Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.

Juvenile localized scleroderma: Clinical and epidemiological features in 750 children. An international study

Objective. Juvenile localized scleroderma (JLS) includes a number of conditions often grouped together. With the long-term goal of developing uniform classification criteria, we studied the epidemiological, clinical and immunological features of children with JLS followed by paediatric rheumatology and dermatology centres. Methods. A large, multicentre, multinational study was conducted by collecting information on the demographics, family history, triggering environmental factors, clinical and laboratory features, and treatment of patients with JLS. Results. Seven hundred and fifty patients with JLS from 70 centres were enrolled into the study. The disease duration at diagnosis was 18 months. Linear scleroderma (LS) was the most frequent subtype (65%), followed by plaque morphea (PM) (26%), generalized morphea (GM) (7%) and deep morphea (DM) (2%). As many as 15% of patients had a mixed subtype. Ninety-one patients (12%) had a positive family history for rheumatic or autoimmune diseases; 100 (13.3%) reported environmental events as possible trigger. ANA was positive in 42.3% of the patients, with a higher prevalence in the LS-DM subtype than in the PM-GM subtype. Scl70 was detected in the sera of 3% of the patients, anticentromere antibody in 2%, anti-double-stranded DNA in 4%, anti-cardiolipin antibody in 13% and rheumatoid factor in 16%. Methotrexate was the drug most frequently used, especially during the last 5 yr. Conclusion. This study represents the largest collection of patients with JLS ever reported. The insidious onset of the disease, the delay in diagnosis, the recognition of mixed subtype and the better definition of the other subtypes should influence our efforts in educating trainees and practitioners and help in developing a comprehensive classification system for this syndrome. © 2006 Oxford University Press.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Microcalcification enhancement and classification on mammograms using the wavelet transform

This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27% using a region growing algorithm. © 2008 IEEE.

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes. © 2009 SPIE.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Portable Hardness tester for timber classification

Hardness is a property largely used in material specifications, mechanical and metallurgical research and quality control of several materials. Specifically for timber, Janka hardness is a simple, quick and easy test, with good correlations with the compression parallel to grain strength, a strong reference in structural classification for this material. More recently, international studies have reported the use of Brinell hardness for timber assessment which resumes the advantages previously mentioned for Janka hardness and make it easier to be performed in the field, especially because of the lower magnitude of the involved loads. A first generation of an equipment for field evaluation of hardness in wood - Portable Hardness tester for wood - based on Brinell hardness has already been developed by the Research Group on Forest Products from FCA/UNESP, Brazil, with very good correlations between the evaluated hardness and several other mechanical properties of the material when performing tests with different species of native and reforested wood (traditionally used as ties - sleepers - in railways). This paper presents results obtained in the experimental program with the first generation of this equipment and preliminary tests with its second generation, which uses accelerometers to substitute the indentation measurements in wood. For the first generation of the equipment functional and calibration tests were carried out using 16 native and reforestation timber lots, among there E. citriodora, E. tereticornis, E. saligna, E. urophylla, E. grandis, Goupia glabra and Bagassa guianenses, with different origins and ages. The results obtained confirm its potential in the classification of specimens, with inclusion errors varying from 4.5% to 16.6%.

Land use image classification through optimum-path forest clustering

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.

Is it possible to make pixel-based radar image classification user-friendly?

In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise pattern recognition algorithms in order to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed. © 2011 IEEE.

New trends in musical genre classification using optimum-path forest

Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.

Automatic classification of fish germ cells through optimum-path forest

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques. © 2011 IEEE.

Classification of crucifer cultivars based on the life-history of diamondback moth (Plutella xylostella)

We compared different cultivars and hybrids of crucifers in relation to development and life-history of diamondback moth (Plutella xylostella) to classify the plants according to their resistance to the pest. The plants used were Manteiga da Geórgia kale, Bola de Neve cauliflower, Ramoso Piracicaba Precoce broccoli, Chato-de-quintal cabbage, and the hybrid cabbages Midori, TPC668, TPC308, and TPC681. We evaluated performance daily until the pupal stage. Pupae were assessed individually to determine the pupal weight, performance, and pupal period. We determined the sex ratio, fecundity, fertility, and longevity of the emerged adults and calculated their reproductive potential. Cabbage hybrids TPC668, TPC308, and TPC681 do not support the development and reproduction of the diamondback moth. These hybrids show a level of resistance that is similar to that found the commercially available hybrid Midori and cultivar Chato de Quintal, which are known to be resistant to the diamondback moth. This finding implies that the capitata (cabbage) cultivars are the most suitable for planting because they are more resistant to pest than the cultivar's moth, acephala (kale). © 2013 Copyright Taylor and Francis Group, LLC.

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

Cellulite: poor correlation between instrumental methods and photograph evaluation for severity classification

SynopsisBackgroundCellulite refers to skin relief alterations in womens thighs and buttocks, causing dissatisfaction and search for treatment. Its physiopathology is complex and not completely understood. Many therapeutic options have been reported with no scientific evidence about benefits. The majority of the studies are not controlled nor randomized; most efficacy endpoints are subjective, like not well-standardized photographs and investigator opinion. Objective measures could improve severity assessment. Our purpose was to correlate non-invasive instrumental measures and standardized clinical evaluation.MethodsTwenty six women presenting cellulite on buttocks, aged from 25 to 41, were evaluated by: body mass index; standardized photography analysis (10-point severity and 5-point photonumeric scales) by five dermatologists; cutometry and high-frequency ultrasonography (dermal density and dermis/hypodermis interface length). Quality of life impact was assessed. Correlations between clinical and instrumental parameters were performed.ResultsGood agreement among dermatologists and main investigator perceptions was detected. Positive correlations: body mass index and clinical scores; ultrasonographic measures. Negative correlation: cutometry and clinical scores. Quality of life score was correlated to dermal collagen density.ConclusionCellulite caused impact in quality of life. Poor correlation between objective measures and clinical evaluation was detected. Cellulite severity assessment is a challenge, and objective parameters should be optimized for clinical trials.