Dynamic texture classification using dynamic fractal analysis

Xu, Yong; Quan, Yuhui; Ling, Haibin; Gao, Hui

doi:10.1109/iccv.2011.6126372

Cited by 100 publications

(84 citation statements)

References 24 publications

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“…Xu et al (Xu et al, 2011) 97.63 Tiwari and Tyagi (Tiwari and Tyagi, 2016a) 85.14 Tiwari and Tyagi (Tiwari and Tyagi, 2016b) 98.57 The proposed method 97.45 Table 7. Comparative results…”

Section: Resultsmentioning

confidence: 99%

“…Many methods, such as the local spatiotemporal filtering using an oriented energy (Wildes and Bergen, 2000), normal flow pattern estimation , spacetime texture analysis (Derpanis and Wildes, 2012), global spatiotemporal transforms (Li et al, 2009), model-based methods (Doretto et al, 2004. ), fractal analysis (Xu et al, 2011), wavelet multifractal analysis (Ji et al, 2013), and spatiotemporal extension of the LBPs (Liu et al, 2017), are concerned to this group. The discriminative methods prevail on the generative methods due to their robustness to the environmental changes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Favorskaya

Pyataeva

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Dynamic Texture (DT) can be considered as an extension of the static texture additionally comprising the motion features. The DT is very wide but the weak studied type of textures that is employed in many tasks of computer vision. The proposed method of the DTs recognition includes a preliminary categorization based on the proposed four categories, such as natural particles with periodic movement, natural translucency/transparent non-rigid blobs with randomly changed movement, man-made opaque rigid objects with periodic movement, and man-made opaque rigid objects with stationary or chaotic movement. Such formulation permitted to construct the separate spatial and temporal Convolutional Neural Networks (CNNs) for each category. The inputs of the CNNs are a pair of successive frames (taken through 1, 2, 3, or 4 frames according to a category), while the outputs store the sets of binary features in a view of histograms. In test stage, the concatenated histograms are compared with the histograms of the classes using the Kullback-Leibler distance. The experiments demonstrate the efficiency of the designed CNNs and provided the recognition rates up 97.46-98.32% for the sequences with a single type of the DT conducted on the DynTex database.

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“…Xu et al (Xu et al, 2011) 97.63 Tiwari and Tyagi (Tiwari and Tyagi, 2016a) 85.14 Tiwari and Tyagi (Tiwari and Tyagi, 2016b) 98.57 The proposed method 97.45 Table 7. Comparative results…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Favorskaya

Pyataeva

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Dyn35 Alpha Beta Gamma VLBP [3] 81.14 ---CVLBP [15] 85.14 ---HLBP [16] 98.57 ---DFS [30] 97 Note: Superscript "d" indicates the approach using deep structural TCoF; "s" is for results using the SVM; "-" means "not available".…”

Section: Methodsmentioning

confidence: 99%

Completed local structure patterns on three orthogonal planes for dynamic texture recognition

Nguyen

Nguyễn

Bouchara

2017

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Abstract-Dynamic texture (DT) is a challenging problem in computer vision because of the chaotic motion of textures. We address in this paper a new dynamic texture operator by considering local structure patterns (LSP) and completed local binary patterns (CLBP) for static images in three orthogonal planes to capture spatial-temporal texture structures. Since the typical operator of local binary patterns (LBP), which uses center pixel for thresholding, has some limitations such as sensitivity to noise and near uniform regions, the proposed approach can deal with these drawbacks by using global and local texture information for adaptive thresholding and CLBP for exploiting complementary texture information in three orthogonal planes. Evaluations on different datasets of dynamic textures (UCLA, DynTex, DynTex++) show that our proposal significantly outperforms recent results in the state-of-the-art approaches.

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“…Figure 2 demonstrates the strongly different appearances of running waters due to varying camera constellations and mutable image content. The complementary use of time and space enables the investigation of spatio-temporal texture and thus a situation-based image segmentation in respect of time-dependent image content (Szummer and Picard, 1996;Peh and Cheong, 2002;Hu et al, 2006;Nelson and Polana, 1992;Xu et al, 2011). On this basis, we add the temporal variability by means of time lapse image sequences.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Environmental Time Lapse Image Sequences for the Determination of Shore Lines Captured by Hand-Held Smartphone Cameras

Kröhnert

Meichsner

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The relevance of globally environmental issues gains importance since the last years with still rising trends. Especially disastrous floods may cause in serious damage within very short times. Although conventional gauging stations provide reliable information about prevailing water levels, they are highly cost-intensive and thus just sparsely installed. Smartphones with inbuilt cameras, powerful processing units and low-cost positioning systems seem to be very suitable wide-spread measurement devices that could be used for geo-crowdsourcing purposes. Thus, we aim for the development of a versatile mobile water level measurement system to establish a densified hydrological network of water levels with high spatial and temporal resolution. This paper addresses a key issue of the entire system: the detection of running water shore lines in smartphone images. Flowing water never appears equally in closerange images even if the extrinsics remain unchanged. Its non-rigid behavior impedes the use of good practices for image segmentation as a prerequisite for water line detection. Consequently, we use a hand-held time lapse image sequence instead of a single image that provides the time component to determine a spatio-temporal texture image. Using a region growing concept, the texture is analyzed for immutable shore and dynamic water areas. Finally, the prevalent shore line is examined by the resultant shapes. For method validation, various study areas are observed from several distances covering urban and rural flowing waters with different characteristics. Future work provides a transformation of the water line into object space by image-to-geometry intersection.

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Dynamic texture classification using dynamic fractal analysis

Cited by 100 publications

References 24 publications

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Completed local structure patterns on three orthogonal planes for dynamic texture recognition

Segmentation of Environmental Time Lapse Image Sequences for the Determination of Shore Lines Captured by Hand-Held Smartphone Cameras

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