Aggregate features approach for texture analysis

Patel, Ripal; Patel, Chirag; Thakkar, Ankit

doi:10.1109/nuicone.2012.6493209

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…As a future extension, if the robustness of large area occlusion, rotation, and some feature changes are also considered, the traditional methods rely on some specifc feature extraction [28] methods, such as using the orthogonal wavelet transform [29] of the image to consider the image details and multiresolution representation. If moving object detection in real scenes is also considered, visual attention is an efective mean [30].…”

Section: Discussionmentioning

confidence: 99%

Traffic Sign Detection: Appropriate Data Augmentation Method from the Perspective of Frequency Domain

Zheng

Tan

et al. 2022

Mathematical Problems in Engineering

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This study introduces a challenge faced by CNN in the task of traffic sign detection: how to achieve robustness to distributional shift. At present, all kinds of CNN models rely on strong data augmentation methods to enrich samples and achieve robustness, such as Mosaic and Mixup. In this study, we note that these methods do not have similar effects on combating noise. We explore the performance of augmentation strategies against disturbance in different frequency bands and provide understanding from the Fourier analysis perspective. This understanding can provide a guidance for selecting data augmentation strategies for different detection tasks and benchmark datasets.

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Section: Discussionmentioning

confidence: 99%

Traffic Sign Detection: Appropriate Data Augmentation Method from the Perspective of Frequency Domain

Zheng

Tan

et al. 2022

Mathematical Problems in Engineering

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“…In various texture analysis cases, the combination of texture analysis techniques with other image processing methods is necessary to achieve the desired effectiveness. Texture features can be defined using descriptors and can be obtained through operations involving single or combined models for texture analysis [32]. For example, the combination of Zernike transformation and GLCM was employed [33] to overcome the limitations of co-occurrence matrices.…”

Section: Related Workmentioning

confidence: 99%

“…Referring to [32] and the successful research [34], the combination of polar transformation and GLCM feature extraction shows promising potential when applied to circular-shaped objects. This is because the integration of polar transformation with GLCM allows for the modeling of texture that not only considers the spatial relationships between neighboring pixels in the image but also considers the radial representation of the texture.…”

Section: Related Workmentioning

confidence: 99%

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

2024

IJIES

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Quality inspection of solder connections in electronic circuit manufacturing is commonly performed using automatic optical inspection (AOI) technology. The utilization of deep learning in AOI has demonstrated high accuracy and fast computation, yet it requires expensive graphics processing unit (GPU) equipped computers. To enable broader and more cost-effective utilization, there is a need for an AOI model that can be embedded in simple central processing unit (CPU) based computers. To pursue this objective, several research efforts have been undertaken to develop AOI models based on classical machine learning techniques. However, the accuracy and speed achieved by these models have not yet matched deep learning-based AOI models. This study aims to enhance the computational efficiency of classic machine learning by processing only pixels containing textural information. The effectiveness enhancement is achieved through the application of log-polar transformation in the extraction of texture features using the gray level co-occurrence matrix (GLCM) to detect defects in trough hole technology (THT) solder joint connections. By transforming cartesian coordinates into polar coordinates, the textural areas to be detected assume a square shape, facilitating efficient texture feature extraction. To ensure a significant improvement in performance, a comparative performance evaluation is conducted on classic machine learning-based AOI models with and without the log-polar transformation. The texture features extracted from both models are classified using the support vector machine (SVM) method. Model testing and evaluation reveals that the proposed enhancement effort is capable of increasing accuracy levels to 95% and reach computation time by 120 milliseconds.

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“…A thresholding function is used to find most similar images for the given testing image. Other approaches for face detection and feature extraction can be found in [31][32][33][34][35][36][37][38].…”

Section: Biometricmentioning

confidence: 99%

A new hybrid method for face recognition

Thakkar

Jivani

Padasumbiya

et al. 2013

2013 Nirma University International Conference on Engineering (NUiCONE)

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Now a days, security is a major concern for any organization. It is very difficult to have enough faith in any person as far as security of the organization is concerned. Due to these reasons, face recognition gets popularity in the security domain. Many conventional methods are available to do the face recognition. In this paper, we have discussed few of them covering advantages, disadvantages and applications. It is not possible to have a single face recognition method to cover all underlying applications of face recognition system. We have also device a new hybrid method by combining existing approaches of Local Binary Pattern (LBP) and Histogram. This hybrid approach has been tested on standard dataset and compared with LBP. Simulation results shows that proposed hybrid approach outperforms compared to LBP as far as security and speed is concerned.Index Terms-Histogram, Pronciple component analysis, local binary pattern, local derivative pattern, neural network.

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Aggregate features approach for texture analysis

Cited by 10 publications

References 21 publications

Traffic Sign Detection: Appropriate Data Augmentation Method from the Perspective of Frequency Domain

Traffic Sign Detection: Appropriate Data Augmentation Method from the Perspective of Frequency Domain

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

A new hybrid method for face recognition

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