Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and an Artificial Neural Network

A texture–based weed classification method was developed. The method consisted of a low–level Gabor wavelets–based feature extraction algorithm and a high–level neural network–based pattern recognition algorithm. This classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass categories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur, velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied. After processing 40 sample images with 20 samples from each class, the results showed that the method was capable of classifying all the samples correctly with high computational efficiency, demonstrating its potential for practical implementation under real–time constraints

Texture Classification by Wavelet Packet Signatures

This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (D20) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D6) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture

Image segmentation in the wavelet domain using N-cut framework

We introduce a wavelet domain image segmentation algorithm based on Normalized Cut (NCut) framework in this thesis. By employing the NCut algorithm we solve the perceptual grouping problem of image segmentation which aims at the extraction of the global impression of an image. We capitalize on the reduced set of data to be processed and statistical features derived from the wavelet-transformed images to solve graph partitioning more efficiently than before. Five orientation histograms are computed to evaluate similarity/dissimilarity measure of local structure. We use properties of the wavelet transform filtering to capture edge information in vertical, horizontal and diagonal orientations. This approach allows for direct processing of compressed data and results in faster implementation of NCut framework than that in the spatial domain and also decent quality of segmentation of natural scene images

New contributions in overcomplete image representations inspired from the functional architecture of the primary visual cortex = Nuevas contribuciones en representaciones sobrecompletas de imágenes inspiradas por la arquitectura funcional de la corteza visual primaria

The present thesis aims at investigating parallelisms between the functional architecture of primary visual areas and image processing methods. A first objective is to refine existing models of biological vision on the base of information theory statements and a second is to develop original solutions for image processing inspired from natural vision. The available data on visual systems contains physiological and psychophysical studies, Gestalt psychology and statistics on natural images The thesis is mostly centered in overcomplete representations (i.e. representations increasing the dimensionality of the data) for multiple reasons. First because they allow to overcome existing drawbacks of critically sampled transforms, second because biological vision models appear overcomplete and third because building efficient overcomplete representations raises challenging and actual mathematical problems, in particular the problem of sparse approximation. The thesis proposes first a self-invertible log-Gabor wavelet transformation inspired from the receptive field and multiresolution arrangement of the simple cells in the primary visual cortex (V1). This transform shows promising abilities for noise elimination. Second, interactions observed between V1 cells consisting in lateral inhibition and in facilitation between aligned cells are shown efficient for extracting edges of natural images. As a third point, the redundancy introduced by the overcompleteness is reduced by a dedicated sparse approximation algorithm which builds a sparse representation of the images based on their edge content. For an additional decorrelation of the image information and for improving the image compression performances, edges arranged along continuous contours are coded in a predictive manner through chains of coefficients. This offers then an efficient representation of contours. Fourth, a study on contour completion using the tensor voting framework based on Gestalt psychology is presented. There, the use of iterations and of the curvature information allow to improve the robustness and the perceptual quality of the existing method. La presente tesis doctoral tiene como objetivo indagar en algunos paralelismos entre la arquitectura funcional de las áreas visuales primarias y el tratamiento de imágenes. Un primer objetivo consiste en mejorar los modelos existentes de visión biológica basándose en la teoría de la información. Un segundo es el desarrollo de nuevos algoritmos de tratamiento de imágenes inspirados de la visión natural. Los datos disponibles sobre el sistema visual abarcan estudios fisiológicos y psicofísicos, psicología Gestalt y estadísticas de las imágenes naturales. La tesis se centra principalmente en las representaciones sobrecompletas (i.e. representaciones que incrementan la dimensionalidad de los datos) por las siguientes razones. Primero porque permiten sobrepasar importantes desventajas de las transformaciones ortogonales; segundo porque los modelos de visión biológica necesitan a menudo ser sobrecompletos y tercero porque construir representaciones sobrecompletas eficientes involucra problemas matemáticos relevantes y novedosos, en particular el problema de las aproximaciones dispersas. La tesis propone primero una transformación en ondículas log-Gabor auto-inversible inspirada del campo receptivo y la organización en multiresolución de las células simples del cortex visual primario (V1). Esta transformación ofrece resultados prometedores para la eliminación del ruido. En segundo lugar, las interacciones observadas entre las células de V1 que consisten en la inhibición lateral y en la facilitación entre células alineadas se han mostrado eficientes para extraer los bordes de las imágenes naturales. En tercer lugar, la redundancia introducida por la transformación sobrecompleta se reduce gracias a un algoritmo dedicado de aproximación dispersa el cual construye una representación dispersa de las imágenes sobre la base de sus bordes. Para una decorrelación adicional y para conseguir más altas tasas de compresión, los bordes alineados a lo largo de contornos continuos están codificado de manera predictiva por cadenas de coeficientes, lo que ofrece una representacion eficiente de los contornos. Finalmente se presenta un estudio sobre el cierre de contornos utilizando la metodología de tensor voting. Proponemos el uso de iteraciones y de la información de curvatura para mejorar la robustez y la calidad perceptual de los métodos existentes

Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss

Models of learning in the visual system: dependence on retinal eccentricity

In the primary visual cortex of primates relatively more space is devoted to the representation of the central visual field in comparison to the representation of the peripheral visual field. Experimentally testable theories about the factors and mechanisms which may have determined this inhomogeneous mapping may provide valuable insights into general processing principles in the visual system. Therefore, I investigated to which visual situations this inhomogeneous representation of the visual field is well adapted, and which mechanisms could support its refinement and stabilization during individual development. Furthermore, I studied possible functional consequences of the inhomogeneous representation for visual processing at central and peripheral locations of the visual field. Vision plays an important role during navigation. Thus, visual processing should be well adapted to self-motion. Therefore, I assumed that spatially inhomogeneous retinal velocity distributions, caused by static objects during self-motion along the direction of gaze, are transformed on average into spatially homogeneous cortical velocity distributions. This would have the advantage that the cortical mechanisms, concerned with the processing of self-motion, can be identical in their spatial and temporal properties across the representation of the whole visual field. This is the case if the arrangement of objects relative to the observer corresponds to an ellipsoid with the observer in its center. I used the resulting flow field to train a network model of pulse coding neurons with a Hebbian learning rule. The distribution of the learned receptive fields is in agreement with the inhomogeneous cortical representation of the visual field. These results suggest that self motion may have played an important role in the evolution of the visual system and that the inhomogeneous cortical representation of the visual field can be refined and stabilized by Hebbian learning mechanisms during ontogenesis under natural viewing conditions. In addition to the processing of self-motion, an important task of the visual system is the grouping and segregation of local features within a visual scene into coherent objects. Therefore, I asked how the corresponding mechanisms depend on the represented position of the visual field. It is assumed that neuronal connections within the primary visual cortex subserve this grouping process. These connections develop after eye-opening in dependence on the visual input. How does the lateral connectivity depend on the represented position of the visual field? With increasing eccentricity, primary cortical receptive fields become larger and the cortical magnification of the visual field declines. Therefore, I investigated the spatial statistics of real-world scenes with respect to the spatial filter-properties of cortical neurons at different locations of the visual field. I show that correlations between collinearly arranged filters of the same size and orientation increase with increasing filter size. However, in distances relative to the size of the filters, collinear correlations decline more steeply with increasing distance for larger filters. This provides evidence against a homogeneous cortical connectivity across the whole visual field with respect to the coding of spatial object properties. Two major retino-cortical pathways are the magnocellular (M) and the parvocellular (P) pathways. While neurons along the M-pathway display temporal bandpass characteristics, neurons along the P-pathway show temporal lowpass characteristics. The ratio of P- to M-cells is not constant across the whole visual field, but declines with increasing retinal eccentricity. Therefore, I investigated how the different temporal response-properties of neurons of the M- and the P-pathways influence self-organization in the visual cortex, and discussed possible consequences for the coding of visual objects at different locations of the visual field. Specifically, I studied the influence of stimulus-motion on the self-organization of lateral connections in a network-model of spiking neurons with Hebbian learning. Low stimulus velocities lead to horizontal connections well adapted to the coding of the spatial structure within the visual input, while higher stimulus velocities lead to connections which subserve the coding of the stimulus movement direction. This suggests that the temporal lowpass properties of P-neurons subserve the coding of spatial stimulus attributes (form) in the visual cortex, while the temporal bandpass properties of M-neurons support the coding of spatio-temporal stimulus attributes (movement direction). Hence, the central representation of the visual field may be well adapted to the encoding of spatial object properties due to the strong contribution of P-neurons. The peripheral representation may be better adapted to the processing of motion

Ultrafast Optical Properties of La0.7Sr0.3MnO3 Thin Films

Thin film solids often exhibit different physical properties in the ultra-thin regime. Enhancement of surface to bulk ratio results in the domination of surface/interface related phenomena such as surface recombination. Moreover, in the ultra-thin regime, quantum size and quantum confinement effects can alter the band gap of the system and constrain the strain wave propagation in the thin film. Ultrafast properties of solids can also be drastically altered in the ultra-thin regime due to the aforementioned phenomena. Experimentally, observation of these phenomena is challenging due to the insufficient material to absorb and interact with the electromagnetic wave. This dissertation addresses the altercation of the ultrafast phenomena in the ultra-thin regime in complex oxide La0.7Sr0.3MnO3 (LSMO) films. To accomplish this goal, a degenerate optical ultrafast pumpprobe setup was developed to perform transient reflectivity (TR) experiments in cross linear and circular polarization configurations. Analyzing methods were ranged from multi-decay component modeling to understand the ultrafast dynamics, to the newly introduced wavelet analysis to study the propagation of transient strain waves in ultra-thin films. Moreover, density functional theory has been used as a complementary method to further prove the claims made based on the observations. Spectroscopic ellipsometry of LSMO ultra-thin films illustrate optical transitions between the substrate (STO3) and thin film’s orbitals, at the interface. It has been indicated that, in the ultra-thin regime, the dominated surface recombination results in a complete excited carriers’ energy loss in less than a few hundreds of picoseconds. Hence, the surface recombination, that is known to be detrimental for device purposes, is introduced as a phenomenon that can be useful for ultrafast switches in ultra-thin films. Moreover, the ultrafast photoinduced spin polarization of LSMO thin films exhibit a sharp decrease in ultrafast regime (~ 1 ps) which suggest LSMO thin films to be used in ultrafast magnetic switches. Wavelet analysis was introduced as an efficient method, compared to the Fourier transform, to analyze oscillatory modes superimposed on the TR signal which are caused by the propagation of strain wave longitudinally in the thin film. As a result, it has been illustrated that the sound velocity in LSMO ultra-thin films increases by decreasing the film thickness. Strong energy transfer between the thin film and the substrate has also been observed using wavelet analysis