16,913 research outputs found
Oriented Edge Forests for Boundary Detection
We present a simple, efficient model for learning boundary detection based on
a random forest classifier. Our approach combines (1) efficient clustering of
training examples based on simple partitioning of the space of local edge
orientations and (2) scale-dependent calibration of individual tree output
probabilities prior to multiscale combination. The resulting model outperforms
published results on the challenging BSDS500 boundary detection benchmark.
Further, on large datasets our model requires substantially less memory for
training and speeds up training time by a factor of 10 over the structured
forest model.Comment: updated to include contents of CVPR version + new figure showing
example segmentation result
Point-wise mutual information-based video segmentation with high temporal consistency
In this paper, we tackle the problem of temporally consistent boundary
detection and hierarchical segmentation in videos. While finding the best
high-level reasoning of region assignments in videos is the focus of much
recent research, temporal consistency in boundary detection has so far only
rarely been tackled. We argue that temporally consistent boundaries are a key
component to temporally consistent region assignment. The proposed method is
based on the point-wise mutual information (PMI) of spatio-temporal voxels.
Temporal consistency is established by an evaluation of PMI-based point
affinities in the spectral domain over space and time. Thus, the proposed
method is independent of any optical flow computation or previously learned
motion models. The proposed low-level video segmentation method outperforms the
learning-based state of the art in terms of standard region metrics
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.Comment: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference
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Object Proposals for Text Extraction in the Wild
Object Proposals is a recent computer vision technique receiving increasing
interest from the research community. Its main objective is to generate a
relatively small set of bounding box proposals that are most likely to contain
objects of interest. The use of Object Proposals techniques in the scene text
understanding field is innovative. Motivated by the success of powerful while
expensive techniques to recognize words in a holistic way, Object Proposals
techniques emerge as an alternative to the traditional text detectors.
In this paper we study to what extent the existing generic Object Proposals
methods may be useful for scene text understanding. Also, we propose a new
Object Proposals algorithm that is specifically designed for text and compare
it with other generic methods in the state of the art. Experiments show that
our proposal is superior in its ability of producing good quality word
proposals in an efficient way. The source code of our method is made publicly
available.Comment: 13th International Conference on Document Analysis and Recognition
(ICDAR 2015
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