Texture characterization, representation, description, and classification based on full range Gaussian Markov random field model with Bayesian approach

Seetharaman, K.; Palanivel, N.

doi:10.1080/19479832.2013.804007

Cited by 21 publications

(8 citation statements)

References 52 publications

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“…Similar conclusions could be made if the SBF method is compared with the eight filtering algorithms presented by Sithole and Vosselman [17]. The future work will focus on the improvement of the proposed filter to reduce the type II errors, markov random field model [48] is introduced to analyze the spatial topology of the segments, multi-source data fusion [49] is employed and parallel computing is performed to promote the efficiency.…”

Section: Discussionmentioning

confidence: 77%

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Lin

Zhang

2014

Remote Sensing

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Abstract:Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.

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Section: Discussionmentioning

confidence: 77%

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Lin

Zhang

2014

Remote Sensing

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“…On each region, the parameters and autocorrelation coefficients are computed, and they are combined together and formed as FVs database. The extracted features are classified into various groups according to their nature using fuzzy c-means algorithm [13]. For each group, median value is calculated, and based on it the FVs are indexed.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Mainly, the key issue to develop a successful retrieval system relies on identifying and choosing the right features that represent the images as strong as possible. Feature representation of the images may include color [1,2,3,4,5,6,9], texture ( [3,7,10,11,13] and shape [1,6,7,8] information.…”

Section: Introductionmentioning

confidence: 99%

Color Image Retrieval Based on Full Range AutoRegressive Model with Low-Level Features

Giri¹,

Seetharaman

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…The future work will focus on the improvement of the proposed filter. For example, the markov random field model [54] might be introduced to analyze the spatial topology, and multisource data fusion [55] is employed to increase the accuracy.…”

Section: Discussionmentioning

confidence: 99%

Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures

2014

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As one of the key steps in the processing of airborne light detection and ranging (LiDAR) data, filtering often consumes a huge amount of time and physical memory. Conventional sequential algorithms are often inefficient in filtering massive point clouds, due to their huge computational cost and Input/Output (I/O) bottlenecks. The progressive TIN (Triangulated Irregular Network) densification (PTD) filter is a commonly employed iterative method that mainly consists of the TIN generation and the judging functions. However, better quality from the progressive process comes at the cost of increasing computing time. Fortunately, it is possible to take advantage of state-of-the-art multi-core computing facilities to speed up this computationally intensive task. A streaming framework for filtering point clouds by encapsulating the PTD filter into independent computing units is proposed in this paper. Through overlapping multiple computing units and the I/O events, the efficiency of the proposed method is improved greatly. More importantly, this framework is adaptive to many filters. Experiments suggest that the proposed streaming PTD (SPTD) is able to improve the performance of massive point clouds processing and alleviate the I/O bottlenecks. The experiments also demonstrate that this SPTD allows the quick processing of massive point clouds with better adaptability. In a 12-core environment, the SPTD gains a speedup of 7.0 for filtering 249 million points.

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Texture characterization, representation, description, and classification based on full range Gaussian Markov random field model with Bayesian approach

Cited by 21 publications

References 52 publications

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Color Image Retrieval Based on Full Range AutoRegressive Model with Low-Level Features

Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures

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