Letícia Rittner

New Tensorial Representation of Color Images: Tensorial Morphological Gradient Applied to Color Image Segmentation

This paper proposes a new Tensorial Representation of HSI color images, where each pixel is a 2 times 2 second order tensor, that can be represented by an ellipse. A proposed tensorial morphological gradient (TMG) is defined as the maximum dissimilarity over the neighborhood determined by a structuring element, and is used in the watershed segmentation framework. Many tensor dissimilarity functions are tested and other color gradients are compared. The comparison uses a new methodology for qualitative evaluation of color image segmentation by watershed, where the watershed lines of the n most significant regions are overlaid on the original image for visual comparison. Experiments show that the TMG using Frobenius norm dissimilarity function presents superior segmentation results, in comparison to other tested gradients.

Pearson's Correlation Coefficient for Discarding Redundant Information in Real Time Autonomous Navigation System

Lately, many applications for control of autonomous vehicles are being developed and one important aspect is the excess of information, frequently redundant, that imposes a great computational cost in data processing. Based on the fact that real-time navigation systems could have their performance compromised by the need of processing all this redundant information (all images acquired by a vision system, for example), this work proposes an automatic image discarding method using the Pearsons correlation coefficient (PCC). The proposed algorithm uses the PCC as the criteria to decide if the current image is similar to the reference image and could be ignored or if it contains new information and should be considered in the next step of the process (identification of the navigation area by an image segmentation method). If the PCC indicates that there is a high correlation, the image is discarded without being segmented. Otherwise, the image is segmented and is set as the new reference frame for the subsequent frames. This technique was tested in video sequences and showed that more than 90% of the images can be discarded without loss of information, leading to a significant reduction of computational time necessary to identify the navigation area.

Extinction Profiles for the Classification of Remote Sensing Data

With respect to recent advances in remote sensing technologies, the spatial resolution of airborne and spaceborne sensors is getting finer, which enables us to precisely analyze even small objects on the Earth. This fact has made the research area of developing efficient approaches to extract spatial and contextual information highly active. Among the existing approaches, morphological profile and attribute profile (AP) have gained great attention due to their ability to classify remote sensing data. This paper proposes a novel approach that makes it possible to precisely extract spatial and contextual information from remote sensing images. The proposed approach is based on extinction filters, which are used here for the first time in the remote sensing community. Then, the approach is carried out on two well-known high-resolution panchromatic data sets captured over Rome, Italy, and Reykjavik, Iceland. In order to prove the capabilities of the proposed approach, the obtained results are compared with the results from one of the strongest approaches in the literature, i.e., APs, using different points of view such as classification accuracies, simplification rate, and complexity analysis. Results indicate that the proposed approach can significantly outperform its alternative in terms of classification accuracies. In addition, based on our implementation, profiles can be generated in a very short processing time. It should be noted that the proposed approach is fully automatic.

A comparison between k-Optimum Path Forest and k-Nearest Neighbors supervised classifiers

This paper presents the k-Optimum Path Forest (k-OPF) supervised classifier, which is a natural extension of the OPF classifier. k-OPF is compared to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers, and we see that k-OPF and k-NN have many similarities. This work shows that the k-OPF is equivalent to the k-NN classifier when all training samples are used as prototypes. Simulations comparing the accuracy results, the decision boundaries and the processing time of the classifiers are presented to experimentally validate our hypothesis. Also, we prove that OPF using the max cost function and the NN supervised classifiers have the same theoretical error bounds.

Hyperspectral Data Classification Using Extended Extinction Profiles

This letter proposes a new approach for the spectral-spatial classification of hyperspectral images, which is based on a novel extrema-oriented connected filtering technique, entitled as extended extinction profiles. The proposed approach progressively simplifies the first informative features extracted from hyperspectral data considering different attributes. Then, the classification approach is applied on two well-known hyperspectral data sets, i.e., Pavia University and Indian Pines, and compared with one of the most powerful filtering approaches in the literature, i.e., extended attribute profiles. Results indicate that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images automatically and swiftly. In addition, an array-based node-oriented max-tree representation was carried out to efficiently implement the proposed approach.

A tensorial framework for color images

This paper proposes a new tensorial color representation, obtained by making a correspondence between color models (HSL, IHSL, HSV, RGB and CIELUV) and tensors. Based on this representation, a proposed tensorial morphological gradient (TMG), defined as the maximum dissimilarity over the neighborhood, was tested using several tensor similarity measures. Experimental results illustrate which color models are more suitable to the proposed tensorial representation and which measures give best results in the TMG computation. The watershed transform was used to demonstrate that the proposed representation and the TMG can be applied to segment color images. A quantitative analysis of segmentation results was also conducted.

Diffusion Tensor Imaging Segmentation by Watershed Transform on Tensorial Morphological Gradient

While scalar image segmentation has been studied extensively, diffusion tensor imaging (DTI) segmentation is a relatively new and challenging task. Either existent segmentation methods have to be adapted to deal with tensorial information or completely new segmentation methods have to be developed to accomplish this task. Alternatively, what this work proposes is the computation of a tensorial morphological gradient of DTI, and its segmentation by IFT-based watershed transform. The strength of the proposed segmentation method is its simplicity and robustness, consequences of the tensorial morphological gradient computation. It enables the use, not only of well known algorithms and tools from the mathematical morphology, but also of any other segmentation method to segment DTI, since the computation of the tensorial morphological gradient transforms tensorial images in scalar ones. In order to validate the proposed method, synthetic diffusion tensor fields were generated, and Gaussian noise was added to them. A set of real DTI was also used in the method validation. All segmentation results confirmed that the proposed method is capable to segment different diffusion tensor images, including noisy and real ones.

A Comparison Between Extinction Filters and Attribute Filters

Attribute filters and extinction filters are connected filters used to simplify greyscale images. The first kind is widely explored in the image processing literature, while the second is not much explored yet. Both kind of filters can be efficiently implemented on the max-tree. In this work, we compare these filters in terms of processing time, simplification of flat zones and reduction of max-tree nodes. We also compare their influence as a pre-processing step before extracting affine regions used in matching and pattern recognition. We perform repeatability tests using extinction filters and attribute filters, set to preserve the same number of extrema, as a pre-processing step before detecting Hessian-Affine and Maximally Stable Extremal Regions (MSER) affine regions. The results indicate that using extinction filters as pre-processing obtain a significantly higher (more than 5% on average) number of correspondences on the repeatability tests than the attribute filters. The results in processing natural images show that preserving 5% of images extrema using extinction filters achieve on average 95% of the number of correspondences compared to applying the affine region detectors directly to the unfiltered images, and the average number of max-tree nodes is reduced by a factor greater than 3. Therefore, we can conclude that extinction filters are better than attribute filters with respect to preserving the number of correspondences found by affine detectors, while simplifying the max-tree structure. The use of extinction filters as a pre-processing step is recommended to accelerate image recognition tasks.

An array-based node-oriented max-tree representation

This paper presents an array-based node-oriented structure for the max-tree representation, which allows direct access and flexible manipulation of its nodes, and is more suitable for OpenMP parallel processing. The proposed structure is based on two arrays called node array (NA), which stores attributes of the nodes, and node index (NI), which indicates the node that each pixel belongs to. We compare it with the pixel-oriented max-tree representation based on a parent array (parent) and an ordering array (S) that allows tree traversals. We show that our max-tree representation requires less memory when the ratio between the number of image pixels and max-tree nodes is greater than 1.6, which is often the case. It is more flexible, and can compute some attributes, such as height and dynamics, with a complexity linear on the number of max-tree nodes instead of the number of image pixels. In our experiments our structure computed the height attribute on average 11.4 faster than the parent/S representation. Also, for a single area-open filter, the sequential implementation of our structure is on average 1.14 times slower and the parallel implementation in a 4-core CPU is 1.2 times faster than the parent/S structure. For an area-open filter followed by the hmax filter, our sequential implementation is 1.34 times faster and our parallel implementation is 2.32 times faster than the parent/S structure.

Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging

Abstract. Brain white matter lesions found upon magnetic resonance imaging are often observed in psychiatric or neurological patients. Individuals with these lesions present a more significant cognitive impairment when compared with individuals without them. We propose a computerized method to distinguish tissue containing white matter lesions of different etiologies (e.g., demyelinating or ischemic) using texture-based classifiers. Texture attributes were extracted from manually selected regions of interest and used to train and test supervised classifiers. Experiments were conducted to evaluate texture attribute discrimination and classifiers’ performances. The most discriminating texture attributes were obtained from the gray-level histogram and from the co-occurrence matrix. The best classifier was the support vector machine, which achieved an accuracy of 87.9% in distinguishing lesions with different etiologies and an accuracy of 99.29% in distinguishing normal white matter from white matter lesions.