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Thesis(M.S.)--Auburn University, 2006. / Abstract. Vita. Includes bibliographic references.

Traditional remote sensing image classification has primarily relied on image spectral information and texture information was ignored or not fully utilized. Existing remote sensing software packages have very limited functionalities with respect to texture information extraction and utilization. This research focuses on the use of multi-scale image texture analysis techniques using Gabor filter banks and Wavelet transformations. Gabor filter banks model texture as irradiance patterns in an image over a limited range of spatial frequencies and orientations. Using Gabor filters, each image texture can be differentiated with respect to its dominant spatial frequency and orientation. Wavelet transformations are useful for decomposition of an image into a set of images based on an orthonormal basis. Dyadic transformations are applied to generate a multi-scale image pyramid which can be used for texture analysis. The analysis of texture is carried out using both artificial textures and remotely sensed image corresponding to natural scenes. This research has shown that texture can be extracted and incorporated in conventional classification algorithms to improve the accuracy of classified results. The applicability of Gabor filter banks and Wavelets is explored for classifying and segmenting remote sensing imagery for geographical applications. A qualitative and quantitative comparison between statistical texture indicators and multi-scale texture indicators has been performed. Multi-scale texture indicators derived from Gabor filter banks have been found to be very effective due to the nature of their configurability to target specific textural frequencies and orientations in an image. Wavelet transformations have been found to be effective tools in image texture analysis as they help identify the ideal scale at which texture indicators need to be measured and reduce the computation time taken to derive statistical texture indicators. A robust set of software tools for texture analysis has been developed using the popular .NET and ArcObjects. ArcObjects has been chosen as the API of choice, as these tools can be seamlessly integrated into ArcGIS. This will aid further exploration of image texture analysis by the remote sensing community.

A new two-dimensional transform is derived and implemented that is able to discriminate with respect to angular and radial frequency. This octave-band directional filter bank (OBDFB) is maximally decimated, has a separable polyphase implmentation, provides perfect reconstruction, and can be implemented in a tree structure allowing for a somewhat arbitrary number of angular and radial divisions. This decomposition is based on the directional filter bank (DFB) and is compared to other transforms with similar properties. Additionally, the OBDFB is used in three applications. Texture segmentation results are provided with comparisons to both decimated and undecimated transforms. With hyperspectral data, the OBDFB is used to increase classification accuracy using texture augmentation and likelihood score combination. Finally, ultrasound despeckling is addressed with respect to real-time implementations, and subjective test results are presented. A non-uniform two-dimensional transform is also designed that is a modified version of the OBDFB. It is rationally sampled and maximally decimated, but it provides both angular and radial frequency passbands from the initial stage instead of making separate divisions like the OBDFB. It also does not create subband boundaries on the principal frequency axes and allows for further decomposition as well.

Traditional remote sensing image classification has primarily relied on image spectral information and texture information was ignored or not fully utilized. Existing remote sensing software packages have very limited functionalities with respect to texture information extraction and utilization. This research focuses on the use of multi-scale image texture analysis techniques using Gabor filter banks and Wavelet transformations. Gabor filter banks model texture as irradiance patterns in an image over a limited range of spatial frequencies and orientations. Using Gabor filters, each image texture can be differentiated with respect to its dominant spatial frequency and orientation. Wavelet transformations are useful for decomposition of an image into a set of images based on an orthonormal basis. Dyadic transformations are applied to generate a multi-scale image pyramid which can be used for texture analysis. The analysis of texture is carried out using both artificial textures and remotely sensed image corresponding to natural scenes. This research has shown that texture can be extracted and incorporated in conventional classification algorithms to improve the accuracy of classified results. The applicability of Gabor filter banks and Wavelets is explored for classifying and segmenting remote sensing imagery for geographical applications. A qualitative and quantitative comparison between statistical texture indicators and multi-scale texture indicators has been performed. Multi-scale texture indicators derived from Gabor filter banks have been found to be very effective due to the nature of their configurability to target specific textural frequencies and orientations in an image. Wavelet transformations have been found to be effective tools in image texture analysis as they help identify the ideal scale at which texture indicators need to be measured and reduce the computation time taken to derive statistical texture indicators. A robust set of software tools for texture analysis has been developed using the popular .NET and ArcObjects. ArcObjects has been chosen as the API of choice, as these tools can be seamlessly integrated into ArcGIS. This will aid further exploration of image texture analysis by the remote sensing community.

The estimation of optical flow has become a vital research field in image sequence analysis especially in past two decades, which found applications in many fields such as stereo optics, video compression, robotics and computer vision. In this thesis, the complex wavelet based algorithm for the estimation of optical flow developed by Magarey and Kingsbury is implemented and investigated. The algorithm is based on a complex version of the discrete wavelet transform (CDWT), which analyzes an image through blocks of filtering with a set of Gabor-like kernels with different scales and orientations. The output is a hierarchy of scaled and subsampled orientation-tuned subimages. The motion estimation algorithm is based on the relationship between translations in image domain and phase shifts in CDWT domain, which is satisfied by the shiftability and interpolability property of CDWT. Optical flow is estimated by using this relationship at each scale, in a coarse-to-fine (hierarchical) manner, where information from finer scales is used to refine the estimates from coarser scales. The performance of the motion estimation algorithm is investigated with various image sequences as input and the effects of the options in the algorithm like curvature-correction, interpolation kernel between levels and some parameter values like confidence threshold iv maximum number of CDWT levels and minimum finest level of detail are also experimented and discussed. The test results show that the method is superior to other well-known algorithms in estimation accuracy, especially under high illuminance variations and additive noise.

Autonomous vehicles face various challenges under difficult terrain conditions such as marginally rural or back-country roads, due to the lack of lane information, road signs or traffic signals. In this thesis, we investigate a novel approach of using Deep Neural Networks (DNNs) to classify off-road surfaces into the types of terrains with the aim of supporting autonomous navigation in unstructured environments. For example, off-road surfaces can be classified as asphalt, gravel, grass, mud, snow, etc. Images from the camera mounted on a mining truck were used to perform semantic segmentation and to classify road surface types. Camera images were segmented manually for training into sets of 16 and 9 classes, for all relevant classes and the drivable classes respectively. A small but diverse dataset of 100 images was augmented and compiled along with nearby frames from the video clips to expand this dataset. Neural networks were used to test the performance for the classification under these off-road conditions. Pre-trained AlexNet was compared to the networks without pre-training. Gabor filters, known to distinguish textured surfaces, was further used to improve the results of the neural network. The experiments show that pre-trained networks perform well with small datasets and many classes. A combination of Gabor filters with pre-trained networks can establish a dependable navigation path under difficult terrain conditions. While the results seem positive for images similar to the training image scenes, the networks fail to perform well in other situations. Though the tests imply that larger datasets are required for dependable results, this is a step closer to making the autonomous vehicles drivable under off-road conditions. / Autonoma fordon står inför olika utmaningar under svåra terrängförhållanden som landsbygds- eller skogsvägar på grund av bristen av körfältinformation, vägskyltar och trafikljus. I denna avhandling undersöker vi ett nytt tillvägagångssätt att använda Djupa Neurala Nätverk (DNN) för att klassificera terrängytor utifrån deras körbarhet i syfte att stödja autonom navigering i ostrukturerade miljöer.Till exempel kan terrängytor klassificeras som asfalt, grus, gräs, lera, snö etc. Bilder från kameran monterad på en gruvbil användes för att utföra semantisk segmentering och klassificera vägytor. Bilderna delades manuellt upp i träningsset på 16 samt 9 klasser för alla relevanta klasser respektive körbara klasser. Ett litet men mångsidigt dataset med 100 bilder förstärktes med närliggande bilder från videoklippen för att expandera detta dataset. Neurala nätverk användes för att testa prestandan hos klassificeringen under dessa terrängförhållanden. Det förtränade nätverket AlexNet jämfördes med nätverken utan träning. Gaborfilter, kända för att särskilja texturerade ytor, användes vidare för att förbättra resultaten av det neurala nätverket. Experimenten visar att förtränade nätverk presterar bra med små dataset och många klasser. En kombination av Gaborfilter med förtränade nätverk kan skapa en pålitlig navigationsväg under svåra terrängförhållanden. Även om resultaten verkar positiva för bilder som liknar träningsbildscenen presterar nätverken inte bra i andra situationer. Även om testen tyder på att stora dataset krävs för tillförlitliga resultat, är detta ett steg närmare att göra de autonoma bilarna körbara i svåra terrängförhållanden.

Automated Face Recognition is the process of determining a subject's identity from digital imagery of their face without user intervention. The term in fact encompasses two distinct tasks; Face Verficiation is the process of verifying a subject's claimed identity while Face Identification involves selecting the most likely identity from a database of subjects. This dissertation focuses on the task of Face Verification, which has a myriad of applications in security ranging from border control to personal banking. Recently the use of 3D facial imagery has found favour in the research community due to its inherent robustness to the pose and illumination variations which plague the 2D modality. The field of 3D face recognition is, however, yet to fully mature and there remain many unanswered research questions particular to the modality. The relative expense and specialty of 3D acquisition devices also means that the availability of databases of 3D face imagery lags significantly behind that of standard 2D face images. Human recognition of faces is rooted in an inherently 2D visual system and much is known regarding the use of 2D image information in the recognition of individuals. The corresponding knowledge of how discriminative information is distributed in the 3D modality is much less well defined. This dissertations addresses these issues through the use of decompositional techniques. Decomposition alleviates the problems associated with dimensionality explosion and the Small Sample Size (SSS) problem and spatial decomposition is a technique which has been widely used in face recognition. The application of decomposition in the frequency domain, however, has not received the same attention in the literature. The use of decomposition techniques allows a map ping of the regions (both spatial and frequency) which contain the discriminative information that enables recognition. In this dissertation these techniques are covered in significant detail, both in terms of practical issues in the respective domains and in terms of the underlying distributions which they expose. Significant discussion is given to the manner in which the inherent information of the human face is manifested in the 2D and 3D domains and how these two modalities inter-relate. This investigation is extended to cover also the manner in which the decomposition techniques presented can be recombined into a single decision. Two new methods for learning the weighting functions for both the sum and product rules are presented and extensive testing against established methods is presented. Knowledge acquired from these examinations is then used to create a combined technique termed Log-Gabor Templates. The proposed technique utilises both the spatial and frequency domains to extract superior performance to either in isolation. Experimentation demonstrates that the spatial and frequency domain decompositions are complimentary and can combined to give improved performance and robustness.

Um mosaico é o conjunto de fotos de uma determinada área, recortadas e montadas técnica e artísticamente, de forma a dar a impressão de que todo o conjunto é uma única fotografia. No caso de fotografias aéreas, sua utilização soluciona o problema da necessidade de se retratar uma área de interesse mais extensa do que o campo de cobertura das lentes da câmera utilizada. O foco deste trabalho é a criação automática de mosaicos buscando encontrar a posição real de um conjunto de imagens imagens adquiridas em baixa altitude, de baixa escala, em relação à um Mapa de Base, de escala maior, realizando, assim, uma correlação entre imagens de escalas diferentes. Este problema é abordado por técnicas de análise multi-escala, mais precisamente, pela utilização de filtros de Gabor. A metodologia desenvolvida utiliza um banco de filtros de Gabor aplicado sobre uma imagem de referência de modo que, a partir da aplicação destes filtros sobre a mesma, seja possível gerar um processo automático de geração do mosaico para o restante do conjunto de imagens. Experimentos realizados utilizando o método proposto demonstram a eficácia do mesmo para imagens com texturas de orientação marcante, como o caso de imagens aéreas de plantação de eucaliptos / A mosaic is a set of pictures of a given area, technically and artistically cut and ?glued? together, giving the impression that the entire set resembles a single picture. For aerial photography, the use of mosaics solves the problem of imaging an area of interest whose dimension is much larger than that covered by the majority of the cameras available. This work focuses on the automatic creation of mosaics and aims to compute the real position of a set of images acquired at low altitudes (lower scale), in relation with a base map larger scale), by correlating images in different scales. Multi-scale analysis techniques, in particular, the Gabor filters, constitute an approach to this problem. The proposed methodology uses a bank of Gabor filters applied over a reference image in a way that an automatic process of mosaic generation, with the remaining set of images, could be carried out. Experiments have shown the efficiency of the proposed technique especially for images with clear textural orientation, for example, the case of aerial photographs of eucalyptus plantations