15,910 research outputs found
Accurate foreground segmentation without pre-learning
Foreground segmentation has been widely used in many computer vision applications. However, most of the existing methods rely on a pre-learned motion or background model, which will increase the burden of users. In this paper, we present an automatic algorithm without pre-learning for segmenting foreground from background based on the fusion of motion, color and contrast information. Motion information is enhanced by a novel method called support edges diffusion (SED) , which is built upon a key observation that edges of the difference image of two adjacent frames only appear in moving regions in most of the cases. Contrasts in background are attenuated while those in foreground are enhanced using gradient of the previous frame and that of the temporal difference. Experiments on many video sequences demonstrate the effectiveness and accuracy of the proposed algorithm. The segmentation results are comparable to those obtained by other state-of-the-art methods that depend on a pre-learned background or a stereo setup. © 2011 IEEE.published_or_final_versionThe 6th International Conference on Image and Graphics (ICIG 2011), Hefei, Anhui, China, 12-15 August 2011. In Proceedings of the 6th ICIG, 2011, p. 331-33
Reversible Recursive Instance-level Object Segmentation
In this work, we propose a novel Reversible Recursive Instance-level Object
Segmentation (R2-IOS) framework to address the challenging instance-level
object segmentation task. R2-IOS consists of a reversible proposal refinement
sub-network that predicts bounding box offsets for refining the object proposal
locations, and an instance-level segmentation sub-network that generates the
foreground mask of the dominant object instance in each proposal. By being
recursive, R2-IOS iteratively optimizes the two sub-networks during joint
training, in which the refined object proposals and improved segmentation
predictions are alternately fed into each other to progressively increase the
network capabilities. By being reversible, the proposal refinement sub-network
adaptively determines an optimal number of refinement iterations required for
each proposal during both training and testing. Furthermore, to handle multiple
overlapped instances within a proposal, an instance-aware denoising autoencoder
is introduced into the segmentation sub-network to distinguish the dominant
object from other distracting instances. Extensive experiments on the
challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of
R2-IOS over other state-of-the-art methods. In particular, the
over classes at IoU achieves , which significantly
outperforms the results of by PFN~\cite{PFN} and
by~\cite{liu2015multi}.Comment: 9 page
Emergence of Object Segmentation in Perturbed Generative Models
We introduce a novel framework to build a model that can learn how to segment
objects from a collection of images without any human annotation. Our method
builds on the observation that the location of object segments can be perturbed
locally relative to a given background without affecting the realism of a
scene. Our approach is to first train a generative model of a layered scene.
The layered representation consists of a background image, a foreground image
and the mask of the foreground. A composite image is then obtained by
overlaying the masked foreground image onto the background. The generative
model is trained in an adversarial fashion against a discriminator, which
forces the generative model to produce realistic composite images. To force the
generator to learn a representation where the foreground layer corresponds to
an object, we perturb the output of the generative model by introducing a
random shift of both the foreground image and mask relative to the background.
Because the generator is unaware of the shift before computing its output, it
must produce layered representations that are realistic for any such random
perturbation. Finally, we learn to segment an image by defining an autoencoder
consisting of an encoder, which we train, and the pre-trained generator as the
decoder, which we freeze. The encoder maps an image to a feature vector, which
is fed as input to the generator to give a composite image matching the
original input image. Because the generator outputs an explicit layered
representation of the scene, the encoder learns to detect and segment objects.
We demonstrate this framework on real images of several object categories.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS
2019), Spotlight presentatio
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