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Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
Based on the realization that more than 100 Tokyo children have died after falling from balconies during the last five years, this paper proposes a new system for detecting potentially dangerous situations using red green blue-depth (RGB-D) cameras, and thus aims to help prevent children from falling from balconies. The system, which is based on the results of our investigation into how residents...
Fast and robust 3D reconstruction of facial geometric structure from a single image is a challenging task with numerous applications, but there exist two problems when applied "in the wild": the 3D estimates are unstable for different photos of the same subject; the 3D estimates are over-regularized and generic. In response, a robust method for regressing discriminative 3D morphable face...
In total hip arthroplasty, a placement of stem, which is one of the parts of the artificial hip joint, is even difficult for skilled operators. An optimum location of stem is different for each patient. No system for specifying the optimum location has been established. We pay attention to the method using ultrasound in consideration of portability and high introduction ratio to the medical field...
The present paper details a novel methodology called Meta-Process Model that is able to generate new data-based models for manufacturing processes when no experimental data is available. For that purpose, the concept of Hyper-Models was used to create a higher level of abstraction of these manufacturing processes, along with a Statistical Shape Model (SSM) that is able to capture the modes of shape...
Recent advances in convolutional neural networks have shown promising results in 3D shape completion. But due to GPU memory limitations, these methods can only produce low-resolution outputs. To inpaint 3D models with semantic plausibility and contextual details, we introduce a hybrid framework that combines a 3D Encoder-Decoder Generative Adversarial Network (3D-ED-GAN) and a Longterm Recurrent Convolutional...
Modelling of a database performance depending on numerous factors is the first step towards its optimization. The linear regression model with optional parameters was created. Regression equation coefficients are optimized with the Flower Pollination metaheuristic algorithm. The algorithm is executed with numerous possible execution parameter combinations and results are discussed. Potential obstacles...
Visualization of data helps to transform a problem into a perceptual task that is easier to comprehend. It helps users to perceive patterns from large amount of data in order to make informed decisions. When visualization is coupled with interactive techniques, it boosts cognition levels in users, as the users can now directly interact with the data. In this paper, we present some of the popular techniques...
A novel procedure to determine foot shape is propose. The relationship between shape features is used to discriminate shape. Five foot shape features that are Left Foot Length (LFL), Left Foot Breadth (LFB), Left Ball Girth Circumference (LBGC), Left Instep Length (LIL) and Left Fibulare Instep Length (LFIL) from a random sample of 161 Malaysians ladies were studied. Two groups of foot shape feature...
Data stream clustering is an active area of research in big data. It refers to clustering constantly arriving new data records and updating existing cluster patterns and outliers in light of the newly arriving data. Density-based algorithms for solving this problem have the promise for finding arbitrary shape clusters and detecting anomalies without prior knowledge of the number of clusters. In this...
3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful...
Shape models provide a compact parameterization of a class of shapes, and have been shown to be important to a variety of vision problems, including object detection, tracking, and image segmentation. Learning generative shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with intractable marginals and posteriors, (2) high-dimensional shape...
There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we...
Non-Gaussian statistical models fit SAR data better than Gaussian-based statistics, in most cases, but are complicated and time-consuming to use for unsupervised image segmentation via probabilistic clustering. The more advanced the model, the more complicated and slow the clustering. The U-distribution has been demonstrated to be one of the most flexible models, capturing the Gaussian/Wishart, the...
Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light...
Compressor maps are one of the main elements describing the behaviour of centrifugal compressors. Although the compressor map is often provided by the manufacturer, there may be changes during the lifetime of the compressor due to refurbishments or wear. Since the compressor maps are often used in real-time optimization problems, there is a need for simple approximation methods. This paper focuses...
Our previous work proposed hand posture estimation technique. The hand region is first extracted using depth image, and then the initial features, such as fingertip, hand center point, and palm size, have been calculated. The concept of active contour using energy function is implemented in order to track fingertip position in the frame image sequence. To discriminate the hand posture sets, a hand...
Advanced Driver Assistance Systems have been shown to greatly improve road safety. However, existing systems are typically reactive with an inability to understand complex traffic scenarios. We present a method to predict driver intention as the vehicle enters an intersection using a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). The model is learnt using the position, heading...
A real-time demand response system can be viewed as a cyber-physical system, with physical systems dependent on cyber infrastructure for coordination and control, which may be vulnerable to cyber-attacks. The time domain dynamic behaviour of individual residential demand responses is governed by a mix of physical system parameters, exogenous influences, user behaviour and preferences, which can be...
In this paper, an overview on existing data mining techniques for time series modeling and analysis will be provided. Classification of available literature on time series data mining shows that the main research orientations can be divided into three subfields: Dimensionality Reduction (Time Series Representation), Similarity Measures and Data Mining Tasks.
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