2,988 research outputs found
Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding
Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness
DBC based Face Recognition using DWT
The applications using face biometric has proved its reliability in last
decade. In this paper, we propose DBC based Face Recognition using DWT (DBC-
FR) model. The Poly-U Near Infra Red (NIR) database images are scanned and
cropped to get only the face part in pre-processing. The face part is resized
to 100*100 and DWT is applied to derive LL, LH, HL and HH subbands. The LL
subband of size 50*50 is converted into 100 cells with 5*5 dimention of each
cell. The Directional Binary Code (DBC) is applied on each 5*5 cell to derive
100 features. The Euclidian distance measure is used to compare the features of
test image and database images. The proposed algorithm render better percentage
recognition rate compared to the existing algorithm.Comment: 15 pages,9 figures, 4 table
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Multi-aspect testing and ranking inference to quantify dimorphism in the cytoarchitecture of cerebellum of male, female and intersex individuals: a model applied to bovine brains
The dimorphism among male, female and freemartin intersex bovines, focusing on the vermal lobules VIII and IX, was analyzed using a novel data analytics approach to quantify morphometric differences in the cytoarchitecture of digitalized sections of the cerebellum. This methodology consists of multivariate and multi-aspect testing for cytoarchitecture-ranking, based on neuronal cell complexity among populations defined by factors, such as sex, age or pathology. In this context, we computed a set of shape descriptors of the neural cell morphology, categorized them into three domains named size, regularity and density, respectively. The output and results of our methodology are multivariate in nature, allowing an in-depth analysis of the cytoarchitectonic organization and morphology of cells. Interestingly, the Purkinje neurons and the underlying granule cells revealed the same morphological pattern: female possessed larger, denser and more irregular neurons than males. In the Freemartin, Purkinje neurons showed an intermediate setting between males and females, while the granule cells were the largest, most regular and dense. This methodology could be a powerful instrument to carry out morphometric analysis providing robust bases for objective tissue screening, especially in the field of neurodegenerative pathologies
Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease
We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions. Our framework automatically identifies the structures most affected by the disease. We evaluate our results by comparing to other methods using a standardized data set of 375 adults available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our framework is sensitive to identifying the onset of Alzheimer’s disease, achieving up to 88.13% accuracy in classifying MCIc versus NC, outperforming previous methods.National Science Foundation (U.S.) (1502435
Radiomic Texture Feature Descriptor to Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal MRI
Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can accurately distinguish rBT from RN. In this work, sophisticated radiomic texture features are used to distinguish rBT from RN on multimodal MRI for disease characterization. First, stochastic multiresolution radiomic descriptor that captures voxel-level textural and structural heterogeneity as well as intensity and histogram features are extracted. Subsequently, these features are used in a machine learning setting to characterize the rBT from RN from four sequences of the MRI with 155 imaging slices for 30 GBM cases (12 RN, 18 rBT). To reduce the bias in accuracy estimation our model is implemented using Leave-one-out crossvalidation (LOOCV) and stratified 5-fold cross-validation with a Random Forest classifier. Our model offers mean accuracy of 0.967 ± 0.180 for LOOCV and 0.933 ± 0.082 for stratified 5-fold cross-validation using multiresolution texture features for discrimination of rBT from RN in this study. Our findings suggest that sophisticated texture feature may offer better discrimination between rBT and RN in MRI compared to other works in the literature
Reconhecimento de padrões em expressões faciais : algoritmos e aplicações
Orientador: HĂ©lio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O reconhecimento de emoções tem-se tornado um tĂłpico relevante de pesquisa pela comunidade cientĂfica, uma vez que desempenha um papel essencial na melhoria contĂnua dos sistemas de interação humano-computador. Ele pode ser aplicado em diversas áreas, tais como medicina, entretenimento, vigilância, biometria, educação, redes sociais e computação afetiva. Há alguns desafios em aberto relacionados ao desenvolvimento de sistemas emocionais baseados em expressões faciais, como dados que refletem emoções mais espontâneas e cenários reais. Nesta tese de doutorado, apresentamos diferentes metodologias para o desenvolvimento de sistemas de reconhecimento de emoções baseado em expressões faciais, bem como sua aplicabilidade na resolução de outros problemas semelhantes. A primeira metodologia Ă© apresentada para o reconhecimento de emoções em expressões faciais ocluĂdas baseada no Histograma da Transformada Census (CENTRIST). Expressões faciais ocluĂdas sĂŁo reconstruĂdas usando a Análise Robusta de Componentes Principais (RPCA). A extração de caracterĂsticas das expressões faciais Ă© realizada pelo CENTRIST, bem como pelos Padrões Binários Locais (LBP), pela Codificação Local do Gradiente (LGC) e por uma extensĂŁo do LGC. O espaço de caracterĂsticas gerado Ă© reduzido aplicando-se a Análise de Componentes Principais (PCA) e a Análise Discriminante Linear (LDA). Os algoritmos K-Vizinhos mais PrĂłximos (KNN) e Máquinas de Vetores de Suporte (SVM) sĂŁo usados para classificação. O mĂ©todo alcançou taxas de acerto competitivas para expressões faciais ocluĂdas e nĂŁo ocluĂdas. A segunda Ă© proposta para o reconhecimento dinâmico de expressões faciais baseado em Ritmos Visuais (VR) e Imagens da HistĂłria do Movimento (MHI), de modo que uma fusĂŁo de ambos descritores codifique informações de aparĂŞncia, forma e movimento dos vĂdeos. Para extração das caracterĂsticas, o Descritor Local de Weber (WLD), o CENTRIST, o Histograma de Gradientes Orientados (HOG) e a Matriz de CoocorrĂŞncia em NĂvel de Cinza (GLCM) sĂŁo empregados. A abordagem apresenta uma nova proposta para o reconhecimento dinâmico de expressões faciais e uma análise da relevância das partes faciais. A terceira Ă© um mĂ©todo eficaz apresentado para o reconhecimento de emoções audiovisuais com base na fala e nas expressões faciais. A metodologia envolve uma rede neural hĂbrida para extrair caracterĂsticas visuais e de áudio dos vĂdeos. Para extração de áudio, uma Rede Neural Convolucional (CNN) baseada no log-espectrograma de Mel Ă© usada, enquanto uma CNN construĂda sobre a Transformada de Census Ă© empregada para a extração das caracterĂsticas visuais. Os atributos audiovisuais sĂŁo reduzidos por PCA e LDA, entĂŁo classificados por KNN, SVM, RegressĂŁo LogĂstica (LR) e Gaussian NaĂŻve Bayes (GNB). A abordagem obteve taxas de reconhecimento competitivas, especialmente em dados espontâneos. A penĂşltima investiga o problema de detectar a sĂndrome de Down a partir de fotografias. Um descritor geomĂ©trico Ă© proposto para extrair caracterĂsticas faciais. Experimentos realizados em uma base de dados pĂşblica mostram a eficácia da metodologia desenvolvida. A Ăşltima metodologia trata do reconhecimento de sĂndromes genĂ©ticas em fotografias. O mĂ©todo visa extrair atributos faciais usando caracterĂsticas de uma rede neural profunda e medidas antropomĂ©tricas. Experimentos sĂŁo realizados em uma base de dados pĂşblica, alcançando taxas de reconhecimento competitivasAbstract: Emotion recognition has become a relevant research topic by the scientific community, since it plays an essential role in the continuous improvement of human-computer interaction systems. It can be applied in various areas, for instance, medicine, entertainment, surveillance, biometrics, education, social networks, and affective computing. There are some open challenges related to the development of emotion systems based on facial expressions, such as data that reflect more spontaneous emotions and real scenarios. In this doctoral dissertation, we propose different methodologies to the development of emotion recognition systems based on facial expressions, as well as their applicability in the development of other similar problems. The first is an emotion recognition methodology for occluded facial expressions based on the Census Transform Histogram (CENTRIST). Occluded facial expressions are reconstructed using an algorithm based on Robust Principal Component Analysis (RPCA). Extraction of facial expression features is then performed by CENTRIST, as well as Local Binary Patterns (LBP), Local Gradient Coding (LGC), and an LGC extension. The generated feature space is reduced by applying Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for classification. This method reached competitive accuracy rates for occluded and non-occluded facial expressions. The second proposes a dynamic facial expression recognition based on Visual Rhythms (VR) and Motion History Images (MHI), such that a fusion of both encodes appearance, shape, and motion information of the video sequences. For feature extraction, Weber Local Descriptor (WLD), CENTRIST, Histogram of Oriented Gradients (HOG), and Gray-Level Co-occurrence Matrix (GLCM) are employed. This approach shows a new direction for performing dynamic facial expression recognition, and an analysis of the relevance of facial parts. The third is an effective method for audio-visual emotion recognition based on speech and facial expressions. The methodology involves a hybrid neural network to extract audio and visual features from videos. For audio extraction, a Convolutional Neural Network (CNN) based on log Mel-spectrogram is used, whereas a CNN built on Census Transform is employed for visual extraction. The audio and visual features are reduced by PCA and LDA, and classified through KNN, SVM, Logistic Regression (LR), and Gaussian NaĂŻve Bayes (GNB). This approach achieves competitive recognition rates, especially in a spontaneous data set. The second last investigates the problem of detecting Down syndrome from photographs. A geometric descriptor is proposed to extract facial features. Experiments performed on a public data set show the effectiveness of the developed methodology. The last methodology is about recognizing genetic disorders in photos. This method focuses on extracting facial features using deep features and anthropometric measurements. Experiments are conducted on a public data set, achieving competitive recognition ratesDoutoradoCiĂŞncia da ComputaçãoDoutora em CiĂŞncia da Computação140532/2019-6CNPQCAPE
Differences in topological progression profile among neurodegenerative diseases from imaging data
The spatial distribution of atrophy in neurodegenerative diseases suggests that brain connectivity mediates disease propagation. Different descriptors of the connectivity graph potentially relate to different underlying mechanisms of propagation. Previous approaches for evaluating the influence of connectivity on neurodegeneration consider each descriptor in isolation and match predictions against late-stage atrophy patterns. We introduce the notion of a topological profile - a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease. By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels. Experimental results comparing topological profiles for Alzheimer's disease, multiple sclerosis and normal ageing show that topological profiles explain the observed data better than single descriptors. Within each condition, most individual profiles cluster around the cohort-level profile, and individuals whose profiles align more closely with other cohort-level profiles show features of that cohort. The cohort-level profiles suggest new insights into the biological mechanisms underlying pathology propagation in each disease
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