Semantic Segmentation of Static and Dynamic Structures of Marina Satellite Images using Deep Learning

Santos, Matheus Machado dos; Giacomo, Giovanni G. De; Drews, Paulo; Botelho, Silvia S. C.

doi:10.1109/bracis.2019.00129

Cited by 5 publications

(3 citation statements)

References 15 publications

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Section: Resultsmentioning

confidence: 99%

“…To study the effectiveness of the method the test database from [27] was chosen. Examples of segmentation on the test database are shown in Figure 4 (a-input image, b-ground truth, c-segmentation result of the method [27], d-segmentation result of the proposed method). The color representation of the segmented images is green for the static structures, red for the dynamic structures and blue for the water.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Video segmentation on static and dynamic textures using a quaternion framework

Zelensky

Voronin

Zhdanova

et al. 2022

Artificial Intelligence and Machine Learning in Defense Applications IV

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This paper presents a new method for video segmentation using deep learning neural networks in the quaternion space into sets of objects, background, static and dynamic textures. We introduce a novel quaternionic anisotropic gradient (QAG) which can combine the color channels and the orientations in the image plane. The local polynomial estimates and the ICI rule are used for QAG calculation. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. Using the QAGs, we extract the local orientation information in the color images. Second, to improve the segmentation result we applied neural network to this derived orientation information. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. Experimental comparisons to stateof-the-art video segmentation methods demonstrate the effectiveness of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Video segmentation on static and dynamic textures using a quaternion framework

Zelensky

Voronin

Zhdanova

et al. 2022

Artificial Intelligence and Machine Learning in Defense Applications IV

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show abstract

“…2-A. The process is better explored and better described in [29]. Movable objects such as boats cannot be trusted on the localization problem.…”

Section: A Aerial Image Processingmentioning

confidence: 99%

Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images

Santos¹,

Giacomo²,

Drews-Jr³

et al. 2022

Preprint

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Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites. This study expands the cross-view image match idea and proposes a cross-domain and cross-view localization framework. The method identifies the correlation between color aerial images and underwater acoustic images to improve the localization of underwater vehicles that travel in partially structured environments such as harbors and marinas. The approach is validated on a real dataset acquired by an underwater vehicle in a marina. The results show an improvement in the localization when compared to the dead reckoning of the vehicle.

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