Gabor Feature Space Research Articles

Personalised gesture recognition based on tri-axis accelerometer using Gabor filters

Human gesture is one of the most important ingredients of context information. In this paper, a novel gesture recognition framework based on tri-axis accelerometer mounted on a cell phone is proposed. Since the length of acceleration signals is variable according to different gestures and different subjects' input speed, most of recognition algorithms cannot be used. To solve this problem, we propose a novel resampling method by combining decimation and interpolation. After that, we propose 1D Gabor coefficients of acceleration signals as features. However, the dimensionality of Gabor feature space used in gesture recognition is very high. We adopt Boosting and a two-stage method PCA plus LDA to select and compress the Gabor feature. The classifier we used is fast multi-class support vector machine. The average recognition results of 17 complex gestures using the proposed Gabor feature are effective. The performance of experimental results shows that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile devices.

Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

AbstractWe present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.

Accelerating Multiresolution Gabor Feature Extraction for Real Time Vision Applications

Multiresolution Gabor filter banks are used for feature extraction in a variety of applications as Gabor filters have shown to be exceptional feature extractors with a close correspondence to the simple cells in the primary visual cortex (V1) of the brain. Yet applying the Gabor filter is a computationally intensive task. Most applications that utilize the Gabor feature space require real time results; however, the large quantity of computations involved has hindered systems from achieving real time performance. The natural solution for such compute intensive tasks is parallelization. FPGAs have emerged as attractive platforms for compute intensive signal processing applications due to their massively parallel computation resources as well as low power consumption and affordability. We present a configurable architecture for Gabor feature extraction on FPGA that enhances the resource utilization of the FPGA hardware fabric while maintaining a streaming data flow to yield exceptional performance. The increased resource utilization resulting from configurability, optimizations, and resource sharing allows for higher levels of parallelism to achieve real time feature extraction of high resolution images. Two architectures are introduced. The first is an architecture for multiresolution feature extraction with extensive resource sharing for enhanced resource utilization. The second is an architecture for many-orientation applications using a coarse to fine grain method to enhance resource utilization by reducing the number of filters applied at different orientations. Our results show that our multiresolution implementation achieves real-time performance on 2048?×?1526 images and exhibits 6X speed up over a GPU implementation while exhibiting energy efficiency with 0.4fps/W compared to the GPU that achieves 0.036fps/W.[1] The implementation for many-orientation applications using the coarse to fine grain method exhibits resource saving of at most 2 O $$ 2\sqrt{O} $$ for O number of orientations and higher, compared to a fully parallel architecture and 25× speedup compared to a GPU implementation for 16 orientations.

Video Colorization Using Parallel Optimization in Feature Space

We present a new scheme for video colorization using optimization in rotation-aware Gabor feature space. Most current methods of video colorization incur temporal artifacts and prohibitive processing costs, while this approach is designed in a spatiotemporal manner to preserve temporal coherence. The parallel implementation on graphics hardware is also facilitated to achieve realtime performance of color optimization. By adaptively clustering video frames and extending Gabor filtering to optical flow computation, we can achieve real-time color propagation within and between frames. Temporal coherence is further refined through user scribbles in video frames. The experimental results demonstrate that our proposed approach is efficient in producing high-quality colorized videos.

Seamlet carving for shape-aware image resizing

In this paper we propose an optimized seam-carving approach for anti-shearing image resizing. Current image/video seam-carving strategy only focuses on deleting the pixels along 8-connected seams, which may lead to the obvious information loss during resizing. Unlike the traditional seam-carving methods, our approach can construct a seamlet to handle the discontinuous pixels and further achieve an optimized image resizing results with shape-preservation. In particular, to suppress zigzag artifacts, we introduce the anti- shearing energy into the seamlet generation, which includes two steps: 1) the energy map is formalized by using Gabor filtering and the saliency map; 2) The energy computation is optimized in Gabor feature space to obtain the resizing results without the connectivity restriction. The experimental results and comparisons show the effectiveness of our method.

Gabor feature-based apple quality inspection using kernel principal component analysis

Automated inspection of apple quality involves computer recognition of good apples and blemished apples based on geometric or statistical features derived from apple images. This paper introduces a Gabor feature-based kernel principal component analysis (PCA) method by combining Gabor wavelet representation of apple images and the kernel PCA method for apple quality inspection using near-infrared (NIR) imaging. First, Gabor wavelet decomposition of whole apple NIR images was employed to extract appropriate Gabor features. Then, the kernel PCA method with polynomial kernels was applied in the Gabor feature space to handle non-linear separable features. The results show the effectiveness of the Gabor-based kernel PCA method in terms of its absolute performance and comparative performance compared to the PCA, kernel PCA with polynomial kernels, Gabor-based PCA and the support vector machine methods. Using the proposed Gabor kernel PCA eliminated the need for local feature segmentation, but also resolved the non-linear separable problem. An overall 90.6% recognition rate was achieved.

Integrated active contours for texture segmentation

We address the issue of textured image segmentation in the context of the Gabor feature space of images. Gabor filters tuned to a set of orientations, scales and frequencies are applied to the images to create the Gabor feature space. A two-dimensional Riemannian manifold of local features is extracted via the Beltrami framework. The metric of this surface provides a good indicator of texture changes and is used, therefore, in a Beltrami-based diffusion mechanism and in a geodesic active contours algorithm for texture segmentation. The performance of the proposed algorithm is compared with that of the edgeless active contours algorithm applied for texture segmentation. Moreover, an integrated approach, extending the geodesic and edgeless active contours approaches to texture segmentation, is presented. We show that combining boundary and region information yields more robust and accurate texture segmentation results.

A new robust circular Gabor based object matching by using weighted Hausdorff distance

This paper describes a new and efficient circular Gabor filter-based method for object matching by using a version of weighted modified Hausdorff distance. An improved Gabor odd filter-based edge detector is performed to get edge maps. A rotation invariant circular Gabor-based filter, which is different from conventional Gabor filter, is used to extract rotation invariant features. The Hausdorff distance (HD) has been shown an effective measure for determining the degree of resemblance between binary images. A version of weighted modified Hausdorff distance (WMHD) in the circular Gabor feature space is introduced to determine which position can be possible object model location, which we call ‘coarse’ location, and at the same time we get correspondence pairs of edge pixels for both object model and input test image. Then we introduce the geometric shape information derived from the above correspondence pairs of edge pixels to find the ‘fine’ location. The experimental results given in this paper show the proposed algorithm is robust to rotation, scale, occlusion, and noise etc.

Simple Gabor feature space for invariant object recognition

Invariant object recognition is one of the most challenging problems in computer vision. The authors propose a simple Gabor feature space, which has been successfully applied to applications, e.g., in invariant face detection to extract facial features in demanding environments. In the proposed feature space, illumination, rotation, scale, and translation invariant recognition of objects can be realized within a reasonable amount of computation. In this study, fundamental properties of Gabor features, construction of the simple feature space, and invariant search operations in the feature space are discussed in more detail.