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Salient object detection has been greatly boosted thanks to the deep convolutional neural networks (CNN), especially fully convolutional neural networks (FCN). Nowadays, it is possible to train an end-to-end deep model for salient object detection. However, the diverse scales of salient objects still pose major challenges for these state-of-the-art methods. In this paper, we investigate how different...
As a special task beyond general object detection, pedestrian detection has attracted much attention in recent years. Despite the significant improvements, detecting pedestrians is still a challenging task, especially for small-size pedestrians. In this paper, we present a multi-scale feature fusion convolutional neural network (MFF-CNN) for pedestrian detection. The MFF-CNN is benchmarked on three...
A key problem in salient object detection is how to effectively exploit the multi-level saliency cues in a unified and data-driven manner. In this paper, building upon the recent success of deep neural networks, we propose a fully convolutional neural network based approach empowered with multi-level fusion to salient object detection. By integrating saliency cues at different levels through fully...
Artificial awareness is an interesting way of realizing artificial intelligent perception for machines. Since the foreground object can provide more useful information for perception and informative description of the environment than background regions, the informative saliency characteristics of the foreground object can be treated as a important cue of the objectness property. Thus, a sparse reconstruction...
In this paper, we propose a new unsupervised text detection approach which is based on Histogram of Oriented Gradient and Graph Spectrum. By investigating the properties of text edges, the proposed approach first extracts text edges from an image and localize candidate character blocks using Histogram of Oriented Gradients, then Graph Spectrum is utilized to capture global relationship among candidate...
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