- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Focal Boundary Guided Salient Object Detection
摘要： The performance of salient object segmentation has been significantly advanced by using deep convolutional networks. However, these networks often produce blob-like saliency maps without accurate object boundaries. This is caused by the limited spatial resolution of their feature maps after multiple pooling operations, and might hinder downstream applications that require precise object shapes. To address this issue, we propose a novel deep model—Focal Boundary Guided (Focal-BG) network. Our model is designed to jointly learn to segment salient object masks and detect salient object boundaries. Our key idea is that additional knowledge about object boundaries can help to precisely identify the shape of the object. Moreover, our model incorporates a refinement pathway to refine the mask prediction, and makes use of the focal loss to facilitate the learning of the hard boundary pixels. To evaluate our model, we conduct extensive experiments. Our Focal-BG network consistently outperforms state-of-the-art methods on five major benchmarks. We provide a detailed analysis of these results and demonstrate that our joint modeling of salient object boundary and mask helps to better capture shape details, especially in the vicinity of object boundaries.
关键词： Salient Object Segmentation,Deep Learning,Visual Saliency Detection,Boundary Detection
更新于2025-09-23 15:23:52
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Saliency-Guided Stereo Camera Control for Comfortable VR Explorations
摘要： The quality of visual comfort and depth perception is a crucial requirement for virtual reality (VR) applications. This paper investigates major causes of visual discomfort and proposes a novel virtual camera controlling method using visual saliency to minimize visual discomfort. We extract the saliency of each scene and properly adjust the convergence plane to preserve realistic 3D effects. We also evaluate the effectiveness of our method on free-form architecture models. The results indicate that the proposed saliency-guided camera control is more comfortable than typical camera control and gives more realistic depth perception.
关键词： visual comfort,virtual reality exploration,stereo 3D,visual saliency
更新于2025-09-23 15:21:01
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Convolutional Neural Network for Blind Mesh Visual Quality Assessment Using 3D Visual Saliency
摘要： In this work, we propose a convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes without having access to the reference. The proposed CNN architecture is fed by small patches selected carefully according to their level of saliency. To do so, the visual saliency of the 3D mesh is computed, then we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Afterward, the obtained views are split to obtain 2D small patches that pass through a saliency filter to select the most relevant patches. Experiments are conducted on two MVQ assessment databases, and the results show that the trained CNN achieves good rates in terms of correlation with human judgment.
关键词： blind mesh visual quality assessment,Convolutional neural network (CNN),mesh visual saliency
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Attention Prediction Using Partial Differential on Color Spaces
摘要： Human attention prediction has attracted significant interest in recent years due to its promising contributions to various computer vision applications. In this paper, we present a simple and effective framework for extracting meaningful features from the color space. The feature extraction is based on computing the second order partial differential for the input image. The features extracted from the RGB and LUV spaces are combined to form a saliency map of natural images. Based on the public dataset used in previous works, we compare our proposed algorithm with several competitive approaches presented in the literature and the results demonstrate that the proposed method is effective. We also apply the feature based on the LUV space to improve some of the previous approaches, and our experimental results show that the application is meaningful for enhancing the accuracy of human attention prediction.
关键词： saliency map,second order partial differential,visual saliency
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Target detection in remote sensing image based on saliency computation of spiking neural network
摘要： Target detection is a-priori conditions for target tracking, classification, recognition, and scene understanding in Remote Sensing Image (RSI) analysis. However, the many traditional algorithms for target detection cannot perform well when the image resolution, especially for high-resolution RSIs, is change. Therefore, in this paper, we introduce a novel target detection algorithm based on the visual saliency of Spiking Neural Networks (SNN), which can efficiently detect the discriminative information from high-resolution RSIs to find targets by a saliency computing. As a result of this, it can provide an efficient and fast calculation method. The proposed visual saliency algorithm was applied to extensive experiments to detect the ship, and experimental results showed the outstanding performance for target detection on the optical RSI and synthetic aperture image.
关键词： visual saliency,target detection,spiking neural network,high-resolution RSI,saliency computation
更新于2025-09-09 09:28:46
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Dim and small infrared target fast detection guided by visual saliency
摘要： In order to detect dim and small infrared targets from a mass of high-resolution images of omni-directional Infrared Search and Track (IRST) systems rapidly and accurately, a fast target detection method guided by visual saliency (TDGS) is proposed. In this method, a coarse-to-fine detection strategy is used. First, in the stage of coarse-detection, according to the differences of global features between targets and backgrounds, a global saliency model based on fast spectral scale space (FSSS) is constructed to suppress complex background regions rapidly. And visual salient regions which contain dim and small targets are extracted from the original image. Then, in the stage of fine-detection, according to differences of local contrast between targets and background, an adaptive local contrast method (ALCM) is applied to finely improve contrast of targets in visual salient regions. Candidate targets can be further extracted through the adaptive threshold segmentation. Finally, dim and small targets are detected by their temporal relativity in multi-frames. Experimental results on four typical image sequences have indicated that the proposed method can not only detect dim and small infrared targets with small amount of computation, high detection probability, and low false alarm rate, but also adapt to various complex backgrounds. It is suitable for dim and small targets detection in omni-directional IRST systems and other practical applications.
关键词： Visual saliency,Coarse-to-fine detection strategy,Dim and small infrared target,Fast detection,Complex backgrounds
更新于2025-09-09 09:28:46