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The facial feature points localization is the core of face recognition, and its accuracy directly affects the accuracy of face recognition system. The accuracy of facial feature points is affected by light, noise, background, and face gestures. Considering the theoretical value and practical significance of facial feature points localization, this thesis goes into the most advanced algorithm of facial...
In this paper, we propose a novel multi-center convolutional neural network for unconstrained face alignment. To utilize structural correlations among different facial landmarks, we determine several clusters based on their spatial position. We pre-train our network to learn generic feature representations. We further fine-tune the pre-trained model to emphasize on locating a certain cluster of landmarks...
Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regressionbased approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories...
Facial landmark detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial landmark detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work,...
Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using...
In this paper, we propose a large vocabulary Mongolian offline handwriting recognition system, using hidden Markov models (HMMs)-deep neural networks (DNN) hybrid architectures which shows superior performance on auto speech recognize (ASR) tasks. We select 50 sub-characters from all shape of Mongolian letters as the smallest modeling unit. First, a set of intensity features are extracted from each...
In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular...
Household appliance classification, based on electricity usage patterns, is gaining a momentum in an era where energy saving has become a priority and connected objects are leveraged to influence consumers' behaviors. In this respect, electricity usage profiling of household appliances is an important step for identifying malfunctioning devices and generating automatic alerts about unusual consumptions...
We propose Multi-View Constrained Local Models - a simple but effective technique for improving facial point detection under large head angles, such as in a car driving setting. Our approach combines a global shape model with separate sets of response maps targeted at different head angles, indexed on the shape model parameters. We explore shape-space division strategies and show that, as well as...
Faces convey much information. Interestingly we humans have a remarkable ability of identifying, extracting, and interpreting this information. Recently automatic facial ageing (AFA) has gained popularity due to its numerous applications which include search for missing people, biometrics, and multimedia. The problem of AFA is faced with various challenges, including incomplete training datasets,...
In this paper, the shape features of different material of flame and interference image in video fire detection were analyzed and compared. The area, perimeter, rotundity, solidity, extent, sharp corners, similarity and centroid displacement are selected as the candidate features. After analyzing and excluding area and perimeter as these candidate features, weak classifiers of six other shape features...
We develop the study of primitives of human motion, which we refer to as movemes. The idea is to understand human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. How can we construct an alphabet of movemes from human data? In this paper we address this issue by introducing the notion of well-posednes. Using examples from...
Segmentation of the Right Ventricle (RV) from cardiac MRI images is necessary for evaluating a number of cardiopulmonary and cardiovascular disorders. Active Shape Models (ASM) have been proposed to capture the variability among the different RV shapes and used to segment the RV. Nevertheless, the method is challenged by the complexity and the large variability among the RV shapes. In this work, we...
Hidden Markov Models (HMM) are used in handwritten strokes recognition task. The two design parameters of HMM are the number of states and number of mixtures in each state. There are two approaches for finding the number of states, namely, equal number of states and variable number of states. Since the shape of strokes will be different, variable number of states approach should be beneficial. This...
The main aim of this paper is to advance the state of the art in automated prostate segmentation using T2 weighted MR images, by introducing a hybrid topological MRI prostate segmentation method which is based on a set of pre-labeled MR atlas images. The proposed method has been experimentally tested on a set of 30 MRI T2 weighted images. For evaluation the automated segmentations of the proposed...
Several powerful approaches have recently been proposed for writer identification, which rely on local descriptors that capture the texture, shape and curvature properties of the handwriting. In this paper we use combinations of three of these features (K-Adjacent Segments, SURF, and Contour Gradient Descriptors), to address the writer identification problem. Experiments demonstrate that feature combinations...
Based on analysis of plate shape defect pattern in cold rolling, a defect recognition method using RBF-BP combinational neural network model optimized by genetic algorithm is proposed in this paper. The method makes use of genetic algorithm to optimize the weights and thresholds of the input layer, hidden layer and output layer in the RBF-BP network, and a GA-RBF-BP network model is formed. It can...
This paper presents a pairwise approach of finding shape correspondence for the construction of SSM, applied to distal femur bone. The correspondence was calculated by adapting a representative shape (template mesh) onto the training shapes in two steps. The first step was global registration which involved initial template mesh deformation using Laplacian Surface Deformation (LSD) method guided by...
A good model of object shape is essential in applications such as segmentation, object detection, inpainting and graphics. For example, when performing segmentation, local constraints on the shape can help where the object boundary is noisy or unclear, and global constraints can resolve ambiguities where background clutter looks similar to part of the object. In general, the stronger the model of...
This paper presents a new method for segmentation of ambiguously defined structures, such as the hippocampus, by exploiting prior knowledge from another perspective. An expert's experience of where to use prior knowledge and where image information, is captured as a local weighting map. This map can be used to locally guide the evolution in a level set evolution framework. Such a map is produced for...
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