Salient Object Detection Via Deformed Smoothness Constraint

Wu, Xiyin; Ma, Xiaodi; Zhang, Jinxia; Wang, Andong

doi:10.1109/icip.2018.8451169

Cited by 14 publications

(17 citation statements)

References 21 publications

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“…The proposed model is compared with several other level set segmentation models: CV model [6], LBF model [11], DRLSE model [16], LPF model [15] and SDREL model [8]. For better comparison, we also chose two state‐of‐the‐art salient region detection method íC the RC model [29] and the DSC model [34]. The parameters of this model are set as follows:

η = 0.1

κ = 0.9

ε = 1.5

μ = 5

ν = 1.5

λ_{1} = 0.01

λ_{2} = 0.01

λ_{3} = 0.06

λ_{4} = 0.06

λ = 5

α = 1.5

β = 0.2

and

Δ t = 0.1

.…”

Section: Resultsmentioning

confidence: 99%

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Level set method with Retinex‐corrected saliency embedded for image segmentation

Liu

Chang

Zhang

et al. 2021

IET Image Processing

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It can be a very challenging task when using level set method segmenting natural images with high intensity inhomogeneity and complex background scenes. A new synthesis level set method for robust image segmentation based on the combination of Retinex‐corrected saliency region information and edge information is proposed in this work. First, the Retinex theory is introduced to correct the saliency information extraction. Second, the Retinex‐corrected saliency information is embedded into the level set method due to its advantageous quality which makes a foreground object stand out relative to the backgrounds. Combined with the edge information, the boundary of segmentation will be more precise and smooth. Experiments indicate that the proposed segmentation algorithm is efficient, fast, reliable, and robust.

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η = 0.1

κ = 0.9

ε = 1.5

μ = 5

ν = 1.5

λ_{1} = 0.01

λ_{2} = 0.01

λ_{3} = 0.06

λ_{4} = 0.06

λ = 5

α = 1.5

β = 0.2

and

Δ t = 0.1

.…”

Section: Resultsmentioning

confidence: 99%

“…Wu et al. [34] proposed a novel propagation model via deformed smoothness constraint to deal with the problem that some object regions with low contrast to background will miss during saliency detection. Some saliency detection models [41–43] have been proposed based on deep learning [44].…”

Section: Background and Motivationmentioning

confidence: 99%

Level set method with Retinex‐corrected saliency embedded for image segmentation

Liu

Chang

Zhang

et al. 2021

IET Image Processing

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“…A saliency method that utilized multiscale extrema of local perceptual color difference was devised to successfully detect large salient objects [3]. A saliency detection method that applied deformed smoothness-based manifold ranking was presented to overcome the problem of misclassified salient objects with low contrast backgrounds [84]. A saliency detection method based on the fusion of foreground-center with background priors was recently proposed to solve the challenge of detecting salient objects touching image boundary [68].…”

Section: Saliency Methods For Challenging Image Categoriesmentioning

confidence: 99%

“…The quantized RGB (QRGB) color image is the input to the histogram-based clustering process to reduce the number of colors in the input image. The numerous color models used in saliency detection methods include RGB [67,70,106], hue, saturation, value (HSV) [107], lightness, redness, yellowness (L*a*b*) [66,68,84,108,109] and combination of color models [67,85,106]. This study has used the QRGB color image for clustering while the L*a*b* color image was applied for the extraction of color features because of its perceptual uniformity [50,66,110].…”

Section: Methodsmentioning

confidence: 99%

“…However, boundarypositioned objects and objects that show high color similarity to background regions are challenging cases for this method. A label propagation method using deformed smoothness was developed based on manifold ranking by exploiting objectness and smoothness constraints to overcome the aforementioned limitation [84]. In general, most of the existing graph-based methods are not adequate to successfully separate salient objects from images with complex background scenes or salient objects with various features [8].…”

Section: Graph-based Saliency Detectionmentioning

confidence: 99%

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Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

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SGVM: Semantic-Guided Variational Model for Sealing Nail Defect Extraction Within Albedo Domain via Photometric Stereo

Liu,

Cao,

et al. 2023

IEEE Access

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Automatic 2D vision-based defect detection on sealing nail (SealN) surfaces is challenging due to interference of complex backgrounds with non-homogeneous and low contrast between foreground and background. Inspired by an interesting observation that the albedo domain recovered by the uncalibrated photometric stereo (UPS) shows obvious differences and significant abruptness between defects' and nondefects' regions, we develop a novel semantic-guided variational model (SGVM) to conditional extract structural defects from albedo map. Specifically, SGVM utilizes one developed global regularized label indicator to semantically guide one local regularized relative Gaussian filter (RGF) for achieving large-scale structures (i.e., defects) preservation and small-scale textures (i.e., background) suppression. Furthermore, defects can be efficiently extracted by thresholding the structure map within the label indicator. Additionally, experimental results on numerous challenging defect images reveal that the proposed SGVM outperforms the existing advanced 2D methods in terms of defect extraction. INDEX TERMSSemantic-guided Variational Model (SGVM), Defect Extraction, Sealing Nail, Albedo Domain, Uncalibrated Photometric Stereo (UPS).

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Salient Object Detection Via Deformed Smoothness Constraint

Cited by 14 publications

References 21 publications

Level set method with Retinex‐corrected saliency embedded for image segmentation

Level set method with Retinex‐corrected saliency embedded for image segmentation

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

SGVM: Semantic-Guided Variational Model for Sealing Nail Defect Extraction Within Albedo Domain via Photometric Stereo

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