Teresa E. Alarcón

MFCA: matched filters with cellular automata for retinal vessel detection

Blood vessel extraction is an important step for abnormality detection and for obtaining good retinopathy diabetic diagnosis in digital retinal images. The use of filter bank has shown to be a powerful technique for detecting blood vessels. In particular, the Matched Filter is appropriate and efficient for this task and in combination with other methods the blood vessel detection can be improved. We propose a combination of the Matched Filter with a segmentation strategy by using a Cellular Automata. The strategy presented here is very efficient and experimentally yields competitive results compared with others methods of the state of the art.

Segmentation of carbon nanotube images through an artificial neural network

Segmentation of carbon nanotube images is an important task for nanotechnology. The segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work, we propose two segmentation algorithms for carbon nanotube images. Each algorithm includes three stages: preprocessing, segmentation and postprocessing. The first one is applied on images from scanning electron microscopy and employs a matched filter bank in the preprocessing step followed by a neural network in the segmenting phase. The second algorithm uses the Perona–Malik filter for enhancing the nanotube information. The segmentation phase is composed of the relaxed Otsu’s threshold and an artificial neural network. This algorithm is applied on images from transmission electron microscopy. The postprocessing stage, for both algorithms, is based on mathematical morphology. The performance of the proposed algorithms is numerically evaluated by using real image databases, manually segmented by an expert. The algorithm for segmentation of scanning electron microscopy achieved 92.74% of overall accuracy, while the algorithm for segmentation of transmission electron microscopy obtained an accuracy of 73.99% if the whole image is considered. A performance improvement is accomplished if only the region of interest is segmented, arriving to 84.19% of overall accuracy.

Bayesian Scheme for Interactive Colourization, Recolourization and Image/Video Editing

We propose a general image and video editing method based on a Bayesian segmentation framework. In the first stage, classes are established from scribbles made by a user on the image. These scribbles can be considered as a multi‐map (multi‐label map) that defines the boundary conditions of a probability measure field to be computed for each pixel. In the second stage, the global minima of a positive definite quadratic cost function with linear constraints, is calculated to find the probability measure field. The components of such a probability measure field express the degree of each pixel belonging to spatially smooth classes. Finally, the computed probabilities (memberships) are used for defining the weights of a linear combination of user provided colours or effects associated to each class. The proposed method allows the application of different operators, selected interactively by the user, over part or the whole image without needing to recompute the memberships. We present applications to colourization, recolourization, editing and photomontage tasks.

A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs

The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow.

Segmentation of Carbon Nanotube Images Through an Artificial Neural Network

The segmentation of nanotube is an important task for Nanotechnology. The performance of segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work we propose two algorithms for segmenting carbon nanotube images. The first one uses a matched filter bank in the preprocessing step and a neural network for segmenting images from Scanning Electron Microscopy. The second algorithm includes the Perona-Malik filter for enhancing the nanotube information. The segmentation phase is composed by the relaxed Otsu’s threshold and an artificial neural network. This algorithm is applied on images from Transmission Electron Microscopy. After the segmentation, for both algorithms, a preprocessing based on mathematical morphology is carried out. The performance of the proposed algorithms is numerically evaluated by using real image databases. Overall accuracy of 92.74 % and 73.99 % were obtained for the first and second algorithm respectively.

A Supervised Segmentation Algorithm for Crop Classification Based on Histograms Using Satellite Images

Recognizing different types of crops trough satellite imagery is an important application of Digital Image Processing in Agriculture. A supervised algorithm for identifying different types of crops is pro- posed. In the training stage, the studied images are preprocessed using a bilateral filter, and then the histogram of intensity levels is constructed for every crop class. The segmentation stage begins with the assignment of the likelihood of each pixel to belong to each class, which is based on the histogram information. Finally the segmentation is obtained using Gauss-Markov Measure Field. For this research Landsat-5 TM satellite images are used. The experimental work included synthetic and real images. In the case of the real image, the ground truth image was given by an expert. The results of the proposed algorithm were compared with other methods such as Maximum likelihood, Fisher linear likelihood, and Minimum Euclidean distance, among others.

Frequency Filter Bank for Enhancing Carbon Nanotube Images

Improving digital images of carbon nanotubes is an important task for characterizing nanotube structures in Nanoscience and Nanotechnology. A two-step algorithm is proposed for enhancing the information of carbon nanotube images, which are obtained by a scanning electron microscopy. In the first step it is carried out the characterization of the intensity profile of the nanotube by using the first and second derivatives along with the local variance. Then, for analyzing the intensity profile of the nanotubes, an adaptive spatial filter is designed. The first step allows to represent the intensity profile of the nanotube through a Gaussian model. In the second step, a Gaussian-matched filter Bank is designed in the frequency domain for enhancing the nanotube information, considering different values of thickness and orientation for the filter bank.

Characterization of Nanotube Structures Using Digital-Segmented Images

An automatic algorithm for measuring the thickness of carbon nanotubes is presented. The proposed algorithm is based on the computation of the thinning body of nanotubes. The main challenge for measuring the thickness of a nanotube is its isolation, due to the overlapping between nanotubes that typically appears in this type of images. In particular, an algorithm for solving the nanotube overlapping problem in previously-segmented images has also been elaborated. The performance of the algorithm is evaluated through a collection of segmented-images which are obtained from real carbon nanotubes using different types of electronic microscopes. The results of the algorithm are compared with measurements, a ground truth, provided by a nanotechnologist.

Color Categorization Models for Color Image Segmentation

In 1969, Brent Berlin and Paul Kay presented a classic study of color naming where experimentally demonstrated that all languages share a universal color assignment system of 11 basic color categories. Based on this work, new color categorization models have appeared in order to confirm this theory. Some of these models assign one category to each color in a certain color space, while other models assign a degree of membership to each category. The degree of membership can be interpreted as the probability of a color to belong to a color category. In the first part of this work we review some color categorization models: discrete and fuzzy based models. Then, we pay special attention to a recent color categorization model that provides a probabilistic partition of a color space, which was proposed by Alarcon and Marroquin in 2009. The proposal combines the color categorization model with a probabilistic segmentation algorithm and also generalizes the probabilistic segmentation algorithm so that one can include interaction between categories. We present some experiments of color image segmentation and applications of color image segmentation to image and video recolourization and tracking.

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources

Classification methods based on Gaussian Markov Measure Field Models and other probabilistic approaches have to face the problem of construction of the likelihood. Typically, in these methods, the likelihood is computed from 1D or 3D histograms. However, when the number of information sources grows, as in the case of satellite images, the histogram construction becomes more difficult due to the high dimensionality of the feature space. In this work, we propose a generalization of Gaussian Markov Measure Field Models and provide a probabilistic segmentation scheme, which fuses multiple information sources for image segmentation. In particular, we apply the general model to classify types of crops in satellite images. The proposed method allows us to combine several feature spaces. For this purpose, the method requires prior information for building a 3D histogram for each considered feature space. Based on previous histograms, we can compute the likelihood of each site of the image to belong to a class. The computed likelihoods are the main input of the proposed algorithm and are combined in the proposed model using a contrast criteria. Different feature spaces are analyzed, among them are 6 spectral bands from LANDSAT 5 TM, 3 principal components from PCA on 6 spectral bands and 3 principal components from PCA applied on 10 vegetation indices. The proposed algorithm was applied to a real image and obtained excellent results in comparison to different classification algorithms used in crop classification.