Anderson Rocha

Image Phylogeny Forests Reconstruction

Today, a simple search for an image on the Web can return thousands of related images. Some results are exact copies, some are variants (or near-duplicates) of the same digital image, and others are unrelated. Although we can recognize some of these images as being semantically similar, it is not as straightforward to find which image is the original. It is not easy either to find the chain of transformations used to create each modified version. There are several approaches in the literature to identify near-duplicate images, as well as to reconstruct their relational structure. For the latter, a common representation uses the parent-child relationship, allowing us to visualize the evolution of modifications as a phylogeny tree. However, most of the approaches are restricted to the case of finding the tree of evolution of the near-duplicates, with few works dealing with sets of trees. Since one set of near-duplicates can contain n independent subsets, it is necessary to reconstruct not only one phylogeny tree, but several trees that will compose a phylogeny forest. In this paper, through the analysis of the state-of-the-art image phylogeny algorithms, we introduce a novel approach to deal with phylogeny forests, based on different combinations of these algorithms, aiming at improving their reconstruction accuracy. We analyze the effectiveness of each combination and evaluate our method with more than 40 000 testing cases, using quantitative metrics.

Multiple parenting identification in image phylogeny

Image phylogeny deals with tracing back parent-child relationships among near duplicates, images that share the same semantic content. This approach results in a visual structure showing the inheritance of semantic content among images, called phylogeny tree. In this paper, we extend upon the image phylogenys original formulation, which considers that an image may inherit content from only a single parent, to deal with situations whereby an image may inherit it from multiple different parents. Our objective is to find the multiple parenting relationships in a set of images, a problem which we refer to as multiple parenting phylogeny. The proposed solution works by first identifying near-duplicate groups and reconstructing their phylogenies; then among the found groups we determine the one(s) representing the composition images; finally, we detect the parenting relations between those compositions and the source images used to create them.

Spin filtering and disorder-induced magnetoresistance in carbon nanotubes:Ab initiocalculations

Nitrogen-doped carbon nanotubes can provide reactive sites on the porphyrin-like defects. Its well known that many porphyrins have transition metal atoms, and we have explored transition metal atoms bonded to those porphyrin-like defects in N-doped carbon nanotubes. The electronic structure and transport are analyzed by means of a combination of density functional theory and recursive Greens functions methods. The results determined the Heme B-like defect (an iron atom bonded to four nitrogens) as the most stable and with a higher polarization current for a single defect. With randomly positioned Heme B-defects in a few hundred nanometers long nanotubes the polarization reaches near 100% meaning an effective spin filter. A disorder induced magnetoresistance effect is also observed in those long nanotubes, values as high as 20000% are calculated with non-magnectic eletrodes.

Toward an Automated Identification of Anastrepha Fruit Flies in the fraterculus group (Diptera, Tephritidae)

In this study, we assess image analysis techniques as automatic identifiers of three Anastrepha species of quarantine importance, Anastrepha fraterculus (Wiedemann), Anastrepha obliqua (Macquart), and Anastrepha sororcula Zucchi, based on wing and aculeus images. The right wing and aculeus of 100 individuals of each species were mounted on microscope slides, and images were captured with a stereomicroscope and light microscope. For wing image analysis, we used the color descriptor Local Color Histogram; for aculei, we used the contour descriptor Edge Orientation Autocorrelogram. A Support Vector Machine classifier was used in the final stage of wing and aculeus classification. Very accurate species identifications were obtained based on wing and aculeus images, with average accuracies of 94 and 95%, respectively. These results are comparable to previous identification results based on morphometric techniques and to the results achieved by experienced entomologists. Wing and aculeus images produced equally accurate classifications, greatly facilitating the identification of these species. The proposed technique is therefore a promising option for separating these three closely related species in the fraterculus group.