Mesh saliency detection via double absorbing Markov chain in feature space

Liu, Xiuping; Tao, Pingping; Cao, Junjie; Chen, He; Zhang, Changqing

doi:10.1007/s00371-015-1184-x

Cited by 9 publications

(10 citation statements)

References 31 publications

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“…Finally several recent algorithms [WSZL13, TKD15, TCL* 15] first apply an over‐segmentation and then compute the saliency per patch (instead of per vertex). Wu et al [WSZL13] exploit the global rarity of the patches, while [TCL* 15] and [LTC*16] estimate their saliency based on their relevance to some of the most unsalient ones. Finally, Tasse et al [TKD15] first compute saliency values per patch by considering their uniqueness and distribution and then smoothly propagate them to the vertices.…”

Section: Previous Workmentioning

confidence: 99%

Visual Attention for Rendered 3D Shapes

Lavoué

Cordier

Seo

et al. 2018

Computer Graphics Forum

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Understanding the attentional behavior of the human visual system when visualizing a rendered 3D shape is of great importance for many computer graphics applications. Eye tracking remains the only solution to explore this complex cognitive mechanism. Unfortunately, despite the large number of studies dedicated to images and videos, only a few eye tracking experiments have been conducted using 3D shapes. Thus, potential factors that may influence the human gaze in the specific setting of 3D rendering, are still to be understood. In this work, we conduct two eye‐tracking experiments involving 3D shapes, with both static and time‐varying camera positions. We propose a method for mapping eye fixations (i.e., where humans gaze) onto the 3D shapes with the aim to produce a benchmark of 3D meshes with fixation density maps, which is publicly available. First, the collected data is used to study the influence of shape, camera position, material and illumination on visual attention. We find that material and lighting have a significant influence on attention, as well as the camera path in the case of dynamic scenes. Then, we compare the performance of four representative state‐of‐the‐art mesh saliency models in predicting ground‐truth fixations using two different metrics. We show that, even combined with a center‐bias model, the performance of 3D saliency algorithms remains poor at predicting human fixations. To explain their weaknesses, we provide a qualitative analysis of the main factors that attract human attention. We finally provide a comparison of human‐eye fixations and Schelling points and show that their correlation is weak.

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Section: Previous Workmentioning

confidence: 99%

Visual Attention for Rendered 3D Shapes

Lavoué

Cordier

Seo

et al. 2018

Computer Graphics Forum

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“…Inspired by this research, visual saliency detection has been applied successfully to 3D meshes and point clouds. In recent years, much research has tried to develop methods for visual saliency on 3D surfaces [10][11][12]17,[19][20][21]32].…”

Section: Related Workmentioning

confidence: 99%

“…Afterward, background patches were selected as queries to transfer saliency, helping the method to handle noise better. An approach for mesh saliency detection based on a Markov Chain is proposed by Liu [10]; the input mesh was partitioned into segments using Neuts algorithms and then oversegmented into patches using Zernike coefficients. Instead of employing center-surround operators, background patches were selected by determining feature variance to separate the insignificant regions.…”

Section: Related Workmentioning

confidence: 99%

“…We replaced the L2norm with the L1-norm in the residual error, because it is more discriminative and robust to outliers. Next, following the instructions in Borji and Itti [40], we rewrote the equations ‖ � − �‖ 1 = ( ( )) and ‖ �‖ 0 = ( ( )), both saliency measurements are then normalized and combined (10).…”

Section: Saliency Detectionmentioning

confidence: 99%

“…There are many computational methods for simulating the HVS and visual saliency, but it nonetheless remains an unexplored field because of the difficulty of designing algorithms to simulate this process [6]. As for computer graphics, while the concept of visual saliency has been widely explored for mesh saliency [6], [8][9][10][11][12][13][14][15][16][17][18], few studies have explored visual saliency in point clouds [19][20][21][22]. Visual saliency is an important topic for 3D surface study and has important applications in 3D geometry processing such as resizing [1], simplification [23][24], smoothing [25], segmentation [26], shape matching and retrieval [27][28], 3D printing [29], and so forth.…”

Section: Introductionmentioning

confidence: 99%

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Point cloud saliency detection via local sparse coding

Narváez

Sánchez-Torres

Bedoya

2019

DYNA

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The human visual system (HVS) can process large quantities of visual information instantly. Visual saliency perception is the process of locating and identifying regions with a high degree of saliency from a visual standpoint. Mesh saliency detection has been studied extensively in recent years, but few studies have focused on 3D point cloud saliency detection. The estimation of visual saliency is important for computer graphics tasks such as simplification, segmentation, shape matching and resizing. In this paper, we present a method for the direct detection of saliency on unorganized point clouds. First, our method computes a set of overlapping neighborhoods and estimates adescriptor vector for each point inside it. Then, the descriptor vectors are used as a natural dictionary in order to apply a sparse coding process. Finally, we estimate a saliency map of the point neighborhoods based on the Minimum Description Length (MDL) principle.Experiment results show that the proposed method achieves similar results to those from the literature review and in some cases even improves on them. It captures the geometry of the point clouds without using any topological information and achieves an acceptable performance. The effectiveness and robustness of our approach are shown by comparing it to previous studies in the literature review.

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