Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

Li, Jian; Levine, Martin D.; An, Xiangjing; Xu, Xin; He, Hangen

doi:10.1109/tpami.2012.147

Cited by 538 publications

(402 citation statements)

References 36 publications

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“…Although, the research on the subject has begun recently at home, but from the published papers, it can be seen that the research is being conducted similar to the foreign level with all kinds of new ideas. Moreover, new algorithm has been put forward, and the theory system of infrared dim target detection has been enriched and perfected [19,20].…”

Section: Current Situation Of Infrared Small Target Detectionmentioning

confidence: 99%

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Yang¹

2015

TOAUTOCJ

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Infrared dim target detection and tracking is widely used in precision guidance, automatic control and other fields. As the small target imaging system has far distance, small area, low SNR infrared characteristics and undergoes image preprocessing, dim target detection technology is more important and more difficult. As a full play of the advantages of infrared small target detection technique has been provided, this has significance in infrared imaging. Based on introducing the tracking technology development status at home and abroad by the study of small target detection, the problems of detecting and tracking small moving target in infrared image sequence of background noise and interference conditions, aiming at dynamic planning in energy diffusion problem are presented. The improved algorithm of dynamic programming is also presented including weighted dynamic programming algorithm and the realization steps of the algorithm. Detection structure and the experimental results are analyzed. The results show that the algorithm has a good effect. In the end, this paper studies the rejected false targets in IR image track association method to obtain real object trajectories for moving small target detection, so as to improve the detection rate and reduce the false alarm rate.

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Section: Current Situation Of Infrared Small Target Detectionmentioning

confidence: 99%

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Yang¹

2015

TOAUTOCJ

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“…Frequency domain was used to extract the saliency of the objects by many literature such as (Bruce, et al, 2007), (Hou & Zhang, 2007), (Li, et al, 2007), (Achanta, et al, 2009), (Zhou, et al, 2010), and (Fang, et al, 2012). The idea behind using the frequency domain is that, they considered that salient points usually of higher change in frequency domain both in magnitude and in orientation.…”

Section: Previous Workmentioning

confidence: 99%

“…The algorithm has been applied on images from different datasets that are available online for saliency tests, such as the datasets in (Liu, 2007), (Judd, 2012), (Achanta, 2010), (Li, 2010), in additional to the HFT (Li, et al, 2007), Itti (Itti, et al, 1998), and MSSS (Achanta & Susstrunk, 2010 ).…”

Section: ≤ ≤ 100mentioning

confidence: 99%

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Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

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IntroductionSalient points, objects, or regions extraction is still a challenging topic of research and its definition is still very general. Different literature have defined the saliency in different ways. In general and based on human vision system HVS, the salient object is the object that may capture the attention or attract the HVS. HVS uses two stages to identify the objects, preattentive and attentive stages (Kadir & Brady, 2001).In pre-attentive stage, the local regions of image that present spatial discontinuity are detected. In the attentive stage, relationships between these regions are found, and grouped together. The rest of the paper will be organized as follows: previous work is presented in section 2. In Section 3, we will explain the proposed saliency extraction technique based on the irregularity of the regions in an image. In section 44, a new automatic thresholding technique will be proposed. Points clustering and region merging will be discussed in section 5. Section 6 will review the saliency evaluation technique and introduce a new method that is suitable to the application at hand. In section 7 we will discuss the experimental results and the optimum window size selection. Finally, conclusions will be derived in section 8. 2 Previous WorkWavelet was used by many authors such as in (Loupias, et al., 2000), (Tian, et al., 2001), (Song, et al., 2006), (Lin & Yang, 2007), and (Arivazhagan & Shebiah, 2009) In this paper, we shall introduce a robust and general saliency extraction method that is fully automated and does not need any intervention from the user. In addition, it does need any pre-knowledge about the image. Irregularity as a Measure of SaliencyIn this Section, we shall discuss the proposed technique. First, we shall define the saliency as any irregular region in the image with nature differs from the nature of the image as a whole; for example, a region with texture in a uniform region, or vice versa, thus we shall define irregularity as the main measure of saliency.Let ℐ( , ) be the image intensity at position ( , ), where = 1, 2, … . , , = 1,2, … , , with and are the image width and height respectively. The image is represented as a set of pixels and is defined as:Where p xy is the pixel's value at location ( , ) and ℕ is the set of natural numbers. Furthermore, we shall define the set ℙ = { | ⊆ } as the power set of .According to the application at hands, the main constrain is that; all the elements of a given subset should belong to a connected region which means they should be in a given neighbouring area.The sub-image, which we shall refer to as a region, is a subset of the image with elements that belong to the same region. The size of this sub-image is × ℎ.Considering that the regions are disjoint, they can be defined as:Assuming that there is no overlapping between the adjacent regions, the total number of regions is equal to × ℎ . In most cases there should be overlapping between the moving windows, if we denote the overlapping value by , then the total numbers of wind...

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“…Conventional small target detection methods such as top-hat filter [2], max-mean/max-median filter [3] and high-pass filters based on least spuares support vector machine (LS-SVM) [4] are widely used to reduce the background clutters. In recent years, a series of simple and fast algorithm based on Fourier transform was proposed, such as spectral residual (SR) [5], phase spectrum of Fourier transform (PFT) [6], hypercomplex Fourier transform (HFT) [7]. With regards to small target detection, frequency domain method is quite different from other methods.…”

Section: Introductionmentioning

confidence: 99%

Target Detection Algorithm Based on Two Layers Human Visual System

et al. 2015

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Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.

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Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

Cited by 538 publications

References 36 publications

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Irregularity-based image regions saliency identification and evaluation

Target Detection Algorithm Based on Two Layers Human Visual System

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