Fast and faithful scale-aware image filters

This paper proposes a fast and accurate computational framework for scale-aware image filters. Our framework is based on accurately approximating $$L^{1}$$ L 1 Gaussian convolution with respect to a transformed pixel domain representing geodesic distance on a guidance image manifold in order to recover salient edges in a manner faithful to scale-space theory while removing small image structures. Our framework possesses linear computational complexity with high approximation precision. We examined it numerically in terms of speed, accuracy, and quality compared with conventional methods.

High-definition human visual attention mapping using wavelets

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

High-definition human visual attention mapping using wavelets

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.