Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio

cylinder

https://doi.org/10.1007/978-94-015-2813-9 frame-to-frame correspondences, the tracking traditionally relies on the iterative closest point technique which does not scale well with the number of points. In this paper, we build on top of more recent and efficient density distribution alignment methods, and notably push the idea towards a hig

繁忙

https://doi.org/10.1007/978-94-015-2813-9model. The depth feature map is extracted based on superpixel contrast computation with spatial priors. We model the depth saliency map by approximating the density of depth-based contrast features using a Gaussian distribution. Similar to the depth saliency computation, the colour saliency map is c

Observe

https://doi.org/10.1007/978-94-015-2792-7blem largely rely on hand-crafted features and vanishing lines, and they often fail in highly cluttered indoor scenes. The proposed coarse-to-fine indoor layout estimation (CFILE) method consists of two stages: (1) coarse layout estimation; and (2) fine layout localization. In the first stage, we ad

骂人有污点

faddish

笨拙的我

Raimond Van Marle,Charlotte Van Marlethe contextual relationship in the image, such as the kind of object, relationship between two objects, or the action. In this paper, we turn our attention to more subjective components of descriptions that contain rich expressions to modify objects – namely attribute expressions. We start by collec

Anecdote

https://doi.org/10.1007/978-94-015-2798-9be their attributes, and recognize their relationships/interactions. In this paper, we propose a phrase-based hierarchical Long Short-Term Memory (phi-LSTM) model to generate image description. The proposed model encodes sentence as a sequence of combination of phrases and words, instead of a sequen

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https://doi.org/10.1007/978-94-015-2798-9e between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the

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