Identification of foliar diseases in cotton crop

The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. © 2012 Taylor & Francis Group.

Fast identification of individuals based on iris characteristics for biometric systems

Nowadays, systems based on biométrie techniques have a wide acceptance in many different areas, due to their levels of safety and accuracy. A biometrie technique that is gaining prominence is the identification of individuals through iris recognition. However, to be proficiently used these systems must process their recognition task as fast as possible. The goal of this work has been the development of an iris recognition method to produce results rapidly, yet without losing the recognition accuracy. The experimental results show that the method is quite promising. © 2012 Taylor & Francis Group.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Assessment of eyebrow position before and after upper eyelid blepharoplasty

Objective: Assess the occurrence of secondary brow ptosis after upper lid blepharoplasty. Methods: Forty-five individuals (n90 brows) submitted to upper lid blepharoplasty, were assessed by means of a comparative analysis using pre- and post-operatively digital photographs, in the primary position of the eye. The images were processed using ImageJ software, transferred to a computer, to an electronic Microsoft Excel 2002® worksheet. Angular measurements were used, taking the lateral canthal angle of the brow, the most medial point of the brow, the medial canthal angle and the lateral canthal angle of the lid as anatomical reference points. When the outer angles were reduced or the inner angles increased after surgery this was considered a brow ptosis. Individuals who had undergone lid surgery associated with the eyebrow, previous eyebrow surgery and those with eyelid ptosis were excluded. The difference between the pre-operative and post-operative measurements were analyzed statistically using the Student's t-test for paired samples and the angular variation was compared with their corresponding contralateral sample using Wilcoxon's non-parametric test. Results: The measurements obtained after the blepharoplasty show significant variations from those before the surgery, indicating that the correction of redundant tissues in the brow accentuates the tendency of the eyebrow to move down. The alterations are more important in the lateral portion of the eyebrow and they occur bilaterally. Conclusion: The assessment of angular measurements obtained pre- and post-operatively showed that there are secondary changes in the position of the eyebrow as a result of upper eyelid blepharoplasty. © 2012 Informa Healthcare USA, Inc.

A semi-automatic method for indirect orientation of aerial images using ground control lines extracted from airborne laser scanner data

This paper presents a method for indirect orientation of aerial images using ground control lines extracted from airborne Laser system (ALS) data. This data integration strategy has shown good potential in the automation of photogrammetric tasks, including the indirect orientation of images. The most important characteristic of the proposed approach is that the exterior orientation parameters (EOP) of a single or multiple images can be automatically computed with a space resection procedure from data derived from different sensors. The suggested method works as follows. Firstly, the straight lines are automatically extracted in the digital aerial image (s) and in the intensity image derived from an ALS data-set (S). Then, correspondence between s and S is automatically determined. A line-based coplanarity model that establishes the relationship between straight lines in the object and in the image space is used to estimate the EOP with the iterated extended Kalman filtering (IEKF). Implementation and testing of the method have employed data from different sensors. Experiments were conducted to assess the proposed method and the results obtained showed that the estimation of the EOP is function of ALS positional accuracy.