The problem of recognizing handwritten mathematics notation has been studied for over forty years with little practical success. The poor performance of math recognition systems is due, at least in part, to a lack of realistic data for use in training recognition systems and evaluating their accuracy. In fields for which such data is available, such as face and voice recognition, the data, along with objectively-evaluated recognition contests, has contributed to the rapid advancement of the state of the art. This thesis proposes a method for constructing data corpora not only for hand- written math recognition, but for sketch recognition in general. The method consists of automatically generating template expressions, transcribing these expressions by hand, and automatically labelling them with ground-truth. This approach is motivated by practical considerations and is shown to be more extensible and objective than other potential methods. We introduce a grammar-based approach for the template generation task. In this approach, random derivations in a context-free grammar are controlled so as to generate math expressions for transcription. The generation process may be controlled in terms of expression size and distribution over mathematical semantics. Finally, we present a novel ground-truthing method based on matching terminal symbols in grammar derivations to recognized symbols. The matching is produced by a best-first search through symbol recognition results. Experiments show that this method is highly accurate but rejects many of its inputs.

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Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

As the demand for higher and more sophisticated security solutions has dramatically increased, a trustworthy and a more intelligent authentication technology has to takeover. That is biometric authentication. Although biometrics provides promising solutions, it is still a pattern recognition and artificial intelligence grand challenge. More importantly, biometric data in itself are vulnerable and requires comprehensive protection that ensures their security at every stage of the authentication procedure including the processing stage. Without this protection biometric authentication cannot replace traditional authentication methods. This protection however cannot be accomplished using conventional cryptographic methods due to the nature of biometric data, its usage and inherited dynamical changes. The new protection method has to transform the biometric data into a secure domain where original information cannot be reversed or retrieved. This secure domain has also to be suitable for accurate authentication performance. In addition, due to the permanence characteristic of the biometric data and the limited number of valid biometrics for each individual, the transform has to be able to generate multiple versions of the same original biometric trait. This to facilitate the replacement and the cancellation of any compromised transformed template with a newer one without compromising the security of the system. Hence the name of the transform that is best known as cancellable biometric. Two cancellable face biometric transforms have been designed, implemented and analysed in this thesis, the Polynomial and Co-occurrence Mapping (PCoM) and the Randomised Radon Signatures (RRS). The PCoM transform is based on high-order polynomial function mappings and co-occurrence matrices derived from the face images. The secure template is formed by the Hadamard product of the generated metrics. A mathematical framework of the two-dimensional Principal Component Analysis (2DPCA) recognition is established for accuracy performance evaluation and analysis. The RRS transform is based on the Radon Transform (RT) and the random projection. The Radon Signature is generated from the parametric Radon domain of the face and mixed with the random projection of the original face image. The transform relies on the extracted signatures and the Johnson-Lindenstrauss lemma for high accuracy performance. The Fisher Discriminant Analysis (FDA) is used for evaluating the accuracy performance of the transformed templates. Each of the transforms has its own security analysis besides a comprehensive security analysis for both. This comprehensive analysis is based on a conventional measure for the Exhaustive Search Attack (ESA) and a new derived measure based on the lower-bound guessing entropy for Smart Statistical Attack (SSA). This entropy measure is shown to be greater than the Shannon lower-bound of the guessing entropy for the transformed templates. This shows that the transforms provide greater security while the ESA analysis demonstrates immunity against brute force attacks. In terms of authentication performance, both transforms have either maintained or improved the accuracy of authentication. The PCoM has maintained the recognition rates for the CMU Advance Multimedia Processing Lab (AMP) and the CMU Pose, Illumination & Expression (PIE) databases at 98.35% and 90.13% respectively while improving the rate for the Olivetti Research Ltd (ORL) database to 97%. The transform has achieved a maximum recognition performance improvement of 4%. Meanwhile, the RRS transform has obtained an outstanding performance by achieving zero error rates for the ORL and PIE databases while improving the rate for the AMP by 37.50%. In addition, the transform has significantly enhanced the genuine and impostor distributions separations by 263.73%, 24.94% and 256.83% for the ORL, AMP and PIE databases while the overlap of these distributions have been completely eliminated for the ORL and PIE databases.

published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

Emotions play an important role for everyday communication. Different methods allow computers to recognize emotions. Most are trained with acted emotions and it is unknown if such a model would work for recognizing naturally appearing emotions. An experiment was setup to estimate the recognition accuracy of the emotion recognition software SHORE, which could detect the emotions angry, happy, sad, and surprised. Subjects played a casino game while being recorded. The software recognition was correlated with the recognition of ten human observers. The results showed a strong recognition for happy, medium recognition for surprised, and a weak recognition for sad and angry faces. In addition, questionnaires containing self-informed emotions were compared with the computer recognition, but only weak correlations were found. SHORE was able to recognize emotions almost as well as humans were, but if humans had problems to recognize an emotion, then the accuracy of the software was much lower.

Correlation filters (CFs) are well established and useful tools for a variety of tasks in signal processing and pattern recognition, including automatic target recognition and tracking, biometrics, landmark detection, and human action recognition. Traditionally, CFs have been designed and implemented efficiently in the frequency domain using the discrete Fourier transform (DFT). However, the element-wise multiplication of two DFTs in the frequency domain corresponds to a circular correlation, which results in aliasing (i.e., distortion) in the correlation output. Prior CF research has largely ignored these aliasing effects by making the assumption that linear correlation is approximated by circular correlation. In this work, we investigate in detail the topic of aliasing in CFs. First, we illustrate that the current formulation of CFs in the frequency domain is inherently flawed, as it unintentionally assumes circular correlation during the design phase. This means that existing CFs are not truly optimal. We introduce zero-aliasing correlation filters (ZACFs) which fix this formulation issue by ensuring that each CF formulation problem corresponds to a linear correlation rather than a circular correlation. By adopting the ZACF design modifications, we show that the recognition and localization performance of conventional CF designs can be significantly improved. We demonstrate these benefits using a variety of data sets and present solutions to the computational challenges associated with computing ZACFs. After a CF is designed, it is used for object recognition by correlating it with a test signal. We investigate the use of the well-known overlap-add (OLA) and overlap-save (OLS) algorithms to improve the computation and memory requirements of this correlation operation for high dimensional applications (e.g., video). Through this process, we highlight important tradeoffs between these two algorithms that have previously been undocumented. To improve the computation and memory requirements of OLA and OLS, we introduce a new block filtering scheme, denoted partial-aliasing OLA (PAOLA) that intentionally introduces aliasing into the output correlation. This aliasing causes conventional CFs to perform poorly. To remedy this, we introduce partial-aliasing correlation filters (PACFs), which are specifically designed to minimize this aliasing. We demonstrate through numerical results that PACFs outperform conventional CFs in the presence of aliasing.

The mechanism of T cell recognition is the central but unsolved puzzle of adaptive immunology. The difficulties come from the multichain structure of TCR/CD3, the binate binding structure of the pMHC molecule, the diversity of the peptides presented on the APC, the critical role of coreceptor CD4/8, the communication between TCR and coreceptor CD4/8, the complex environment of interactions taking place and the binding and signaling coupled process of recognition. Most studies were using the 3D kinetic measurements or biological functional assays to address the mechanism of the T cell recognition. However, those assays are usually either lacking of physiology relevance or missing of the initial recognition signals. Here a 2D micropipette adhesion assay with high temporal resolution (-second) was used to address the in situ kinetics of molecular interaction at the membrane of live T cells. The aim of this project is to advance our understanding to the T cell recognition mechanism. The micropipette adhesion assay was firstly used to address a simple case, the resting state pMHC-CD8 interaction. In the absence of TCR-pMHC interaction, the pMHC-CD8 interaction has a very low affinity that depends on the MHC alleles and the lipid rafts of the T cell membrane where CD8 resides, but not on the peptide complexed to the MHC and whether the CD8 is an a a homodimer or an αβ heterodimer. For cognate pMHC, following the initial observation in the F5 T cell system, the binding also displays a two-step curve in the OTI T cell system. The first-step binding occurs before one second and has a very fast on-rate and off-rate (>2s ⁻¹), and the secondstep binding follows immediately but reaches a much higher level of binding. It was identified that the first-step binding is mediated by the TCR-pMHC interaction, and the second-step binding is triggered by the TCR-pMHC interaction but mediated by CD8- pMHC binding. The two-step binding is the unique property of cognate pMHC, and it can be abolished by disrupting the lipid rafts, inhibiting the Src family protein tyrosine kinases (PTK) or protein tyrosine phosphatase (PTP). The finding of two-step binding identifies a CD8-dependent signaling amplification pathway. The data also indicated the active communication between TCR and CD8 in the antigen recognition. The crosstalk between TCR and CD8 was further dissected using two anti-CD8 antibodies 53.6.7 and CT-CD8a. 53-6.7 can significantly enhance the binding of pMHC to the T cell. Although the enhancement is directly mediated by MHC-CD8 interaction, the enhancing role of this antibody is TCR dependent. Blocking the TCR-pMHC interaction on OTI T cell or expressing CD8 alone on a hybridoma abolished the enhancement. The enhancement is also dependent on the integrity of lipid rafts and the normal function of PTP. In contrast, the antibody CT-CD8 can inhibit the binding of pMHC to the T cells and interfere with the TCR-pMHC interaction. The enhancing or inhibitory role of these two anti-CD8 antibodies is reversely correlated with the affinities of TCR-pMHC interactions. Only 53-6.7, but not CT-CD8 antibody, can phosphorylate and activate Lck. The data demonstrated a dual way crosstalk between TCR and CD8, and indicated the importance of cooperation of TCR and CD8 in antigen recognition. In the physiology condition, the TCR must accurately and efficiently recognize the cognate peptide from thousands of surrounding endogenous peptides. There is an argument regarding whether the endogenous peptides plays a role in helping the TCR recognition. Our results demonstrated that the nonstimulatory peptides can significantly enhance the T cell recognition sensitivity. In the presence of nonstimulatory peptide, the TCR can efficiently detect a single antigenic pMHC. The enhancement of recognition is due to the CD8 binding to the nonstimulatory pMHC. Blocking the CD8 binding can paralyze the enhancement. In contrast, it was found that the presence of antagonist can inhibit the binding of agonist pMHC to the T cells, and the inhibition occurs in the initial recognition step. Based on the data, an "amplification and competition" model was proposed to explain the molecular mechanism of the enhancement and inhibition function of the nonstimulatory and antagonist peptides in the T cell recognition, respectively.

Sketch recognition is an active research field, with the goal to automatically recognize hand-drawn diagrams by a computer. The technology enables people to freely interact with digital devices like tablet PCs, Wacoms, and multi-touch screens. These devices are easy to use and have become very popular in market. However, they are still quite costly and need more time to be integrated into existing systems. For example, handwriting recognition systems, while gaining in accuracy and capability, still must rely on users using tablet-PCs to sketch on. As computers get smaller, and smart-phones become more common, our vision is to allow people to sketch using normal pencil and paper and to provide a simple microphone, such as one from their smart-phone, to interpret their writings. Since the only device we need is a single simple microphone, the scope of our work is not limited to common mobile devices, but also can be integrated into many other small devices, such as a ring. In this thesis, we thoroughly investigate this new area, which we call acoustic based sketch recognition, and evaluate the possibilities of using it as a new interaction technique. We focus specifically on building a recognition engine for acoustic sketch recognition. We first propose a dynamic time wrapping algorithm for recognizing isolated sketch sounds using MFCC(Mel-Frequency Cesptral Coefficients). After analyzing its performance limitations, we propose improved dynamic time wrapping algorithms which work on a hybrid basis, using both MFCC and four global features including skewness, kurtosis, curviness and peak location. The proposed approaches provide both robustness and decreased computational cost. Finally, we evaluate our algorithms using acoustic data collected by the participants using a device's built-in microphone. Using our improved algorithm we were able to achieve an accuracy of 90% for a 10 digit gesture set, 87% accuracy for the 26 English characters and over 95% accuracy for a set of seven commonly used gestures.

Doctor of Philosophy(PhD) / Communication is a fundamental process that allows animals to effectively transfer information between groups or individuals. Recognition plays an essential role in permitting animals to distinguish individuals based upon both communicatory and non-communicatory signals allowing animals to direct suitable behaviours towards them. Several modes of recognition exist and in colonial breeding animals which congregate in large numbers, acoustic signalling is thought to be the most effective as it suffers less from environmental degradation. Otariid seals (fur seals and sea lions) are generally colonial breeding species which congregate at high densities on offshore islands. In contrast to the other Arctocephaline species, the Australian fur seal, Arctocephalus pusillus doriferus, along with its conspecific, the Cape fur seal, A. p. pusillus, display many of the behavioural traits of sea lions. This may have important consequences in terms of its social structure and evolution. The acoustic communication of Australian fur seals was studied on Kanowna Island, Bass Strait, Australia. Analysing the acoustic structure of vocalisations and their use facilitates our understanding of the social function of calls in animal communication. The vocal repertoires of males, females, pups and yearlings were characterised and their behavioural context examined. Call structural variations in males were evident with changes in behavioural context, indicating parallel changes in the emotive state of sender. For a call to be used in vocal recognition it must display stereotypy within callers and variation between them. In Australian fur seal females and pups, individuals were found to have unique calls. Mutual mother-pup recognition has been suggested for otariids and this study supports the potential for this process to occur through the use of vocalisations. Call structural changes in pup vocalisations were also investigated over the progression of the year, from birth to weaning. Vocalisations produced by pups increased in duration, lowered in both the number of parts per call and the harmonic band containing the maximum frequency as they became older, suggesting calls are changing constantly as pups grow toward maturity. It has been suggested through descriptive reports, that the bark call produced by males is important to vocal recognition. The present study quantified this through the analysis of vocalisations produced by male Australian fur seals. Results support descriptive evidence suggesting that male barks can be used to discriminate callers. Traditional playback studies further confirmed that territorial male Australian fur seals respond significantly more to the calls of strangers than to those of neighbours, supporting male vocal recognition. This study modified call features of the bark to determine the importance to vocal recognition. The results indicate that the whole frequency spectrum was important to recognition. There was also an increase in response from males when they heard more bark units, indicating the importance of repetition by a caller. Recognition occurred when males heard between 25-75% of each bark unit, indicating that the whole duration of each bark unit is not necessary for recognition to occur. This may have particular advantages for communication in acoustically complex breeding environments, where parts of calls may be degraded by the environment. The present study examined the life history characteristics of otariids to determine the factors likely to influence and shape its vocal behaviour. Preliminary results indicate that female density, body size and the breeding environment all influence the vocal behaviour of otariids, while duration of lactation and the degree of polygyny do not appear to be influential. Understanding these interactions may help elucidate how vocal recognition and communication have evolved in different pinniped species.