Local Directional Pattern (LDP) &amp;#150; A Robust Image Descriptor for Object Recognition

Jabid, Taskeed; Kabir, Md. Hasanul; Chae, Oksam

doi:10.1109/avss.2010.17

Cited by 86 publications

(61 citation statements)

References 18 publications

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“…(4) According to the posterior probability values of sample x to be tested belonging to each class obtained by base classifier e k , we calculate the decision credibility β k of the classifier using Eqs. (20) and (21). …”

Section: Description Of the Proposed Algorithmmentioning

confidence: 99%

“…To meet the requirements, this study uses the curve fitting in the numerical analysis to construct the sigmoid function in describing the change relationship between τ k and decision credibility β k as shown in Eq. (21). Each parameter in Eq.…”

Section: Decision Credibility Of Classifiermentioning

confidence: 99%

“…Commonly used image features include colour, shape and texture features. Amongst them, the texture features based on statistics are favoured by domestic and foreign scholars because of their simplicity and effectiveness; the representative methods are local binary pattern [61], central symmetric local binary pattern [18], local directional pattern [21], local gradient patterns [23], modified census transform [15] and local mean pattern [46]. In this study, the symmetric local graph structure (SLGS) [1] algorithm is used to extract the feature of face images.…”

Section: Feature Description Of Face Imagementioning

confidence: 99%

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Multi-classifier ensemble based on dynamic weights

Ren

2017

Multimed Tools Appl

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In this study, a novel multi-classifier ensemble method based on dynamic weights is proposed to reduce the interference of unreliable decision information and improve the accuracy of fusion decision. The algorithm defines decision credibility to describe the realtime importance of the classifier to the current target, combines this credibility with the reliability calculated by the classifier on the training data set and dynamically assigns the fusion weight to the classifier. Compared with other methods, the contribution of different classifiers to fusion decision in acquiring weights is fully evaluated in consideration of the capability of the classifier to not only identify different sample regions but also output decision information when identifying specific targets. Experimental results on public face databases show that the proposed method can obtain higher classification accuracy than that of single classifier and some popular fusion algorithms. The feasibility and effectiveness of the proposed method are verified.

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Section: Description Of the Proposed Algorithmmentioning

confidence: 99%

Section: Decision Credibility Of Classifiermentioning

confidence: 99%

Section: Feature Description Of Face Imagementioning

confidence: 99%

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Multi-classifier ensemble based on dynamic weights

Ren

2017

Multimed Tools Appl

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“…An Edge Histogram Descriptor (EHD) can be accumulated to describe the shape of the regions [15]. Local Directional Pattern (LDP) and Local Binary Pattern (LBP) are coded descriptors regarding the difference in gradients and gray values used to describe the texture of the image regions [16,17]. LBP was used by Mohammed and Yampolskiy in relation to face recognition [17].…”

Section: Related Workmentioning

confidence: 99%

Hardware Architecture for Real-Time Computation of Image Component Feature Descriptors on a FPGA

Malik

Thörnberg

Imran

et al. 2014

International Journal of Distributed Sensor Networks

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This paper describes a hardware architecture for real-time image component labeling and the computation of image component feature descriptors. These descriptors are object related properties used to describe each image component. Embedded machine vision systems demand a robust performance and power efficiency as well as minimum area utilization, depending on the deployed application. In the proposed architecture, the hardware modules for component labeling and feature calculation run in parallel. A CMOS image sensor (MT9V032), operating at a maximum clock frequency of 27 MHz, was used to capture the images. The architecture was synthesized and implemented on a Xilinx Spartan-6 FPGA. The developed architecture is capable of processing 390 video frames per second of size 640 × 480 pixels. Dynamic power consumption is 13 mW at 86 frames per second.

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“…It assigns an 8-bit binary code to each pixel based on its neighboring edge response values in different directions. Kirsch masks, known to detect different directional edge responses, are used [15]. Its edge response masks in eight directions are shown in Figure 6.…”

Section: Local Directional Pattern (Ldp)mentioning

confidence: 99%

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Local Directional Pattern (LDP)  A Robust Image Descriptor for Object Recognition

Cited by 86 publications

References 18 publications

Multi-classifier ensemble based on dynamic weights

Multi-classifier ensemble based on dynamic weights

Hardware Architecture for Real-Time Computation of Image Component Feature Descriptors on a FPGA

Avatar captcha : telling computers and humans apart via face classification and mouse dynamics.

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