36,082 research outputs found
Salient Region Segmentation
Saliency prediction is a well studied problem in computer vision. Early
saliency models were based on low-level hand-crafted feature derived from
insights gained in neuroscience and psychophysics. In the wake of deep learning
breakthrough, a new cohort of models were proposed based on neural network
architectures, allowing significantly higher gaze prediction than previous
shallow models, on all metrics.
However, most models treat the saliency prediction as a \textit{regression}
problem, and accurate regression of high-dimensional data is known to be a hard
problem. Furthermore, it is unclear that intermediate levels of saliency (ie,
neither very high, nor very low) are meaningful: Something is either salient,
or it is not.
Drawing from those two observations, we reformulate the saliency prediction
problem as a salient region \textit{segmentation} problem. We demonstrate that
the reformulation allows for faster convergence than the classical regression
problem, while performance is comparable to state-of-the-art.
We also visualise the general features learned by the model, which are showed
to be consistent with insights from psychophysics
Salient Region Segmentation
Saliency prediction is a well studied problem in
computer vision. Early saliency models were based on low-level
hand-crafted feature derived from insights gained in neuroscience
and psychophysics. In the wake of deep learning breakthrough,
a new cohort of models were proposed based on neural network
architectures, allowing significantly higher gaze prediction than
previous shallow models, on all metrics. However, most models
treat the saliency prediction as a regression problem, and accurate
regression of high-dimensional data is known to be a hard
problem. Furthermore, it is unclear that intermediate levels of
saliency (ie, neither very high, nor very low) are meaningful:
Something is either salient, or it is not. Drawing from those two
observations, we reformulate the saliency prediction problem as
a salient region segmentation problem. We demonstrate that the
reformulation allows for faster convergence than the classical
regression problem, while performance is comparable to stateof-the-art. We also visualise the general features learned by the
model, which are showed to be consistent with insights from
psychophysics.Engineering and Physical Sciences Research Council (EPSRC
Salient Region Segmentation
Saliency prediction is a well studied problem in
computer vision. Early saliency models were based on low-level
hand-crafted feature derived from insights gained in neuroscience
and psychophysics. In the wake of deep learning breakthrough,
a new cohort of models were proposed based on neural network
architectures, allowing significantly higher gaze prediction than
previous shallow models, on all metrics. However, most models
treat the saliency prediction as a regression problem, and accurate
regression of high-dimensional data is known to be a hard
problem. Furthermore, it is unclear that intermediate levels of
saliency (ie, neither very high, nor very low) are meaningful:
Something is either salient, or it is not. Drawing from those two
observations, we reformulate the saliency prediction problem as
a salient region segmentation problem. We demonstrate that the
reformulation allows for faster convergence than the classical
regression problem, while performance is comparable to stateof-the-art. We also visualise the general features learned by the
model, which are showed to be consistent with insights from
psychophysics.Engineering and Physical Sciences Research Council (EPSRC
Instance-Level Salient Object Segmentation
Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distinct object instance labels for an input image. Our method consists of
three steps, estimating saliency map, detecting salient object contours and
identifying salient object instances. For the first two steps, we propose a
multiscale saliency refinement network, which generates high-quality salient
region masks and salient object contours. Once integrated with multiscale
combinatorial grouping and a MAP-based subset optimization framework, our
method can generate very promising salient object instance segmentation
results. To promote further research and evaluation of salient instance
segmentation, we also construct a new database of 1000 images and their
pixelwise salient instance annotations. Experimental results demonstrate that
our proposed method is capable of achieving state-of-the-art performance on all
public benchmarks for salient region detection as well as on our new dataset
for salient instance segmentation.Comment: To appear in CVPR201
An improved image segmentation algorithm for salient object detection
Semantic object detection is one of the most important and challenging problems in image analysis. Segmentation is an optimal approach to detect salient objects, but often fails to generate meaningful regions due to over-segmentation. This paper presents an improved semantic segmentation approach which is based on JSEG algorithm and utilizes multiple region merging criteria. The experimental results demonstrate that the proposed algorithm is encouraging and effective in salient object detection
Salient region detection using contrast-based saliency and watershed segmentation
Salient region detection is useful for many applications such as image segmentation, compression, image retrieval, object tracking, and machine vision systems.In this paper, an approach to detect salient regions in a visual scene using contrast-based saliency and watershed segmentation is presented.The approach allows salient objects to be detected and extracted for analysis while preserving the actual boundaries of the salient objects. The approach can be executed in parallel making it efficient for real time applications
Hierarchical Salient Object Detection for Assisted Grasping
Visual scene decomposition into semantic entities is one of the major
challenges when creating a reliable object grasping system. Recently, we
introduced a bottom-up hierarchical clustering approach which is able to
segment objects and parts in a scene. In this paper, we introduce a transform
from such a segmentation into a corresponding, hierarchical saliency function.
In comprehensive experiments we demonstrate its ability to detect salient
objects in a scene. Furthermore, this hierarchical saliency defines a most
salient corresponding region (scale) for every point in an image. Based on
this, an easy-to-use pick and place manipulation system was developed and
tested exemplarily.Comment: Accepted for ICRA 201
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