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Predictive models for multibiometric systems
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations
A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables
It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications
SOME TRIGONOMETRIC SIMILARITY MEASURES OF COMPLEX FUZZY SETS WITH APPLICATION
Similarity measures of fuzzy sets are applied to compare the closeness among fuzzy sets. These measures have numerous applications in pattern recognition, image processing, texture synthesis, medical diagnosis, etc. However, in many cases of pattern recognition, digital image processing, signal processing, and so forth, the similarity measures of the fuzzy sets are not appropriate due to the presence of dual information of an object, such as amplitude term and phase term. In these cases, similarity measures of complex fuzzy sets are the most suitable for measuring proximity between objects with two-dimensional information. In the present paper, we propose some trigonometric similarity measures of the complex fuzzy sets involving similarity measures based on the sine, tangent, cosine, and cotangent functions. Furthermore, in many situations in real life, the weight of an attribute plays an important role in making the right decisions using similarity measures. So in this paper, we also consider the weighted trigonometric similarity measures of the complex fuzzy sets, namely, the weighted similarity measures based on the sine, tangent, cosine, and cotangent functions. Some properties of the similarity measures and the weighted similarity measures are discussed. We also apply our proposed methods to the pattern recognition problem and compare them with existing methods to show the validity and effectiveness of our proposed methods
Logic Based Pattern Recognition - Ontology Content (2)
Logic based Pattern Recognition extends the well known similarity models, where the distance
measure is the base instrument for recognition. Initial part (1) of current publication in iTECH-06 reduces the logic
based recognition models to the reduced disjunctive normal forms of partially defined Boolean functions. This
step appears as a way to alternative pattern recognition instruments through combining metric and logic
hypotheses and features, leading to studies of logic forms, hypotheses, hierarchies of hypotheses and effective
algorithmic solutions. Current part (2) provides probabilistic conclusions on effective recognition by logic means in
a model environment of binary attributes
Query Enrichment for Image Collections by Reuse of Classification Rules
User queries over image collections, based on semantic similarity,
can be processed in several ways. In this paper, we propose to reuse the rules
produced by rule-based classifiers in their recognition models as query pattern
definitions for searching image collections
Phenotypic matching by spot pattern potentially mediates female giraffe social associations
Animal color pattern is a phenotypic trait that may mediate assortative mixing (also known as homophily), whereby similar looking individuals have stronger social associations. Masai giraffe (Giraffa camelopardalis tippelskirchi) coat spot patterns show high variation and some spot traits appear to be heritable. Giraffes also have high visual acuity, which may facilitate intraspecific communication and recognition based on spot patterns. Giraffe groupings are dynamic, merging and splitting throughout the day, but females form long-term associations. We predicted that adult female giraffes show stronger associations with other females that have similar spot pattern traits. We quantified the spot pattern characteristics of 399 adult female Masai giraffes and determined the pattern similarity among pairs (dyads) in their social network. We then tested for an association between coat pattern similarity (spot size, shape, and orientation) and dyadic association strength, and quantified assortative mixing. The strength of social associations was positively correlated with similarity in spot shape. Our results are compatible with assortativity by coat patterns that are similar between mother and offspring, potentially reflecting an effect of relatedness on both pattern similarity and female social associations. These results offer evidence that spot pattern could function as a visual cue for intraspecific communication and kin or individual recognition in a fission-fusion species
Advancing the accuracy of protein fold recognition by utilizing profiles from hidden Markov models
Protein fold recognition is an important step towards
solving protein function and tertiary structure prediction problems. Among a wide range of approaches proposed to solve this problem, pattern recognition based techniques have achieved the best results. The most effective pattern recognition-based techniques for solving this problem have been based on extracting evolutionary-based features. Most studies have relied on thePosition Specific Scoring Matrix (PSSM) to extract these features. However it is known that profile-profile sequence alignment techniques can identify more remote
homologs than sequence-profile approaches like PSIBLAST. In this study we use a profile-profile sequence alignment technique, namely HHblits, to extract HMM profiles.We will show that unlike previous studies, using the HMM profile to extract evolutionary information can significantly enhance the protein fold prediction accuracy. We develop a new pattern recognition based system called HMMFold which extracts HMM based evolutionary information and captures remote homology information better than previous studies. Using
HMMFold we achieve up to 93.8% and 86.0% prediction accuracies when the sequential similarity rates are less than 40% and 25%, respectively. These results are up to 10% better than previously reported results for this task. Our results show significant enhancement especially for benchmarks with sequential similarity as low as 25% which highlights the effectiveness of HMMFold to address
this problem and its superiority over previously proposed approaches found in the literature
Human gait recognition: viewing angle effect on normal walking pattern
Gait recognition has recently gained interest of researchers as it performs identification of subjects at a distance from the camera. However, due to the changes in the viewing angles, it gets cumbersome for a system to perform recognition based on the walking pattern of an individual. In this work, the aim is to propose a simple baseline method for the purpose of human recognition based on the shape of its body and walking pattern when the subject is observed from different viewing angles. The recognition is also tested on the subjects in a scenario where the individual subjects are registered while walking in normal walking pattern followed by the testing in normal walking mode, apart from being observed at different viewing angles. Gait periodicity is estimated after extracting the silhouettes of an individual, followed by obtaining the total silhouette representation of an individual using Matlab. The total silhouette representations obtained from the probe gait data are compared to the gallery gait data representations for the purpose of similarity computation by calculating the RMS value between the said representations. Higher the value, lesser is the similarity & vice versa
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