Feature Weighting and Retrieval Methods for Dynamic Texture Motion Features

Rahman, Ashfaqur; Murshed, Manzur

doi:10.2991/jnmp.2009.2.1.4

Cited by 4 publications

(3 citation statements)

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“…Feature weighing methods are commonly used to find the relative significance among a set of features that are effectively used by the retrieval methods to search image sequences efficiently from large databases. Block motion based approaches mostly use Motion Co-occurrence Matrix (MCM) features that are called abstract features [15] [16]. The conventional feature weighting approach considers the features altogether and uses a heuristic search for large feature sets, as an exhaustive search is highly time consuming under such a scenario.…”

Section: Feature Weighting and Retrieval Methods For Dynamic Texturementioning

confidence: 99%

See 1 more Smart Citation

Block Motion Based Dynamic Texture Analysis: A Review

Rahman¹,

Tasnim²

2014

IJCTT

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Dynamic texture refers to image sequences of non-rigid objects that exhibit some regularity in their movement. Videos of smoke, fire etc. fall under the category of dynamic texture. Researchers have investigated different ways to analyze dynamic textures since early nineties. Both appearance based (image intensities) and motion based approaches are investigated. Motion based approaches turn out to be more effective. A group of researchers have investigated ways to utilize the motion vectors readily available with the blocks in video codes like MGEG/H26X. In this paper we provide a review of the dynamic texture analysis methods using block motion. Research into dynamic texture analysis using block motion includes recognition, motion computation, segmentation, and synthesis. We provide a comprehensive review of these approaches.

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Section: Feature Weighting and Retrieval Methods For Dynamic Texturementioning

confidence: 99%

“…96.03% recognition accuracy was obtained using the approach in [15]. A set of retrieval methods using MCM features is presented in [16][17] [18].…”

Section: Feature Weighting and Retrieval Methods For Dynamic Texturementioning

confidence: 99%

Block Motion Based Dynamic Texture Analysis: A Review

Rahman¹,

Tasnim²

2014

IJCTT

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“…Therefore, researchers tend to focus on the spatial variations (structural distribution) in the intensity or color space of the rice images. In particular, they paid significant attention to the spatial structure features of rice image, usually called image texture[ 38 ].…”

Section: Application Casementioning

confidence: 99%

Visual Perception-Based Statistical Modeling of Complex Grain Image for Product Quality Monitoring and Supervision on Assembly Production Line

et al. 2016

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Computer vision as a fast, low-cost, noncontact, and online monitoring technology has been an important tool to inspect product quality, particularly on a large-scale assembly production line. However, the current industrial vision system is far from satisfactory in the intelligent perception of complex grain images, comprising a large number of local homogeneous fragmentations or patches without distinct foreground and background. We attempt to solve this problem based on the statistical modeling of spatial structures of grain images. We present a physical explanation in advance to indicate that the spatial structures of the complex grain images are subject to a representative Weibull distribution according to the theory of sequential fragmentation, which is well known in the continued comminution of ore grinding. To delineate the spatial structure of the grain image, we present a method of multiscale and omnidirectional Gaussian derivative filtering. Then, a product quality classifier based on sparse multikernel–least squares support vector machine is proposed to solve the low-confidence classification problem of imbalanced data distribution. The proposed method is applied on the assembly line of a food-processing enterprise to classify (or identify) automatically the production quality of rice. The experiments on the real application case, compared with the commonly used methods, illustrate the validity of our method.

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