Gabor Energy Features Research Articles

CFGVF: An improved correlation filters based visual tracking algorithm

Visual tracking is a challenging task in computer vision. Correlation filter (CF) based visual tracking algorithm has become an attractive tracking technique, while there are still some limitations. Existing CF-based tracking algorithms are vulnerable to the influence of the surrounding background, and the usage of fixed scale template may easily lead to tracking failure. To overcome the above limitations, we propose a CFs-based visual tracking algorithm, CFs with Gabor energy filter, variable-scale template and features fusion (CFGVF). In CFGVF, the Gabor energy filter is firstly adopted to preprocess every frame of the image sequence, which largely eliminates the influence of illumination variation. Then, the Gabor energy, Histogram of Oriented Gradient and color naming features are integrated to enhance the ability of dealing with significant appearance variations such as deformation and motion blur. Furthermore, we propose a variable-scale template method to estimate the scale of the target object. Finally, an online updating schema is adopted to reduce the interference of surrounding background and occlusion. Experimental results on the visual tracking benchmark dataset OOTB show that the performance and robustness of the CFGVF tracker outperform those of several state-of-the-art trackers.

A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

In this article, the authors used saliency detection for video streaming problem to be able to transmit regions of video frames in a ranked manner based on their importance. The authors designed an empirically-based study to investigate bottom-up features to achieve a ranking system stating the saliency priority. We introduced a gradual saliency detection model using a Bayesian framework for static scenes under conditions that we had no cognitive bias. To extract color saliency, we used a new feature contrast in Lab color space as well as a k-nearest neighbor search based on k-d tree search technique to assign a ranking system into different colors according to our empirical study. To find the salient textured regions we employed contrast-based Gabor energy features and then we added a new feature as intensity variance map. We merged different feature maps and classified saliency maps using a Naive Bayesian Network to prioritize the saliency across a frame. The main goal of this work is to create the ability to assign a saliency priority for the entirety of a video frame rather than simply extracting a salient area which is widely performed.

An evolutionary single Gabor kernel based filter approach to face recognition

For the past few decades, Gabor filter banks have been used for extracting features in face recognition. The state-of-the-art approach for the design and selection of Gabor filter banks is based on a trial and error. This results in more computational complexity and higher response time. To overcome this problem, an attempt is made to design a single optimized Gabor filter instead of a filter bank for feature extraction. This approach improves the filter performance by significantly reducing the computational complexity and response time. The hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) is used for optimizing the parameters of the single Gabor filter. In this context, an evolutionary single Gabor kernel (ESGK) based filter approach is proposed for face recognition. The proposed method is used to extract Gabor energy feature vectors from face images. We also propose a new eigenvalue based classification approach for face recognition. This approach is derived from sparse based representation methods. The novelty in our paper is measurement of sparsity of the weighting coefficients of each training sample. The main contribution of the paper is two-fold. Firstly, investigation of ESGK approach, which is not found in the literature. Secondly, introducing a new eigen value based classifier. FERET, ORL, UMIST, GT and LFW databases are used to measure the efficiency of our proposed method. The results are compared with a holistic Gabor filter bank based recognition methods. It is witnessed that our proposed method outperforms the state-of-the-art methods.

Survey of Brain Abnormality using Gabor Wavelets

Nowadays, MR images are very important for automatic defects detection in many diagnostic and therapeutic applications. This available work categorized the brain tissues as normal or abnormal automatically, using computer vision. This paper introduces novel approach of strong automatic diagnostic system using Gabor Wavelets and to find accuracy of tumor diagnosis in brain. Here we are tried to give clear justification from brain tissues using Gabor wavelets, entropy, contrast, energy and statistic features (such as mean, median etc.).

Improved contour detection model with spatial summation properties based on nonclassical receptive field

The responses of cortical neurons to a stimulus in a classical receptive field (CRF) can be modulated by stimulating the non-CRF (nCRF) of neurons in the primary visual cortex (V1). In the very early stages (at around 40 ms), a neuron in V1 exhibits strong responses to a small set of stimuli. Later, however (after 100 ms), the neurons in V1 become sensitive to the scene’s global organization. As per these visual cortical mechanisms, a contour detection model based on the spatial summation properties is proposed. Unlike in previous studies, the responses of the nCRF to the higher visual cortex that results in the inhibition of the neuronal responses in the primary visual cortex by the feedback pathway are considered. In this model, the individual neurons in V1 receive global information from the higher visual cortex to participate in the inhibition process. Computationally, global Gabor energy features are involved, leading to the more coherent physiological characteristics of the nCRF. We conducted an experiment where we compared our model with those proposed by other researchers. Our model explains the role of the mutual inhibition of neurons in V1, together with an approach for object recognition in machine vision.

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)—left arm up down, right arm up down, waist twisting and walking—have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time–frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.

The Recognition of Stored Grain Pests Based on The Gabor Wavelet and Sparse Representation

In order to improve the recognition rate and accuracy of stored grain pests classification, saving classification time, a new recognition method based on the Gabor wavelet and sparse representation is proposed in this paper. In this paper, nine typical pests in the stored grain are regarded as the research object, Gabor energy features and morphological features are extracted, principal component analysis is used to reduction dimension and sparse representation is used to achieve the classification of stored grain pests. Simulation results show that, Gabor energy feature is a better choose for grain pests classification, and the overall performance of Gabor features and sparse representation is better than the traditional classification methods. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.4860

Iris Data Indexing Method Using Gabor Energy Features

Biometric features are extracted from a complex pattern and stored as high dimensional data. These data do not follow traditional sorting order like numerical and alphabetical data. Hence, a linear search method makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. To address this problem, we propose an efficient indexing mechanism to retrieve iris biometric templates using Gabor energy features. The Gabor energy features are calculated from the preprocessed iris texture in different scales and orientations to generate a 12-dimensional index key for an iris template. An index space is created based on the values of index keys of all individuals. A candidate set is retrieved from the index space based on the values of query index key. Next, we rank the retrieved candidates according to their occurrences. If the identity of the query template is matched, then it is a hit, otherwise a miss. We have experimented our approach with Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris databases. Our proposed approach gives 11.3%, 14.5%, 16.3%, 13.5%, and 10.3% penetration rates and 98.2%, 91.1%, 90.7%, 85.2%, and 96% hit rates for Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris database, respectively, when we consider the retrieving templates up to the fifth rank. Experiments substantiate that our approach is capable of retrieving biometric data with a higher hit rate and lower penetration rate compared to the existing approaches. Application of Gabor energy features to index iris data proves to be effective for fast and accurate retrieval. With our proposed approach, it is possible to retrieve a set of iris templates similar to the query template in the order of milliseconds and is independent of sizes of databases.

WRITER IDENTIFICATION BY TEXTURE ANALYSIS BASED ON KANNADA HANDWRITING

Writer identification problem is one of the important area of research due to its various applications and is a challenging task. The major research on writer identification is based on handwritten English documents with text independent and dependent. However, there is no significant work on identification of writers based on Kannada document. Hence, in this paper, we propose a text-independent method for off-line writer identification based on Kannada handwritten scripts. By observing each individual’s handwriting as a different texture image, a set of features based on Discrete Cosine Transform, Gabor filtering and gray level co-occurrence matrix, are extracted from preprocessed document image blocks. Experimental results demonstrate that the Gabor energy features are more potential than the DCTs and GLCMs based features for writer identification from 20 people.

Similarity Measurement using Gabor Energy Feature and Mutual Information for Image Registration

Image registration is an essential process to analyze the time series of satellite images for the purpose of image fusion and change detection. The Mutual Information (MI) is commonly used as similarity measure for image registration because of its robustness to noise. Due to the radiometric differences, it is not easy to apply MI to multi-temporal satellite images using directly the pixel intensity. Image features for MI are more abundantly obtained by employing a Gabor filter which varies adaptively with the filter characteristics such as filter size, frequency and orientation for each pixel. In this paper we employed Bidirectional Gabor Filter Energy (BGFE) defined by Gabor filter features and applied the BGFE to similarity measure calculation as an image feature for MI. The experiment results show that the proposed method is more robust than the conventional MI method combined with intensity or gradient magnitude.

Using Gabor filter for the illumination invariant recognition of color texture

We derive Gabor feature matrices that are used to recognize color texture independent of illumination changes. The feature depends on Gabor energy feature in which we derive a multi-band Gabor energy feature matrix. We also prove that two Gabor energy feature matrices such that represent two corresponding textures under different illumination are related by a linear transform. Experimental results show that the model is successful in recognizing color texture independent of the change in the spectral illuminations.

Wavelet transform-based locally orderless images for texture segmentation

Texture analysis is an important issue in many areas like object recognition, image retrieval study, medical imaging, robotics, and remote sensing. Despite the development of a family of techniques over the last couple of decades, there are only a few reliable methods. Multiresolution techniques seem to be attractive for many applications. In this study, we present an approach based on the discrete wavelet transform and scale space concept. We integrate the framework of locally orderless images (LOIs) with the transform coefficients to obtain a flexible method for texture segmentation. Compared to intensity (spatial domain), the wavelet coefficients appear to be more reliable with respect to noise immunity and the ease of feature formation. Hence, we represent each discrete coefficient value with a probability density function to form isophote images. Each isophote image is then convolved with a Gaussian to form LOIs, which specify a local histogram in each transform point. These LOIs, or statistical moments computed from LOIs, can be regarded as texture features. An experiment with the standard Brodatz's and VisTex texture databases demonstrates the superior performance of the wavelet-based LOIs compared to conventional LOI-based moments or wavelet and Gabor energy features. The elegance of the approach is in the relatively greater flexibility in producing segmentation results. A simple minimum distance classifier and confusion matrix analyses confirm the above attributes.

On idempotence and related requirements in edge detection

The energy feature detectors described by R. Owens et al. (1989) are good candidates for idempotent edge detectors. However, some of them (in particular, the Gabor energy feature detector) suffer from a serious defect that is absent in gradient-type operators: their sensitivity to gray-level shift in the original image. This leads to errors in the localization of step edges. The Fourier phase and amplitude conditions outlined by M.C. Morrone and D.C. Burr (1988) for the class of energy feature detectors guarantee a zero DC level when the convolution masks are taken in L/sup 1/; therefore, the resulting energy feature detector is invariant under grey-level shift in the original image. Also, the properties of the underlying edge model are invariant under a smoothing of the image by a Gaussian or any function in L/sup 1/ having zero Fourier phase. In particular, such a smoothing does not deteriorate the idempotence of the edge detector. Some concrete examples of energy feature detectors satisfying the Morrone conditions are described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>