10,023 research outputs found
Semantic image segmentation using morphological tools
PostprintIn this work, we study the extraction of semantic objects using morphological tools. We decompose the image into its level sets and level lines (the borders of the level sets). Specifically, from all the level lines we extract the ones that contain T-junctions, have compact form, and are wellcontrasted to obtain the semantic objects in the scene. For all these factors, we establish an estimation procedure
A video object generation tool allowing friendly user interaction
In this paper we describe an interactive video object segmentation tool developed in the framework of the ACTS-AC098 MOMUSYS project. The Video Object Generator with User Environment (VOGUE) combines three different sets of automatic and semi-automatic-tool (spatial segmentation, object tracking and temporal segmentation) with general purpose tools for user interaction. The result is an integrated environment allowing the user-assisted segmentation of any sort of video sequences in a friendly and efficient manner.Peer ReviewedPostprint (published version
A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
In recent years, with the development of aerospace technology, we use more
and more images captured by satellites to obtain information. But a large
number of useless raw images, limited data storage resource and poor
transmission capability on satellites hinder our use of valuable images.
Therefore, it is necessary to deploy an on-orbit semantic segmentation model to
filter out useless images before data transmission. In this paper, we present a
detailed comparison on the recent deep learning models. Considering the
computing environment of satellites, we compare methods from accuracy,
parameters and resource consumption on the same public dataset. And we also
analyze the relation between them. Based on experimental results, we further
propose a viable on-orbit semantic segmentation strategy. It will be deployed
on the TianZhi-2 satellite which supports deep learning methods and will be
lunched soon.Comment: 8 pages, 3 figures, ICNC-FSKD 201
Dynamic Objects Segmentation for Visual Localization in Urban Environments
Visual localization and mapping is a crucial capability to address many
challenges in mobile robotics. It constitutes a robust, accurate and
cost-effective approach for local and global pose estimation within prior maps.
Yet, in highly dynamic environments, like crowded city streets, problems arise
as major parts of the image can be covered by dynamic objects. Consequently,
visual odometry pipelines often diverge and the localization systems
malfunction as detected features are not consistent with the precomputed 3D
model. In this work, we present an approach to automatically detect dynamic
object instances to improve the robustness of vision-based localization and
mapping in crowded environments. By training a convolutional neural network
model with a combination of synthetic and real-world data, dynamic object
instance masks are learned in a semi-supervised way. The real-world data can be
collected with a standard camera and requires minimal further post-processing.
Our experiments show that a wide range of dynamic objects can be reliably
detected using the presented method. Promising performance is demonstrated on
our own and also publicly available datasets, which also shows the
generalization capabilities of this approach.Comment: 4 pages, submitted to the IROS 2018 Workshop "From Freezing to
Jostling Robots: Current Challenges and New Paradigms for Safe Robot
Navigation in Dense Crowds
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages
Loyek C, Kölling J, Langenkämper D, Niehaus K, Nattkemper TW. A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama J, Bradley E, Hollmén J, eds. Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science. Vol 7014. Berlin, Heidelberg: Springer; 2011: 258-269
A generic news story segmentation system and its evaluation
The paper presents an approach to segmenting broadcast TV news programmes automatically into individual news stories. We first segment the programme into individual shots, and then a number of analysis tools are run on the programme to extract features to represent each shot. The results of these feature extraction tools are then combined using a support vector machine trained to detect anchorperson shots. A news broadcast can then be segmented into individual stories based on the location of the anchorperson shots within the programme. We use one generic system to segment programmes from two different broadcasters, illustrating the robustness of our feature extraction process to the production styles of different broadcasters
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