Automatic absolute orientation of scanned aerial photographs

Image orientation is a basic problem in Digital Photogrammetry. While interior and relative orientations were succesfully automated, the same can not be said about absolute orientation. This process can be automated by using an approach based on relational matching and a heuristic that uses the analytical relation between straight features in the object space and its homologous in the image space. A build-in self-diagnosis is also used in this method, that is based on the implementation of data snooping statistic test in the process of spatial resection, using the Iterated Extended Kalman Filtering (IEKF). The aim of this paper is to present the basic principles of the proposed approach and results based on real data.

CENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION and MATCHING APPROACH FOR FACE RECOGNITION

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.

A recursive approach to space resection using straight lines

An approach using straight lines as features to solve the photogrammetric space resection problem is presented. An explicit mathematical model relating straight lines, in both object and image space, is used. Based on this model, Kalman Filtering is applied to solve the space resection problem. The recursive property of the filter is used in an iterative process which uses the sequentially estimated camera location parameters to feedback to the feature extraction process in the image. This feedback process leads to a gradual reduction of the image space for feature searching, and consequently eliminates the bottleneck due to the high computational cost of the image segmentation phase. It also enables feature extraction and the determination of feature correspondence in image and object space in an automatic way, i.e., without operator interference. Results obtained from simulated and real data show that highly accurate space resection parameters are obtained as well as a progressive processing time reduction. The obtained accuracy, the automatic correspondence process, and the short related processing time show that the proposed approach can be used in many real-time machine vision systems, making possible the implementation of applications not feasible until now.

Exterior orientation of CBERS-2B imagery using multi-feature control and orbital data

The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China-Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced. © 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Estudo fotoelástico do controle vertical com o arco de dupla chave na técnica Straight wire

INTRODUÇÃO: em alguns casos, a extração de pré-molares torna-se necessária e nem sempre os espaços são completamente fechados após o alinhamento e nivelamento. O arco de dupla chave, ou Double Key Loop (DKL), é um arco retangular de aço para retração, com duas alças - uma mesial e outra distal ao canino. OBJETIVOS: este trabalho propôs-se a estudar o local onde a força é exercida, após a ativação desse arco, utilizando ativação na alça distal, ativação entre as alças e na alça distal, e ativação com Gurin®. MÉTODOS: foram montados nove modelos fotoelásticos de um arco dentário inferior, sem os primeiros pré-molares e os terceiros molares, com braquetes In-Ovation e arco DKL. O arco foi ativado e a região de incisivos, caninos e dentes posteriores foi fotografada, com interposição de filtros polarizadores de luz. RESULTADOS E CONCLUSÕES: após a análise do modelo fotoelástico, concluiu-se que a ativação com Gurin® pode produzir movimento de retração anterior com componente extrusivo; a ativação na alça distal pode produzir movimento de retração anterior sem componente extrusivo; e a ativação entre as alças e na alça distal pode produzir movimento de retração anterior com componente intrusivo.

Feature selection environment for genomic applications

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e. g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. Results: The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes ( targets or predictors) is also implemented in the system. Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

Spatial-temporal variation in coiled and straight morphotypes of Cylindrospermopsis raciborskii (Wolsz) Seenayya et Subba Raju (Cyanobacteria)

(Spatial-temporal variation in coiled and straight morphotypes of Cylindrospermopsis raciborskii (Wolsz) Seenayya et Subba Raju (Cyanobacteria)). This study reports the spatial and temporal behavior of straight and coiled morphotypes of C. raciborskii in a reservoir in Brazil`s semiarid region as well as the main factors responsible for the variability. Two set of samples were collected from the subsurface and bottom in the central region of a reservoir in two seasonal periods (dry January 2005; rainy June 2005) over 20-hour sampling periods during daylight (8 am, 12 pm and 4 pm) and dark (8 pm, 12 am and 4 am) hours. Measurements of abiotic parameters were determined concurrently to the sampling of biotic variables. Two C. raciborskii morphotypes were found in the reservoir: straight and coiled. There was no difference in density of the straight and coiled C. raciborskii morphotypes between the different sampling times for either season. Vertical differences were found in the distribution of both morphotypes in both seasons, with greater densities recorded at the subsurface. Densities of the two C. raciborskii morphotypes were greater in the dry season, with the density of the coiled morphotype at the surface two-fold greater than that of the straight morphotype and that found in the rainy season. The ecological success of the coiled morphotype was due to thermal stratification, whereas a mixed condition was determinant in the success of the straight morphotype.

Voice Feature Characteristic in Morbid Obese Population

Morbid obesity is highly prevalent in the Western World, and its consequences present a real public health challenge. Voice alterations can represent one of these consequences and represent an opportunity for interference with therapeutic methods. This particularly features of the individual`s voice was the goal of the present study. A group of 45 adult volunteers of both sexes with a BMI greater than 35 Kg/m(2) was selected among patients of the Obesity Ambulatory of the Digestive Surgery Division. The control group consisted of volunteers matched by sex, age (+/- 1 year), and smoking habits, but with a BMI bellow 30 Kg/m(2). All subjects were submitted to laryngoscopic examination, audio perceptive analysis, and voice acoustics determination. Examinations were always performed by the same doctor, and diagnoses were provided by two different physician specialists in laryngology and voice. Obese individuals exhibit the following modifications in voice feature: hoarseness, murmuring, vocal instability, altered jitter and shimmer, and reduced maximum phonation times as well the presence of voice strangulation at the end of emission. The voices of individuals with morbid obesity are different of the voice of nonobese people and demonstrate significant changes in vocal characteristics.

Claudin-7 down-regulation is an important feature in oral squamous cell carcinoma

Aims: Claudins, a large family of essential tight junction (TJ) proteins, are abnormally regulated in human carcinomas, especially claudin-7. The aim of this study was to investigate claudin-7 expression and alterations in oral squamous cell carcinoma (OSCC). Methods and results: Expression of claudin-7 was analysed in 132 cases of OSCC organized in a tissue microarray. Claudin-7 mRNA transcript was evaluated using real-time polymerase chain reaction and the methylation status of the promoter was also assessed. Claudin-7 was negative in 58.3% of the cases. Loss of claudin-7 protein expression was associated with recurrence (P = 0.019), tumour size (P = 0.014), clinical stage of OSCC (P = 0.055) and disease-free survival (P = 0.015). Down-regulation of the claudin-7 mRNA transcripts was observed in 78% of the cases, in accordance with immunoexpression. Analysis of the methylation status of the promoter region of claudin-7 revealed that treatment of O28 cells (that did not express claudin-7 mRNA transcripts) with 5-Aza-2`-Deoxycytidine (5-Aza-dC) led to the re-expression of claudin-7 mRNA transcript. Conclusion: Loss of claudin-7 expression is associated with important subcellular processes in OSCC with impact on clinical parameters.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention

Short-term Feature Space and Music Genre Classification

In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.