Performance Analysis of the Modified-Hybrid Optical Neural Network Object Recognition System Within Cluttered Scenes

Kypraios, Ioannis

doi:10.5772/45797

Cited by 1 publication

(3 citation statements)

References 46 publications

(37 reference statements)

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“…A problem solving system consists of a domain theory which specifies the task to be solved, the initial problem states and the targeted problem goals, and a control knowledge which guides the decision-making process. Thus, knowledge representation can have a direct effect to the efficiency of the problem solving process [14,15].…”

Section: Object Recognition Systems For Automated Image Analysismentioning

confidence: 99%

“…Instead, unsupervised learning can be used for discovering the underlying structure or pattern of the data. Thus, the term "clustering" refers to a process of grouping similar things together [14]. Therefore, unsupervised learning can be used for discovering such clusters in the input data.…”

Section: Unsupervised Clusteringmentioning

confidence: 99%

“…There is an increased level of difficulty in correctly classifying and performing speciation for endangered bird species 1 and 2 during the winter aerial surveys due to the higher similarity of the birds' plumage between species 1 and species 2. Therefore, we propose the design and development of a novel biologicallyinspired hybrid digital-optical system [14] for increasing the accuracy of the bird species speciation and the overall time it takes to process an aerial survey. As we are going to describe in the next sections, our proposed system is capable of performing both knowledge representation and knowledge learning by incorporating in its data: (i) the shape of each endangered species 1 and 2, (ii) the size of each endangered species 1 and 2, and (iii) the colour information of each endangered species 1 and 2.…”

Section: Biologically-inspired Hybrid Digital-optical Systemmentioning

confidence: 99%

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A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

Kypraios¹

2023

Vision Sensors - Recent Advances

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From coastal landscapes to biodiversity remote sensing can on the one hand capture all the natural heritage elements and on the other hand can help in maintaining protected species. In a typical remote sensing application, a few thousands of super high-resolution images are captured and need to be processed. The next step of the processing involves converting those images to an appropriate format for visual display of the data. Then, the image analyst needs to define the regions of interests (ROIs) in each captured image. Next, ROIs need to be defined for identifying specific objects or extracting the required information. First drawback of this processing cycle is the use of image analysis tools which provide them only with scaling or zooming features. Second, there is no conceptual connection between the image analysis tools and the actual processing cycle. Third, such existing tools do not usually automate any steps in the processing cycle. We combine an optical correlator with a supervised or an unsupervised classifier learning algorithm and show how our proposed novel cognitive architecture is conceptually connected with the image analysis processing cycle. We test the architecture with captured images and describe how it can automate the processing cycle.

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Section: Object Recognition Systems For Automated Image Analysismentioning

confidence: 99%

Section: Unsupervised Clusteringmentioning

confidence: 99%

Section: Biologically-inspired Hybrid Digital-optical Systemmentioning

confidence: 99%

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