A Note on Advantages of the Fuzzy Gabor Filter in Object and Text Detection

This note presents a fuzzy optimization of Gabor filter-based object and text detection. The derivation of a 2D Gabor filter and the guidelines for the fuzzification of the filter parameters are described. The fuzzy Gabor filter proved to be a robust text an object detection method in low-quality input images as extensively evaluated in the problem of license plate localization. The extended set of examples confirmed that the fuzzy optimized Gabor filter with adequately fuzzified parameters detected the desired license plate texture components and highly improved the object detection when compared to the classic Gabor filter. The robustness of the proposed approach was further demonstrated on other images of various origin containing text and different textures, captured using low-cost or modest quality acquisition procedures. The possibility to fine tune the fuzzification procedure to better suit certain applications offers the potential to further boost detection performance.

Genetic Algorithm Based Gabor CNN For Palmprint Recognition

In the field of biometrics, palmprint recognition has received great interest and made tremendous progress in the past two decades. In palmprint recognition, the important step is to extract the discriminative features from the image and compare it with templates for identification and verification tasks. In this paper, a new genetic-based 2D Gabor filter with the Convolutional Neural Network is presented. The scale and orientation details captured by Gabor filters are optimized based on central frequency, which is determined based on genetic algorithm fitness function. The proposed technique is implemented on four publicly available palmprint datasets- PolyU, CASIA, IITD, and Tongji. Experimental results confirm that the proposed technique achieves better accuracy when compared to Palmnet.

Speech Enhancement based on 2D Gabor Filters for Arabic Phoneme Spoken by Malay Speakers

In this paper, a speech enhancement method using 2D Gabor filter is proposed. The proposed filter is used to enhance Arabic phoneme speech signals that have been recorded under control environment namely indoor room recording. All the phoneme signals are spoken by Malay speakers and considered as non-native Arabic speakers. Firstly, corrupted speech signals by noise must be enhanced before further processing. The effectiveness of the suggested approach is evaluated in compare with Wiener filter. It is proven that the proposed 2D Gabor filters performed appropriately for speech enhancement purpose at different wavelengths.

Speech Enhancement based on 2D Gabor Filters for Arabic Phoneme Spoken by Malay Speakers

In this paper, a speech enhancement method using 2D Gabor filter is proposed. The proposed filter is used to enhance Arabic phoneme speech signals that have been recorded under control environment namely indoor room recording. All the phoneme signals are spoken by Malay speakers and considered as non-native Arabic speakers. Firstly, corrupted speech signals by noise must be enhanced before further processing. The effectiveness of the suggested approach is evaluated in compare with Wiener filter. It is proven that the proposed 2D Gabor filters performed appropriately for speech enhancement purpose at different wavelengths.

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.