One of the key challenges in face perception lies in determining the contribution of different cues to face identification. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with grayscale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

As the United States is on the cusp of returning astronauts to the Moon, it becomes increasingly apparent that the assembly of structures in space will have to rely upon robots to perform the construction process. With a focus on sustaining a presence on the Moon's surface in such a harsh and unforgiving environment, demonstrating the robustness of autonomous assembly and capabilities of robotic manipulators is necessary. Current robotic assembly on Earth consists mainly of inspection or highly controlled environments, and always with a human in the loop to step in and fix issues if a problem occurs. To remove the human element, the robot system then must account for safety as well. Thus, system risk can easily overwhelm project costs. This thesis proposes a combination of hardware and state estimation solutions to improve the feasibility of low-fidelity and low-resolution robots for precision assembly tasks. Doing so reduces the risk to mission success, as the hardware becomes easier to replace or repair. The hardware modifications implement an electro-permanent magnet interface with alignment features to reduce the fidelity needed for the robot end effector. On the state estimation side, an Ensemble Kalman Filter is implemented, along with a scaling system to prevent FASER Lab hardware from becoming stuck due to hardware limitations. Overall, the three modifications improved the test robot's autonomous convergence error by 98.5%, bettering the system sufficiently to make an autonomous assembly process feasible. / Master of Science / With the dawn of new space age nearly upon us, one of the most important aspects to working in space will be robotic assembly, whether on the surface of other planetary bodies like the Moon or in zero-gravity, in order to keep astronauts safe and to reduce spaceship launch costs. Both places have their own difficult problems to deal with, and doing any actions in those locations come with a significant amount of risk involved. To reduce extreme risk, you can spend more money to over-protect the robots, or reduce the consequences of the risk. This thesis describes a way to reduce the impact of risks to a mission by checking whether inexpensive robots can be adapted and modified to be able to perform similar construction actions to a much more expensive robot. It does this by using specialized hardware and software programs to better align the robot to where it needs to go without people needing to step in and help it. The experiments showed a 98.5% improvement to the system from without any of the modifications and validated that the low-cost robot could be improved sufficiently to be useful.

The central challenge in face recognition lies in understanding the role different facial features play in our judgments of identity. Notable in this regard are the relative contributions of the internal (eyes, nose and mouth) and external (hair and jaw-line) features. Past studies that have investigated this issue have typically used high-resolution images or good-quality line drawings as facial stimuli. The results obtained are therefore most relevant for understanding the identification of faces at close range. However, given that real-world viewing conditions are rarely optimal, it is also important to know how image degradations, such as loss of resolution caused by large viewing distances, influence our ability to use internal and external features. Here, we report experiments designed to address this issue. Our data characterize how the relative contributions of internal and external features change as a function of image resolution. While we replicated results of previous studies that have shown internal features of familiar faces to be more useful for recognition than external features at high resolution, we found that the two feature sets reverse in importance as resolution decreases. These results suggest that the visual system uses a highly non-linear cue-fusion strategy in combining internal and external features along the dimension of image resolution and that the configural cues that relate the two feature sets play an important role in judgments of facial identity.

It is often a challenge to atomically determine the structure of large macromolecular assemblies, even if successfully crystallized, due to their weak diffraction of X-rays. Refinement algorithms that work with low-resolution diffraction data are necessary for researchers to obtain a picture of the structure from limited experimental information. Relationship between the structure and function of proteins implies that a refinement approach delivering accurate structures could considerably facilitate further research on their function and other related applications such as drug design. Here a refinement algorithm called the Deformable Complex Network is presented. Computation results revealed that, significant improvement was observed over the conventional refinement and DEN refinement, across a wide range of test systems from the Protein Data Bank, indicated by multiple criteria, including the free R value, the Ramachandran Statistics, the GDT (<1Å) score, TM-score as well as associated electron density map.

Pattern recognition is very important in automatic industry. The automation machinery vision system must exchange information very fast with the object we need. So the machinery vision system must have powerful recognition ability. There are more important on image processing, lately. But most researches of image processing are developed on high-resolution image. However, in same situation, for increasing the processing speed, reducing the saving space. Low-resolution image are the only way to achieve the above condition up to now. For the purpose of quickly recognition, we construct the recognition system on low-resolution image. From observing the characteristic of hexagonal grid, we knew the hexagonal grid have greater angular resolution and better image performance than rectangular grid. Therefore, we apply the hexagonal grid on low-resolution image, and using Curve Bend Function (call CBF) on hexagonal gird system; for promoting the accuracy of recognition.We presented an technique of subpixel on low-resolution hexagonal image to obtain better results.

<p>X-ray crystallography 3D atomic models are used in a variety of research areas to understand and manipulate protein structure. Research and application are dependent on the quality of the models. Low-resolution experimental data is a common problem in crystallography which makes solving structures and producing the reliable models that many scientists depend on difficult.</p><p>In this work, I develop new, automated tools for validation and correction of low-resolution structures. These tools are gathered under the name CaBLAM, for C-alpha Based Low-resolution Annotation Method. CaBLAM uses a unique, C-alpha-geometry-based parameter space to identify outliers in protein backbone geometry, and to identify secondary structure that may be masked by modeling errors.</p><p>CaBLAM was developed in the Python programming language as part of the Phenix crystallography suite and the open CCTBX Project. It makes use of architecture and methods available in the CCTBX toolbox. Quality-filtered databases of high-resolution protein structures, especially the Top8000, were used to construct contours of expected protein behavior for CaBLAM. CaBLAM has also been integrated into the codebase for the Richardson Lab's online MolProbity validation service.</p><p>CaBLAM succeeds in providing useful validation feedback for protein structures in the 2.5-4.0A resolution range. This success demonstrates the relative reliability of the C-alpha; trace of a protein in this resolution range. Full mainchain information can be extrapolated from the C-alpha; trace, especially for regular secondary structure elements.</p><p>CaBLAM has also informed our approach to validation for low-resolution structures. Moderation of feedback, to reduce validation overload and to focus user attention on modeling errors that are both significant and correctable, is one of our goals. CaBLAM and the related methods that have grown around it demonstrate the progress towards this goal.</p> / Dissertation

In this thesis, we reexamine the classical problem of video super-resolution, with an aim to reproduce fine edge/texture details of acquired digital videos. In general, the video super-resolution reconstruction is an ill-posed inverse problem, because of an insufficient number of observations from registered low-resolution video frames. To stabilize the problem and make its solution more accurate, we develop two video super-resolution techniques: 1) a 2D autoregressive modeling and interpolation technique for video super-resolution reconstruction, with model parameters estimated from multiple registered low-resolution frames; 2) the use of image model as a regularization term to improve the performance of the traditional video super-resolution algorithm. We further investigate the interactions of various unknown variables involved in video super-resolution reconstruction, including motion parameters, high-resolution pixel intensities and the parameters of the image model used for regularization. We succeed in developing a joint estimation technique that infers these unknowns simultaneously to achieve statistical consistency among them. / Thesis / Master of Applied Science (MASc)

A espectroscopia de Ressonância Magnética Nuclear (RMN) em baixo campo tem demonstrado ser um método rápido e confiável para se avaliar a qualidade da carne. A predição desta, especialmente a carne de porco, tem sido avaliada usando o tempo de relaxação transversal (T2). As medidas foram feitas com a seqüência de pulsos CPMG (Carr-Purcell-Meibom-Gill) que correlaciona os parâmetros da qualidade da carne de suínos, tais como, a capacidade de retenção de água (CRA), o pH e perda de água por cocção (PPC), com tempo de relaxação T2. Neste trabalho, analisou-se a qualidade da carne bovina pela seqüência de pulsos CPMG, que é dependente do tempo relaxação transversal (T2), e pela técnica de precessão livre de onda continua (CWFP), que depende de T2 e da relaxação longitudinal (T1). Foram analisadas amostras do contra-filé do tipo músculo- longissimus lumborum, retiradas da região da 12ª e da 6ª costela de bovinos, de três grupos genéticos de bovinos; adaptados ou não às condições tropicais, sendo dois deles de 3/4 e um de 9/16 de sangue europeu. Os dados de CPMG foram analisados utilizando-se os processamentos de análise multiexponencial discreta, análise multiexponencial contínua, baseada na transformada inversa de Laplace (ILT) e com métodos quimiométricos como análises de componentes principais (PCA), análises de agrupamentos hierárquicos (HCA) e regressão por mínimos quadrados parciais (PLS). Os dados de CWFP por serem mais complexos do que os do CPMG foram analisados principalmente por métodos quimiométricos. Conclui-se que não há diferença significativa entre o corte realizado na 12ª costela com o da 6ª costela e que a técnica CWFP é mais eficaz na separação entre o sexo e grupo genético dos animais, do que a técnica CPMG / Low resolution Nuclear magnetic resonance spectroscopy (NMR) is shown to be a fast and accurate method for evaluating meat quality. The prediction of meat quality, especially in pork, has been performed using transverse relaxation time (T2). The measurements have been made with CPMG (Carr-Purcell-Meibom-Gill) pulse sequence which correlate the pork quality parameters such as water-holdcapacity (WHC), pH, and cooking loss (CL) with T2. In this work, the beef quality has been analyzed evaluated by transverse relaxation time (T2) measured by CPMG pulse sequence and by the T2, and, longitudinal relaxation time (T1), measured by CWFP (Continuous Wave Free Precession) technique The samples were collected from Longissimus lumborium muscle, from the 12th and 6th ribs region. The animals from three genetic groups, (two were ¾ and one was 9/16 european) were used in the experiments. The CPMG data were processed by multiexponential fitting, inverse Laplace transformation and chemometrics methods such as principal component analysis, hierarchic cluster analysis and partial least square regression. The CPMG data were not able to distinguish between the genetic group sex and the meat cut. The CWFP data analyzed by chemometrics methods show difference between Canchin males and females and between Canchin and Angus males. The results show almost no difference between the samples collected from the 12th and 6th rib.

O reconhecimento facial é uma tarefa que os seres humanos realizam naturalmente todos os dias e praticamente sem esforço nenhum. Porém para uma máquina este processo não é tão simples. Com o aumento do poder computacional das máquinas atuais criou-se um grande interesse no processamento de imagens e vídeos digitais, com aplicações nas mais diversas áreas de conhecimento. Este trabalho objetiva a comparação de técnicas de reconhecimento facial, já conhecidas na literatura, com o intuito de identificar qual técnica possui melhor desempenho em um ambiente real e semicontrolado. Secundariamente avalia-se a possibilidade da utilização de uma ou mais técnicas de reconhecimento facial para identificar automaticamente a presença de alunos em uma sala de aula de artes marciais, utilizando imagens das câmeras de vigilância instaladas no recinto, levando em consideração aspectos importantes, tais como: imagens com pouca nitidez, luminosidade não ideal, movimentação constante dos alunos e o fato das câmeras estarem em um ângulo fixo. Este trabalho está relacionado às áreas de Processamento de Imagens e Reconhecimento de Padrões, e integra a linha de pesquisa de \"Monitoramento de Presença\" do projeto \"Ensino e Monitoramento de Atividades Físicas via Técnicas de Inteligência Artificial\" (Processo 2014.1.923.86.4, publicado no DOE 125(45), em 10/03/2015), projeto este executado em conjunto da Universidade de São Paulo, Faculdade Campo Limpo Paulista e Academia Central Kungfu-Wushu. Com os experimentos realizados e apresentados neste trabalho foi possível concluir que, dentre os métodos de reconhecimento facial utilizados, o método Local Binary Patterns teve o melhor desempenho no ambiente proposto. Por outro lado, o método Eigenfaces teve o pior desempenho de acordo com os experimentos realizados. Além disso, foi possível concluir também que não é viável a realização da detecção de presença automática de forma confiável no ambiente proposto, pois a taxa de reconhecimento facial foi relativamente baixa, se comparada a outros trabalhos do estado da arte, trabalhos estes que usam de ambientes de testes mais amigáveis, mas ao mesmo tempo menos comumente encontrados em nosso dia-a-dia. Acredita-se que foi possível alcançar os objetivos propostos pelo trabalho e que o mesmo possa contribuir para o estado da arte atual na área de visão computacional, mais precisamente no âmbito do reconhecimento facial. Ao final são sugeridos alguns trabalhos futuros que podem ser utilizados como ponto de partida para a continuação desta pesquisa ou até mesmo de novas pesquisas relacionadas a este tema / Face recognition is a task that human beings perform naturally in their everyday lives, usually with no effort at all. To machines, however, this process is not so simple. With the increasing computational power of current machines, a great interest was created in the field of digital videos and images processing, with applications in most diverse areas of knowledge. This work aims to compare face recognition techniques already know in the literature, in order to identify which technique has the best performance in a real and semicontrolled environment. As a secondary objective, we evaluate the possibility of using one or more face recognition techniques to automatically identify the presence of students in a martial arts classroom using images from the surveillance cameras installed in the room, taking into account important aspects such as images with low sharpness, illumination variation, constant movement of students and the fact that the cameras are at a fixed angle. This work is related to the Image Processing and Pattern Recognition areas, and integrates the research line \"Presence Monitoring\" of the project entitled \"Education and Monitoring of Physical Activities using Artificial Intelligence Techniques\" (Process 2014.1.923.86.4, published in DOE 125 (45) on 03/10/2015), developed as a partnership between the University of São Paulo, Campo Limpo Paulista Faculty, and Kungfu-Wushu Central Academy. With the experiments performed and presented in this work it was possible to conclude that, amongst all face recognition methods that were tested, Local Binary Patterns had the best performance in the proposed environment. On the other hand, Eigenfaces had the worse performance according to the experiments. Moreover, it was also possible to conclude that it is not feasible to perform the automatic presence detection reliably in the proposed environment, since the face recognition rate was relatively low, compared to the state of the art which uses, in general, more friendly test environments but at the same time less likely found in our daily lives. We believe that it was possible to achieve the objectives proposed by this work and that can contribute to the current state of the art in the computer vision field and, more precisely, in the face recognition area. Finally, some future work is suggested that can be used as a starting point for the continuation of this work or even for new researches related to this topic

O reconhecimento facial é uma tarefa que os seres humanos realizam naturalmente todos os dias e praticamente sem esforço nenhum. Porém para uma máquina este processo não é tão simples. Com o aumento do poder computacional das máquinas atuais criou-se um grande interesse no processamento de imagens e vídeos digitais, com aplicações nas mais diversas áreas de conhecimento. Este trabalho objetiva a comparação de técnicas de reconhecimento facial, já conhecidas na literatura, com o intuito de identificar qual técnica possui melhor desempenho em um ambiente real e semicontrolado. Secundariamente avalia-se a possibilidade da utilização de uma ou mais técnicas de reconhecimento facial para identificar automaticamente a presença de alunos em uma sala de aula de artes marciais, utilizando imagens das câmeras de vigilância instaladas no recinto, levando em consideração aspectos importantes, tais como: imagens com pouca nitidez, luminosidade não ideal, movimentação constante dos alunos e o fato das câmeras estarem em um ângulo fixo. Este trabalho está relacionado às áreas de Processamento de Imagens e Reconhecimento de Padrões, e integra a linha de pesquisa de \"Monitoramento de Presença\" do projeto \"Ensino e Monitoramento de Atividades Físicas via Técnicas de Inteligência Artificial\" (Processo 2014.1.923.86.4, publicado no DOE 125(45), em 10/03/2015), projeto este executado em conjunto da Universidade de São Paulo, Faculdade Campo Limpo Paulista e Academia Central Kungfu-Wushu. Com os experimentos realizados e apresentados neste trabalho foi possível concluir que, dentre os métodos de reconhecimento facial utilizados, o método Local Binary Patterns teve o melhor desempenho no ambiente proposto. Por outro lado, o método Eigenfaces teve o pior desempenho de acordo com os experimentos realizados. Além disso, foi possível concluir também que não é viável a realização da detecção de presença automática de forma confiável no ambiente proposto, pois a taxa de reconhecimento facial foi relativamente baixa, se comparada a outros trabalhos do estado da arte, trabalhos estes que usam de ambientes de testes mais amigáveis, mas ao mesmo tempo menos comumente encontrados em nosso dia-a-dia. Acredita-se que foi possível alcançar os objetivos propostos pelo trabalho e que o mesmo possa contribuir para o estado da arte atual na área de visão computacional, mais precisamente no âmbito do reconhecimento facial. Ao final são sugeridos alguns trabalhos futuros que podem ser utilizados como ponto de partida para a continuação desta pesquisa ou até mesmo de novas pesquisas relacionadas a este tema / Face recognition is a task that human beings perform naturally in their everyday lives, usually with no effort at all. To machines, however, this process is not so simple. With the increasing computational power of current machines, a great interest was created in the field of digital videos and images processing, with applications in most diverse areas of knowledge. This work aims to compare face recognition techniques already know in the literature, in order to identify which technique has the best performance in a real and semicontrolled environment. As a secondary objective, we evaluate the possibility of using one or more face recognition techniques to automatically identify the presence of students in a martial arts classroom using images from the surveillance cameras installed in the room, taking into account important aspects such as images with low sharpness, illumination variation, constant movement of students and the fact that the cameras are at a fixed angle. This work is related to the Image Processing and Pattern Recognition areas, and integrates the research line \"Presence Monitoring\" of the project entitled \"Education and Monitoring of Physical Activities using Artificial Intelligence Techniques\" (Process 2014.1.923.86.4, published in DOE 125 (45) on 03/10/2015), developed as a partnership between the University of São Paulo, Campo Limpo Paulista Faculty, and Kungfu-Wushu Central Academy. With the experiments performed and presented in this work it was possible to conclude that, amongst all face recognition methods that were tested, Local Binary Patterns had the best performance in the proposed environment. On the other hand, Eigenfaces had the worse performance according to the experiments. Moreover, it was also possible to conclude that it is not feasible to perform the automatic presence detection reliably in the proposed environment, since the face recognition rate was relatively low, compared to the state of the art which uses, in general, more friendly test environments but at the same time less likely found in our daily lives. We believe that it was possible to achieve the objectives proposed by this work and that can contribute to the current state of the art in the computer vision field and, more precisely, in the face recognition area. Finally, some future work is suggested that can be used as a starting point for the continuation of this work or even for new researches related to this topic