Region localization based on rotational invariant feature and improved self organized map

Traditional texture region location methods with Gabor features are often limited in the selection of Gabor filters and fail to deal with the target which contains both texture and non-texture parts. Thus, to solve this problem, a two-step new model was proposed. In the first step, the original features extracted by Gabor filters are applied to training a self-organizing map (SOM) neural network and a novel merging scheme is presented to achieve the clustering. A back propagation (BP) network is used as a classifier to locate the target region approximately. In the second step, Chan-Vese active contour model is applied to detecting the boundary of the target region accurately and morphological processing is used to create a connected domain whose convex hull can cover the target region. In the experiments, the proposed method is demonstrated accurate and robust in localizing target on texture database and practical barcode location system as well.

BackgroundSpine magnetic resonance image (MRI) plays a very important role in the diagnosis of various spinal diseases, such as disc degeneration, scoliosis, and osteoporosis. Accurate localization and segmentation of the intervertebral disc (IVD) in spine MRI can help accelerate the diagnosis time and assist in the treatment by providing quantitative parameters. In this paper, a method based on Gabor filter bank is proposed for IVD localization and segmentation.MethodsFirst, the structural features of IVDs are extracted using a Gabor filter bank. Second, the Gabor features of spine are calculated and spinal curves are detected. Third, the Gabor feature images (GFI) of IVDs are calculated and adjusted according to the spinal curves. Fourth, the IVDs are localized by clustering analysis with GFI. Finally, an optimum grayscale-based algorithm with self-adaptive threshold, combined with the localization results and Gabor features of the spine, is performed for IVDs segmentation.ResultsThe proposed method is verified by an MRI dataset consisting of 278 IVDs from 37 patients. The accuracy of localization is 98.23 % and the dice similarity index for segmentation evaluation is 0.9237.ConclusionsThe proposed Gabor filter based method is effective for IVD localization and segmentation. It would be useful in computer-aided diagnosis of IVD diseases and computer-assisted spine surgery.

2D texture based classification, segmentation and 3D orientation estimation of tissues using DT-CWT feature extraction methods

Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature, different parameter settings have been suggested, and filter banks created by these parameter settings work well in general. From the perspective of pattern classification, however, filter banks thus designed may not be ideal. In the present study, we propose a new approach to Gabor filter bank design, by incorporating feature selection, i.e. filter selection, into the design process. The merits of incorporating filter selection in filter bank design are twofold. Firstly, filter selection produces a compact Gabor filter bank and hence reduces computational complexity of texture feature extraction. Secondly, Gabor filter bank thus designed produces low-dimensional feature representation with improved sample-to-feature ratio, and this in turn leads to improved performance of texture classification. Experiment results on benchmark datasets and a real application have demonstrated the effectiveness of the proposed method.

In this paper, we present a facial expression recognition method based on Gabor feature and sparse representation. Sparse Representation based Classification (SRC) has been widely used in computer vision and pattern recognition. And Gabor filter banks can be used to approximately model the signal processing in visual primary cortex. We believe that the nature of the attractive performance of SRC and Gabor feature lies in that they both followed the natures of signal perception of retina and information processing of cortex in human vision. Therefore, we combined the Gabor feature and SRC for facial expression recognition. The comparison experiments of proposed Gabor+SRC algorithm and straightforward SRC application are conducted on JAFFE database. And the experimental results showed the attractive performance of the proposed Gabor+SRC method.

A new method for designing Gabor filter banks with various frequencies and orientations is proposed to perform feature extraction. A feature decomposition modular neural network classifier based on the feature extraction by the Gabor filter banks is presented for object recognition problems and the influence of band limited noise.

Gabor filters have been proven to be very useful for texture retrieval and are widely adopted (1, 2, 3, 4, 5, 6). However, the original Gabor texture features are rotation variant. Recently, Zhang et al proposed rotation normalization using circular shift. They have shown the proposed rotation normalization is effective through some examples, but did not comprehensively study its performance. The purpose of this paper is to study the performance of the rotation normalization on a good size texture database. Our experimental results show that the proposed rotation normalization is effective in retrieving rotated textures and has some adverse effect on retrieving non-rotated texture.

Palmprint Identification Using Gabor and Wide Principal Line Features

In this research we concentrate on adaptive texture feature extractors which are automatically extracted from an image collection. We use independent component analysis (ICA) to extract independent component filters (ICF). ICF have previously been shown to include edge filters, having properties similar to the receptive fields of simple cells of the human visual cortex. In this thesis we evaluate the utility of ICF-based collection-specific features in the context of content-based image retrieval (CBIR), with the view to demonstrate the viability of using such automatic collection-specific features. We find that global features extracted using a small number of ICF outperform those extracted by a bank of Gabor filters, even when very large number of Gabors are used. We also present comparisons against a variety of state-of-the-art features and show that ICF-based features perform better than these, without the need for any hand-tuning. ICA extracts large number of filters. In order to find a useful smaller subset we evaluate a previously-published variance-based filter selection method. We identify the shortcomings of this method. Our proposed improvements to filter-based feature selection and extraction, response scaling and locally normalised convolution, was a result of trying to address some of these shortcomings. Even with these improvements the variance-based method has an intrinsic flaw of being susceptible to selecting redundant filters. We propose a new filter selection method based on normalised cross-correlation and clustering. We show that our method selects a more useful subset compared to the previously published variance-based method. We also propose a salient-point based local feature which uses ICF and show that our proposed feature performs better than SIFT features and also the global ICF features. We further illustrate the utility of ICFSIFT by developing a grid-based adaptive local feature and we demonstrate that ICFSIFT extracts different and useful information compared to the ICF-based global and grid-based local features. After applying the techniques proposed in this thesis to four standard texture collections, we show that, while hand-tuned features can work well for their target collection, the automatic adaptive features perform better for the general case.

Invariance against rotation of 3D objects is one of the essential properties for 3D shape analysis. Recently proposed algorithms have achieved rotationally invariant 3D point set analysis by using inherently rotation-invariant 3D shape features, i.e., distances and angles among 3D points, as input to Deep Neural Networks (DNNs). The DNNs capture spatial hierarchy and context among the geometric features to produce accurate analytical results. In this article, we delve further into the DNN-based approach to rotation-invariant and highly accurate 3D point set analysis. In particular, we focus our attention on segmentation of 3D point sets, which is one of the most challenging among 3D point set analysis tasks. We propose a novel DNN for 3D point set segmentation called Rotation-invariant and Multi-scale feature Graph convolutional neural network, or RMGnet. Our RMGnet is more flexible than the previous methods as it accepts as input any handcrafted 3D shape features having rotation invariance. In addition, to accurately segment 3D point sets composed of parts having various sizes, we randomize scales at which handcrafted features are extracted and perform multi-resolution analysis of the features by using the DNN. Experimental evaluation demonstrates high segmentation accuracy as well as rotation invariance of the proposed RMGnet.

A learning vector quantization(LVQ) neural network classifier is for the first time applied for SAR data classification, of which the training termination rule is modified to make it have both the ability of classification and class signature analysis. A very high land cover classification accuracy is achieved. Especially under the condition that texture is considered, almost all roads can be classified correctly, which cannot be identified by BP MLP neural network and ML classifier.

Object detection using gabor filters

A method for SAR target recognition based on low-frequency sub-band image, Gabor filter bank and sub-block statistical feature is presented in the paper. The sub-band image extracted from the pre-processed SAR image is filtered by Gabor filter bank on different directions and scales. The each filtered sub-band image is divided into different sub-blocks and the statistical features derived from every sub-block are regarded as the target recognition feature, which can be used to recognize the targets with SVM. The proposed method is tested and validated on MSTAR dataset for 3-type SAR target recognition. (4 pages)

<p>Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed. </p>

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Gabor feature extraction and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing the contour. A Gabor filter bank for extraction of rotation- invariant texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted by the snake-like contour algorithm. A number of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with less than 10.2% of the accuracy which is relative to boundary provided manually by experts Keywords—Gabor Filter Bank, Support Vector Machines, Prostate Segmentation