Célia A. Zorzo Barcelos

An automatic based nonlinear diffusion equations scheme for skin lesion segmentation

The clinical diagnosis of skin lesions is not an easy task for dermatologists. A segmentation method that makes the extraction of important characteristics of the skin lesion image can aid dermatologists in the clinical diagnosis of skin cancer. In this paper a segmentation method that combines the use of nonlinear diffusion equations and the Canny edge detector for the automatic detection of the skin edge lesions is presented. Experimental results show the efficacy of the proposed method even when the lesion is noisy or when the lesion is covered by hairs.

Anisotropic diffusion for effective shape corner point detection

This work presents a new method for detecting shape corner points. These points are characterized as having high curvature value and their detection is an important task in several applications, including motion tracking and object recognition. As noisy points also have high curvature value we propose a framework that includes smoothing and corner point localization. First, we defined a function that associates each shape contour point with its curvature value, then the proposed method automatically smooths this function via an anisotropic filter based on an evolutionary equation, simultaneously localizing the corner points. The results obtained show that the proposed model has good performance when compared with three other techniques.

Image feature descriptor based on shape salience points

Abstract The work presented in this article aims at shape feature extraction and description. In this paper, we propose a shape-based image retrieval technique using salience points to describe shapes. The saliences of a shape are defined as the higher curvature points along the shape contour. The technique presented here consists of: a salience point detector; a salience representation using angular relative position and curvature value analyzed from a multi-scale perspective; and a matching algorithm considering local and global features to calculate the dissimilarity. The proposed technique is robust to noise and presents good performance when dealing with shapes of different classes but visually similar. The experiments were made in order to illustrate the performance of the proposed technique, and the results show the good performance of our method when compared with other shape-based methods in literature.

Adaptive Image Retrieval through the Use of a Genetic Algorithm

In this work an image retrieval system adaptable to users interests by the use of relevance feedback via genetic algorithm is presented. The retrieval process is based on local similarity patterns. The goal of the genetic algorithm is to infer weights for regions and features that better translate the users requirements producing better quality rankings. The genetic algorithm used has as its main innovation an order-based fitness function, which is appropriate to the ranking requirements of a majority of the users. This fitness function will quickly drive the genetic algorithm in the process of searching for an optimal solution. Evaluations in several databases have shown the robustness and efficiency of the proposed retrieval method even when the query is a sketch or damaged image.Logic and proofs constitute key factors in increasing the user trust towards the semantic Web. Defeasible reasoning is a useful tool towards the development of the logic layer of the semantic Web architecture. However, having a solid mathematical notation, it may be confusing to end users, who often need graphical trace and explanation mechanisms for the derived conclusions. In a previous work of ours, we outlined a methodology for representing defeasible logic rules, utilizing directed graphs that feature distinct node and connection types. However, visualizing a defeasible logic rule base also involves the placement of the multiple graph elements in an intuitive way, a non-trivial task that aims at improving user comprehensibility. This paper presents a stratification algorithm for visualizing defeasible logic rule bases that query and reason about RDF data as well as a tool that applies this algorithm.

Image restoration using digital inpainting and noise removal

Inpainting and denoising are two important tasks in the field of image processing with broad applications in image and vision analysis. In this paper, we present a new approach for image restoration. Our method simultaneously fills in missing, corrupted, or undesirable information while it removes noise. The denoising is performed by the smoothing equation working inside and outside of the inpainting domain but in completely different ways. Inside the inpainting domain, the smoothing is carried out by the Mean Curvature Flow, while the smoothing of the outside of the inpainting domain is carried out in a way as to encourage smoothing within a region and discourage smoothing across boundaries. Besides smoothing, the approach here presented permits the transportation of available information from the outside towards the inside of the inpainting domain. This combination permits the simultaneous use of filling-in and differentiated smoothing of different regions of an image. The experimental results show the effective performance of the combination of these two procedures in restoring scratched photos, disocclusion (or removal of entire objects from the image) in vision analysis and text removal from images.

Image inpainting and denoising by nonlinear partial differential equations

A new approach is presented for recovering shapes from noisy and damaged images as well as the filling in of missing information or the removal of objects from an image. The procedure allows for the denoising and inpainting of images simultaneously. The denoising is performed by the smoothing equation working inside and outside of the inpainting domain but in completely different ways. Inside the inpainting domain the smoothing is carried out by the mean curvature flow, while the smoothing of the outside of the inpainting domain is carried out in a way to encourage smoothing within a region and discourage smoothing across boundaries. Besides smoothing, the approach here presented permits the transportation of available information from the outside towards the inside of the inpainting domain. This combination permits the simultaneous use of filling-in and differentiated smoothing of different regions of an image. The experimental results show the effective performance of the combination of these two procedures in image restoration.

An image retrieval system using shape salience points

Content-Based Image Retrieval (CBIR) systems have been developed to support the image retrieval based on image properties, such as color, shape and texture. In this paper, we are concerned with shape-based image retrieval. In this context, we propose a method to describe shapes based on salience points. The proposed descriptor utilizes a salience detector which is robust to noise, and an elastic matching algorithm to measure the similarity between two shapes represented by their salience points. The proposed approach is robust to noise and gives good results in recognizing shapes of the same class, even if they are represented by a different number of salience points.

A soft multiphase segmentation model via Gaussian mixture

This paper developed a new soft multiphase segmentation model. Different from most maximum-likelihood based and Bayesian-estimation based methods, the proposed model introduced a geometrical constraint- “the length term” into the model which makes the model more rigorous in analysis while still flexible in implementation. Moreover, the model used mixed Gaussian with different parameters for different patterns. As a result, it is more robust to noise. The experiments demonstrated its high efficiency.

A shape contour descriptor based on salience points

In this paper, we propose a shape-based image retrieval technique using salience points to describe shapes. This technique consists of a salience point detector robust to noise, a salience representation using angular relative position and curvature value, invariant to rotation, translation and scaling, and an elastic matching algorithm to analyze the similarity. The proposed technique is robust to noise and presents good performance when dealing with shapes of different class but visually similar. The experiments were made in order to illustrate the performance of the proposed technique. The results show the good performance of our method when comparing with other shape-based methods in the literature.

Suspicious event recognition using infrared imagery

The societys concern about safety is growing every day and with it the demand for intelligent surveillance systems with the minimal human intervention possible. In this work we identify suspicious events that could take place in a parking lot based on infrared imagery. The object segmentation process is performed using a dynamic background-subtraction technique which robustly adapts detection to illumination changes. Segmented objects are tracked by a two phase function: prediction and correction. During the tracking process the objects are classified into two categories: Person and Vehicles, based on features like size, velocity and temperature. With the objects correctly segmented and classified using features like velocity and time stood in one spot, it is possible to identify suspicious events occurring in the monitored area. Experimental results are presented to demonstrate the effectiveness of the proposed technique to recognize suspicious events.