6,991 research outputs found
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
Automated detection of extended sources in radio maps: progress from the SCORPIO survey
Automated source extraction and parameterization represents a crucial
challenge for the next-generation radio interferometer surveys, such as those
performed with the Square Kilometre Array (SKA) and its precursors. In this
paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source
Automated Recognition), to detect and parametrize extended sources in radio
interferometric maps. It is based on a pre-filtering stage, allowing image
denoising, compact source suppression and enhancement of diffuse emission,
followed by an adaptive superpixel clustering stage for final source
segmentation. A parameterization stage provides source flux information and a
wide range of morphology estimators for post-processing analysis. We developed
CAESAR in a modular software library, including also different methods for
local background estimation and image filtering, along with alternative
algorithms for both compact and diffuse source extraction. The method was
applied to real radio continuum data collected at the Australian Telescope
Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU
survey. The source reconstruction capabilities were studied over different test
fields in the presence of compact sources, imaging artefacts and diffuse
emission from the Galactic plane and compared with existing algorithms. When
compared to a human-driven analysis, the designed algorithm was found capable
of detecting known target sources and regions of diffuse emission,
outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
TThe goal of our work is to discover dominant objects in a very general
setting where only a single unlabeled image is given. This is far more
challenge than typical co-localization or weakly-supervised localization tasks.
To tackle this problem, we propose a simple but effective pattern mining-based
method, called Object Location Mining (OLM), which exploits the advantages of
data mining and feature representation of pre-trained convolutional neural
networks (CNNs). Specifically, we first convert the feature maps from a
pre-trained CNN model into a set of transactions, and then discovers frequent
patterns from transaction database through pattern mining techniques. We
observe that those discovered patterns, i.e., co-occurrence highlighted
regions, typically hold appearance and spatial consistency. Motivated by this
observation, we can easily discover and localize possible objects by merging
relevant meaningful patterns. Extensive experiments on a variety of benchmarks
demonstrate that OLM achieves competitive localization performance compared
with the state-of-the-art methods. We also evaluate our approach compared with
unsupervised saliency detection methods and achieves competitive results on
seven benchmark datasets. Moreover, we conduct experiments on fine-grained
classification to show that our proposed method can locate the entire object
and parts accurately, which can benefit to improving the classification results
significantly
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