Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Plant Leaf Identification Using Gabor Wavelets

This article presents a novel method of plant classification using Gabor wavelet filters to extract texture filters in a foliar surface. The aim of this promising method is to add to the results obtained by other leaf attributes (such as shape, contour, color, among others), increasing, therefore, the percentage of classification of plant species. To corroborate the efficiency of the technique, an experiment using 20 species from Brazilian flora was done and discussed. The results are also compared with texture Fourier descriptors and cooccurrence matrices. (C) 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 236-243, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20201

Implementación de un detector de coral utilizando filtros Gabor Wavelets y máquinas de aprendizaje

Este trabajo se enfoca en la implementación de un detector de arrecife de coral de desempeño rápido que se utiliza para un vehículo autónomo submarino (Autonomous Underwater Vehicle, AUV, por sus siglas en inglés). Una detección rápida de la presencia de coral asegura la estabilización del AUV frente al arrecife en el menor tiempo posible, evitando colisiones con el coral. La detección de coral se hace en una imagen que captura la escena que percibe la cámara del AUV. Se realiza una clasificación píxel por píxel entre dos clases: arrecife de coral y el plano de fondo que no es coral. A cada píxel de la imagen se le asigna un vector característico, el mismo que se genera mediante el uso de filtros Gabor Wavelets. Éstos son implementados en C++ y la librería OpenCV. Los vectores característicos son clasificados a través de nueve algoritmos de máquinas de aprendizaje. El desempeño de cada algoritmo se compara mediante la precisión y el tiempo de ejecución. El algoritmo de Árboles de Decisión resultó ser el más rápido y preciso de entre todos los algoritmos. Se creó una base de datos de 621 imágenes de corales de Belice (110 imágenes de entrenamiento y 511 imágenes de prueba).

Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling

Adaptive embedded systems are required in various applications. This work addresses these needs in the area of adaptive image compression in FPGA devices. A simplified version of an evolution strategy is utilized to optimize wavelet filters of a Discrete Wavelet Transform algorithm. We propose an adaptive image compression system in FPGA where optimized memory architecture, parallel processing and optimized task scheduling allow reducing the time of evolution. The proposed solution has been extensively evaluated in terms of the quality of compression as well as the processing time. The proposed architecture reduces the time of evolution by 44% compared to our previous reports while maintaining the quality of compression unchanged with respect to existing implementations. The system is able to find an optimized set of wavelet filters in less than 2 min whenever the input type of data changes.

Exploring visual features through Gabor representations for facial expression detection

Gabor representations have been widely used in facial analysis (face recognition, face detection and facial expression detection) due to their biological relevance and computational properties. Two popular Gabor representations used in literature are: 1) Log-Gabor and 2) Gabor energy filters. Even though these representations are somewhat similar, they also have distinct differences as the Log-Gabor filters mimic the simple cells in the visual cortex while the Gabor energy filters emulate the complex cells, which causes subtle differences in the responses. In this paper, we analyze the difference between these two Gabor representations and quantify these differences on the task of facial action unit (AU) detection. In our experiments conducted on the Cohn-Kanade dataset, we report an average area underneath the ROC curve (A`) of 92.60% across 17 AUs for the Gabor energy filters, while the Log-Gabor representation achieved an average A` of 96.11%. This result suggests that small spatial differences that the Log-Gabor filters pick up on are more useful for AU detection than the differences in contours and edges that the Gabor energy filters extract.

基于Gabor小波的着色及纹理合成算法

着色和纹理合成是图形图像中的两类基本研究课题。前者需根据用户定义的彩色笔触信息，自动对黑白照片、电影或者漫画染上颜色；后者则需根据用户输入的样本纹理，经计算得出与样本纹理视觉上近似的结果纹理。这两类课题都有广泛的应用背景。如着色常常用于给经典的黑白电影或者照片自动上色，解决现在的染色工序中存在的需要大量人工交互的难题；而纹理合成常用于电影和电子游戏的地形地貌、织物、头发等等纹理的自动生成。 这两大类问题都需要分析纹理特征，并且依赖于分析结果的准确性。Gabor小波滤波器与人眼的视觉感受野相当吻合，用它来分析纹理得到的结果比较精确。鉴于此，本文把Gabor小波应用到了着色问题和纹理合成中。对于着色问题，本文用基于Gabor小波的特征向量重新定义邻居关系，然后用最优化方法迭代地对照片和卡通染色。相比以往的算法，本算法具有用户交互少、效果好、算法简单稳健的优点，并且算法允许用户逐步地添加色彩细节。对于纹理合成，本文用基于Gabor小波的特征向量来预计算K-Coherence候选集，提高了K-Coherence算法的准确性，从而改进了纹理合成的最终效果。 本文提出的算法是天然并行的，因而可利用GPU加速，做到实时计算。

基于Gabor小波特征确认多媒体消息数字水印版权标识

为解决基于数字水印的无线多媒体消息版权管理系统对提取后水印标识的自动识别问题,在充分考虑多媒体消息在传播中可能遭受攻击的基础上,提出一种基于Gabor小波特征的标识确认方案.该方案利用这类小波函数确定的滤波器适合局部分析和多方向多尺度分析的特点,提取与水印版权标识结构信息相关的统计量,形成特征集向量,通过特征集的距离比较,在小尺寸水印质量退化情况下,实现了对水印标识的识别.分析和实验表明,该方案能够满足无线多媒体消息版权管理的需求,并且在文中分析的情况下,设备的自动识别精度可以达到95%以上,较好地支持了对无线多媒体消息的版权管理.

基于Gabor小波的渐进式着色算法

为了解决黑白图像自动染色的难题,提出了一种基于Gabor小波的渐进式着色算法。该算法首先使用Gabor小波对黑白图像的纹理特征进行分析,在此基础上,根据纹理特征差异重新定义像素的邻居关系,最后利用最优化方法对染色问题进行迭代求解。该算法主要的创新点是交互操作少,并允许用户逐步添加色彩细节。同时该算法还是天然并行的,能够利用图形处理器(GPU)进行实时计算。为该算法和当今流行的着色算法做了效果对比,并且进行了效率分析,实验结果表明了该算法的可用性和效率。

Design of silicon IP cores for biorthogonal wavelet transforms

A methodology for rapid silicon design of biorthogonal wavelet transform systems has been developed. This is based on generic, scalable architectures for the forward and inverse wavelet filters. These architectures offer efficient hardware utilisation by combining the linear phase property of biorthogonal filters with decimation and interpolation. The resulting designs have been parameterised in terms of types of wavelet and wordlengths for data and coefficients. Control circuitry is embedded within these cores that allows them to be cascaded for any desired level of decomposition without any interface logic. The time to produce silicon designs for a biorthogonal wavelet system is only the time required to run synthesis and layout tools with no further design effort required. The resulting silicon cores produced are comparable in area and performance to hand-crafted designs. These designs are also portable across a range of foundries and are suitable for FPGA and PLD implementations.

Rapid design of biorthogonal wavelet transforms

A rapid design methodology for biorthogonal wavelet transform cores has been developed based on a generic, scaleable architecture for wavelet filters. The architecture offers efficient hardware utilisation by combining the linear phase property of biorthogonal filters with decimation in a MAC-based implementation. The design has been captured in VHDL and parameterised in terms of wavelet type, data word length and coefficient word length. The control circuit is embedded within the cores and allows them to be cascaded without any interface glue logic for any desired level of decomposition. The design time to produce silicon layout of a biorthogonal wavelet system is typically less than a day. The silicon cores produced are comparable in area and performance to hand-crafted designs, The designs are portable across a range of foundries and are also applicable to FPGA and PLD implementations.

Rapid VLSI design of biorthogonal wavelet transform cores

A rapid design methodology for biorthogonal wavelet transform cores has been developed. This methodology is based on a generic, scaleable architecture for the wavelet filters. The architecture offers efficient hardware utilization by combining the linear phase property of biorthogonal filters with decimation in a MAC based implementation. The design has been captured in VHDL and parameterized in terms of wavelet type, data word length and coefficient word length. The control circuit is embedded within the cores and allows them to be cascaded without any interface glue logic for any desired level of decomposition. The design time to produce silicon layout of a biorthogonal wavelet based system is typically less than a day. The resulting silicon cores produced are comparable in area and performance to hand-crafted designs. The designs are portable across a range of foundries and are also applicable to FPGA and PLD implementations.

Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.