9,018 research outputs found
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Prototypicality effects in global semantic description of objects
In this paper, we introduce a novel approach for semantic description of
object features based on the prototypicality effects of the Prototype Theory.
Our prototype-based description model encodes and stores the semantic meaning
of an object, while describing its features using the semantic prototype
computed by CNN-classifications models. Our method uses semantic prototypes to
create discriminative descriptor signatures that describe an object
highlighting its most distinctive features within the category. Our experiments
show that: i) our descriptor preserves the semantic information used by the
CNN-models in classification tasks; ii) our distance metric can be used as the
object's typicality score; iii) our descriptor signatures are semantically
interpretable and enables the simulation of the prototypical organization of
objects within a category.Comment: Paper accepted in IEEE Winter Conference on Applications of Computer
Vision 2019 (WACV2019). Content: 10 pages (8 + 2 reference) with 7 figure
Procedural Historic Building Information Modelling (HBIM) For Recording and Documenting European Classical Architecture
Procedural Historic Building Information Modelling (HBIM) is a new approach for modelling historic buildings which develops full building information models from remotely sensed data. HBIM consists of a novel library of reusable parametric objects, based on historic architectural data and a system for mapping these library objects to survey data. Using concepts from procedural modelling, a new set of rules and algorithms have been developed to automatically combine HBIM library objects and generate different building arrangements by altering parameters. This is a semi-automatic process where the required building structure and objects are first automatically generated and then refined to match survey data.
The encoding of architectural rules and proportions into procedural modelling rules helps to reduce the amount of further manual editing that is required. The ability to transfer survey data such as building footprints or cut-sections directly into a procedural modelling rule also greatly reduces the amount of further editing required. These capabilities of procedural modelling enable a more automated and efficient overall workflow for reconstructing BIM geometry from point cloud data. This document outlines the research carried out to evaluate the suitability of a procedural modelling approach for improving the process of reconstructing building geometry from point clouds. To test this hypothesis, three procedural modelling prototypes were designed and implemented for BIM software. Quantitative accuracy testing and qualitative end-user scenario testing methods were used to evaluate the research hypothesis. The results obtained indicate that procedural modelling has potential for achieving more accurate, automated and easier generation of BIM geometry from point clouds
Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans
We propose an unsupervised method for parsing large 3D scans of real-world
scenes into interpretable parts. Our goal is to provide a practical tool for
analyzing 3D scenes with unique characteristics in the context of aerial
surveying and mapping, without relying on application-specific user
annotations. Our approach is based on a probabilistic reconstruction model that
decomposes an input 3D point cloud into a small set of learned prototypical
shapes. Our model provides an interpretable reconstruction of complex scenes
and leads to relevant instance and semantic segmentations. To demonstrate the
usefulness of our results, we introduce a novel dataset of seven diverse aerial
LiDAR scans. We show that our method outperforms state-of-the-art unsupervised
methods in terms of decomposition accuracy while remaining visually
interpretable. Our method offers significant advantage over existing
approaches, as it does not require any manual annotations, making it a
practical and efficient tool for 3D scene analysis. Our code and dataset are
available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parse
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