eldarbaykiev

Eldar Baykiev's starred repositories

installation-guide-of-maskrcnn

Mask R-CNN creates a high-quality segmentation mask in addition to the Faster R-CNN network. In addition to class labels and scores, a segmentation mask is created for the objects detected by this neural network. In this repository, using Anaconda prompt step by step Mask R-CNN setup is shown.

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Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

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Mask-RCNN-TF2

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0

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FasterRCNN

Clean and readable implementations of Faster R-CNN in PyTorch and TensorFlow 2 with Keras.

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rsgislib-tutorials

A set of notebook tutorials for RSGISLib.

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Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

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Towards-measuring-shape-similarity-of-polygons-based-on-multiscale-features-and-grid-context-descrip

In spatial analysis application, measuring the shape similarity of polygons is crucial for polyg-onal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should be able to find out the difference of polygons as objects in observation in terms of visual perception and the difference of the region, boundary, and structure formed by the polygons from the mathematic point of view. In the existing approaches, shape similarity of polygons is calculated by only comparing their mathematic characteristics while not taking the human perception into consideration. Aiming to solve this problem, we use the features of con-text and texture of polygons, since they are basic visual perception elements to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into grid representation, the contour feature is represent-ed as a multiscale statistic feature, and the region feature is transformed into condensed grid context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes it as a correlation between textures extracted by shape feature. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor.

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