In this paper, we propose a salient region detection method with multi-feature fusion and edge constraint. First, an image feature extraction and fusion network based on dense connection structure and multi-channel convolution channel is designed. Then, a multi-scale atrous convolution block is applied to enlarge reception field. Finally, to increase accuracy, a combined loss function including classified loss and edge loss is built for multi-task training. Experimental results verify the effectiveness of the proposed method.

Salient region detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the multi-feature fusion network (MFFN) - a efficient salient region detection architecture based on Convolution Neural Network (CNN). A novel feature extraction structure is designed to obtain feature maps from CNN. A fusion dense block is used to fuse all low-level and high-level feature maps to derive salient region results. MFFN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that MFFN achieves the state-of-the-art performance on salient region detection and requires much less parameters and computation time. Ablation experiments demonstrate the effectiveness of each module in MFFN.

Labeling a salient region accurately in video with cluttered background and complex motion condition is still a challenging work. Most existing video salient region detection models mainly extract the stimulus-driven saliency features to detect the salient region in video. They are easily influenced by the cluttered background and complex motion conditions. It may lead to incomplete or wrong detection results. In this paper, we propose a video salient region detection framework by fusing the stimulus-driven saliency features and spatiotemporal consistency cue to improve the performance of detection under these complex conditions. On one hand, stimulus-driven spatial saliency features and temporal saliency features are extracted effectively to derive the initial spatial and temporal salient region map. On the other hand, in order to make use of the spatiotemporal consistency cue, an effective spatiotemporal consistency optimization model is presented. We use this model optimize the initial spatial and temporal salient region map. Then the superpixel-level spatiotemporal salient region map is derived by optimizing the initial spatiotemporal salient region map. Finally, the pixel-level spatiotemporal salient region map is derived by solving a self-defined energy model. Experimental results on the challenging video datasets demonstrate that the proposed video salient region detection framework outperforms state-of-the-art methods.

Processes in image recognition include target detection and shape extraction. Active Net has been proposed as one of the methods for such processing. It treats the target detection in an image as an energy optimization problem. In this paper, a problem of the conventional Active Net is presented and the new Active Net is proposed. The new net is improved the ability for detecting a target. Finally, the validity of the proposed net is confirmed by experimental results.

In this paper, we propose a method to detect a road sign from a road scene image in the daytime. In order to utilize color feature of sign efficiently, color distribution of sign is examined, and then color similarity map is constructed. Additionally, color similarity shown on the map is incorporated into image energy of an active net model. A road sign is extracted as if it is wrapped up in an active net. Some experimental results obtained by applying an active net to images are presented.