Homography (computer vision)

Résumé

In the field of computer vision, any two images of the same planar surface in space are related by a homography (assuming a pinhole camera model). This has many practical applications, such as , , or camera motion—rotation and translation—between two images. Once camera resectioning has been done from an estimated homography matrix, this information may be used for navigation, or to insert models of 3D objects into an image or video, so that they are rendered with the correct perspective and appear to have been part of the original scene (see Augmented reality). 3D plane to plane equation We have two cameras a and b, looking at points P_i in a plane. Passing from the projection {}^bp_i=\left({}^bu_i;{}^bv_i;1\right) of P_i in b to the projection {}^ap_i=\left({}^au_i;{}^av_i;1\right) of P_i in a: : {}^ap_i = \frac{{}^bz_i}{{}^az_i}K_a \cdot H_{ab} \cdot K_b^{-1} \cdot {}^bp_i where {}

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https://en.wikipedia.org/wiki/Homography_(computer_vision)

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Towards Modelling Of Visual Saliency In Point Clouds For Immersive Applications

Evangelos Alexiou, Touradj Ebrahimi, Peisen Xu

Modelling human visual attention is of great importance in the field of computer vision and has been widely explored for 3D imaging. Yet, in the absence of ground truth data, it is unclear whether such predictions are in alignment with the actual human viewing behavior in virtual reality environments. In this study, we work towards solving this problem by conducting an eye-tracking experiment in an immersive 3D scene that offers 6 degrees of freedom. A wide range of static point cloud models is inspected by human subjects, while their gaze is captured in real-time. The visual attention information is used to extract fixation density maps, that can be further exploited for saliency modelling. To obtain high quality fixation points, we devise a scheme that utilizes every recorded gaze measurement from the two eye-cameras of our set-up. The obtained fixation density maps together with the recorded gaze and head trajectories are made publicly available, to enrich visual saliency datasets for 3D models.

IEEE

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2019

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Double Refinement Network for Efficient Monocular Depth Estimation

Nikita Durasov

Monocular depth estimation is the task of obtaining a measure of distance for each pixel using a single image. It is an important problem in computer vision and is usually solved using neural networks. Though recent works in this area have shown significant improvement in accuracy, the state-of-the-art methods tend to require massive amounts of memory and time to process an image. The main purpose of this work is to improve the performance of the latest solutions with no decrease in accuracy. To this end, we introduce the Double Refinement Network architecture. The proposed method achieves state-of-the-art results on the standard benchmark RGB-D dataset NYU Depth v2, while its frames per second rate is significantly higher (up to 18 times speedup per image at batch size 1) and the RAM usage is lower.

IEEE2019

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