliency and conical pyramid based template representation

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