Salient Extraction Research Articles

Saliency-based relief generation

Relief is a special art form that differs from painting and other round sculpture, and is traditionally created by laborious hand carving. Existing methods for digital relief generation focus on direct geometric compression, which transforms a three-dimensional (3D) mesh into a detail-preserving surface with a shallow depth, indicating the presence of 3D figures. We propose to add saliency information into digital relief generation. Novel saliency extraction methods are introduced to preserve relief features, and then a non-linear boosting of details is adopted to generate the final relief models. This work seamlessly combines visual perception and geometrical processing.

가우스 가중치를 이용한 돌출 값 추정을 위한 방법

It is important work to extract saliency regions from an image as preprocessing for various image processing methods. In this paper, we introduce an improved method to estimate saliency value of each pixel from an image. The proposed method is an improved work of the previously studied method using color and statistical framework to estimate saliency values. At first, saliency value of each pixel is calculated using the local contrast of an image region at various scales and the most significant saliency pixel is determined using saliency value of each pixel. Then, saliency value of each pixel is again estimated using gauss weight with respect to the most significant saliency pixel and the saliency of each pixel is determined to calculate initial probability. At last, the saliency value of each pixel is calculated by Bayes` rule. The experiments show that our approach outperforms the current statistical based method.

시각집중과 평균이동 알고리즘을 이용한 선박 검출

In this paper, a video based ship detection method is proposed to monitor port efficiently. Visual saliency map algorithm and mean shift algorithm is applied to detect moving ships don`t include background information which is difficult to track moving ships. It is easy to detect ships at the port using saliency map algorithm, because it is very effective to extract saliency object from background. To remove background information in the saliency region, image segmentation and clustering using mean shift algorithm is used. As results of detecting simulation with images of a camera installed at the harbor, it is shown that the proposed method is effective to detect ships.

Micro Surface Defect Detection Method for Silicon Steel Strip Based on Saliency Convex Active Contour Model

Accurate detection of surface defect is an indispensable section in steel surface inspection system. In order to detect the micro surface defect of silicon steel strip, a new detection method based on saliency convex active contour model is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background for the purpose of highlighting the potential objects. The extracted saliency map is then exploited as a feature, which is fused into a convex energy minimization function of local-based active contour. Meanwhile, a numerical minimization algorithm is introduced to separate the micro surface defects from cluttered background. Experimental results demonstrate that the proposed method presents good performance for detecting micro surface defects including spot-defect and steel-pit-defect. Even in the cluttered background, the proposed method detects almost all of the microdefects without any false objects.

Fusion of visible and infrared images using saliency analysis and detail preserving based image decomposition

Image fusion for visible and infrared images is a significant task in image analysis. The target regions in infrared image and abundant detail information in visible image should be both extracted into the fused result. Thus, one should preserve or even enhance the details from original images in fusion process. In this paper, an algorithm using pixel value based saliency detection and detail preserving based image decomposition is proposed. Firstly, the multi-scale decomposition is constructed using weighted least squares filter for original infrared and visible images. Secondly, the pixel value based saliency map is designed and utilized for image fusion in different decomposition level. Finally, the fusion result is reconstructed by synthesizing different scales with synthetic weights. Since the information of original signals can be well preserved and enhanced with saliency extraction and multi scale decomposition process, the fusion algorithm performs robustly and excellently. The proposed approach is compared with other state-of the-art methods on several image sets to verify the effectiveness and robustness.

Real-time automatic small target detection using saliency extraction and morphological theory

We propose a real-time target detection approach in infrared imagery. This method combined saliency detection technology and morphological theory. A local frequency-tuned based saliency extraction technology is designed for highlighting the potential targets area. With saliency extraction and thresholding processing, the potential targets map is obtained, and then morphological operation is used for noise suppression. The experimental results prove the proposed approach is robust and efficient for target detection.

Detail enhanced multi-source fusion using visual weight map extraction based on multi scale edge preserving decomposition

Image fusion is an important problem in image analysis. The abundant detail information in multisource images should be preserved or even enhanced for the fused result. In this paper, using visual weight map extraction based on multiscale edge preserving decomposition, a details enhancing fusion algorithm is proposed. The saliency extraction based visual weight map is used for image fusion at different scales, which is decomposed by edge preserving image smooth approach. With this method, the details information which human visual system interests can be preserved and enhanced easily. The quantitative comparisons of experiments demonstrate that the proposed algorithm performs better than other methods, especially in visual effect and keeping details.

New strategy for image and video quality assessment

Image and video quality assessment (QA) is a critical issue in image and video processing applications. General full-reference (FR) QA criteria such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE) do not accord well with human subjective assessment. Some QA indices that consider human visual sensitivity, such as mean structural similarity (MSSIM) with structural sensitivity, visual information fidelity (VIF) with statistical sensitivity, etc., were proposed in view of the differences between reference and distortion frames on a pixel or local level. However, they ignore the role of human visual attention (HVA). Recently, some new strategies with HVA have been proposed, but the methods extracting the visual attention are too complex for real-time realization. We take advantage of the phase spectrum of quaternion Fourier transform (PQFT), a very fast algorithm we previously proposed, to extract saliency maps of color images or videos. Then we propose saliency-based methods for both image QA (IQA) and video QA (VQA) by adding weights related to saliency features to these original IQA or VQA criteria. Experimental results show that our saliency-based strategy can approach more closely to human subjective assessment compared with these original IQA or VQA methods and does not take more time because of the fast PQFT algorithm.

Enhancement of Perceptually Salient Contours using a Parallel Artificial Cortical Network

In this paper we present a parallel artificial cortical network inspired by the Human visual system, which enhances the salient contours of an image. The network consists of independent processing elements, which are organized into hypercolumns. They process concurrently the distinct orientations of all the edges of the image. These processing elements are a new set of orientation kernels appropriate for the discrete lattice of the hypercolumns. The Gestalt laws of proximity and continuity that describe the process of saliency extraction in the human brain are encoded by means of weights. These weights interconnect the kernels according to a novel connection pattern based on co-exponentiality. The output of every kernel is modulated by the outputs of its neighboring kernels, according to a new affinity function. This function takes into account the degree of difference between the facilitation of the two lobes of the kernel. Saliency enhancement results as a consequence of the local interactions between the kernels. The network was tested on real and synthetic images and displays promising results for both. Comparisons with other methods with the same scope, demonstrate that the proposed method performs adequately. Furthermore it exhibits O(N) complexity with execution times that have never been reported by any other method so far, even though it is executed on a conventional PC.