807 research outputs found
A generic tool for interactive complex image editing
Plenty of complex image editing techniques require certain per-pixel property or magnitude to be known, e.g., simulating depth of field effects requires a depth map. This work presents an efficient interaction paradigm that approximates any per-pixel magnitude from a few user strokes by propagating the sparse user input to each pixel of the image. The propagation scheme is based on a linear least-squares system of equations which represents local and neighboring restrictions over superpixels. After each user input, the system responds immediately, propagating the values and applying the corresponding filter. Our interaction paradigm is generic, enabling image editing applications to run at interactive rates by changing just the image processing algorithm, but keeping our proposed propagation scheme. We illustrate this through three interactive applications: depth of field simulation, dehazing and tone mapping
MiniNet: An Efficient Semantic Segmentation ConvNet for Real-Time Robotic Applications
Efficient models for semantic segmentation, in terms of memory, speed, and computation, could boost many robotic applications with strong computational and temporal restrictions. This article presents a detailed analysis of different techniques for efficient semantic segmentation. Following this analysis, we have developed a novel architecture, MiniNet-v2, an enhanced version of MiniNet. MiniNet-v2 is built considering the best option depending on CPU or GPU availability. It reaches comparable accuracy to the state-of-the-art models but uses less memory and computational resources. We validate and analyze the details of our architecture through a comprehensive set of experiments on public benchmarks (Cityscapes, Camvid, and COCO-Text datasets), showing its benefits over relevant prior work. Our experiments include a sample application where these models can boost existing robotic applications
Performance of object recognition in wearable videos
Wearable technologies are enabling plenty of new applications of computer
vision, from life logging to health assistance. Many of them are required to
recognize the elements of interest in the scene captured by the camera. This
work studies the problem of object detection and localization on videos
captured by this type of camera. Wearable videos are a much more challenging
scenario for object detection than standard images or even another type of
videos, due to lower quality images (e.g. poor focus) or high clutter and
occlusion common in wearable recordings. Existing work typically focuses on
detecting the objects of focus or those being manipulated by the user wearing
the camera. We perform a more general evaluation of the task of object
detection in this type of video, because numerous applications, such as
marketing studies, also need detecting objects which are not in focus by the
user. This work presents a thorough study of the well known YOLO architecture,
that offers an excellent trade-off between accuracy and speed, for the
particular case of object detection in wearable video. We focus our study on
the public ADL Dataset, but we also use additional public data for
complementary evaluations. We run an exhaustive set of experiments with
different variations of the original architecture and its training strategy.
Our experiments drive to several conclusions about the most promising
directions for our goal and point us to further research steps to improve
detection in wearable videos.Comment: Emerging Technologies and Factory Automation, ETFA, 201
Event Transformer+. A multi-purpose solution for efficient event data processing
Event cameras record sparse illumination changes with high temporal
resolution and high dynamic range. Thanks to their sparse recording and low
consumption, they are increasingly used in applications such as AR/VR and
autonomous driving. Current top-performing methods often ignore specific
event-data properties, leading to the development of generic but
computationally expensive algorithms, while event-aware methods do not perform
as well. We propose Event Transformer+, that improves our seminal work evtprev
EvT with a refined patch-based event representation and a more robust backbone
to achieve more accurate results, while still benefiting from event-data
sparsity to increase its efficiency. Additionally, we show how our system can
work with different data modalities and propose specific output heads, for
event-stream predictions (i.e. action recognition) and per-pixel predictions
(dense depth estimation). Evaluation results show better performance to the
state-of-the-art while requiring minimal computation resources, both on GPU and
CPU
Incremental Learning of Object Models From Natural Human-Robot Interactions
In order to perform complex tasks in realistic human environments, robots need to be able to learn new concepts in the wild, incrementally, and through their interactions with humans. This article presents an end-to-end pipeline to learn object models incrementally during the human-robot interaction (HRI). The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models. Throughout the article, we highlight the main challenges associated with this problem, such as high degree of occlusion and clutter, domain change, low-resolution data, and interaction ambiguity. This article shows the benefits of using multiview approaches and combining visual and language features, and our experimental results outperform standard baselines
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