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We present a novel model to represent and match contour lines of closed shapes. This model is based on the mechanism of visual cortex. It extracts orientation features from input images with simple computation units that imitate simple cells in the visual cortex. The contour lines are accurately located by searching adjacent activated simple units. These activated simple units are concatenated in...
The IEEE is currently developing a standard “Electrical characterization of printed circuit board and related interconnects at frequencies up to 50GHz” (IEEE Std P370). As part of the development of this standard, a technique (or techniques) is (are) required in order to provide a measure of the goodness-of-fit between measured and simulated data for a structure or between different measurements or...
Directly embedded foundations have long been used by electrical utilities for power lines with wood or steel poles. A “rule of thumb” approach of “10% length plus 2ft” has usually been used to design directly embedded wood poles. While the approach works well in overall from the past experience, its ignorance of the effect of soil and pole properties leads to an inconsistent design: either overly...
Topic modeling is a widely used approach for analyzing large text collections. In particular, Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling approaches to aggregate vocabulary from a document corpus to form latent "topics". However, learning meaningful topic models with massive document collections which contain millions of documents, billions of tokens is challenging,...
The quality of living spaces has long been a key issue in design focused on user needs. To correctly define the environmental quality of indoor spaces, many aspects must be considered, which necessarily involves transverse knowledge. An important role in the quality of spaces is the correct use of natural light to ensure good lighting and user comfort. This is even truer within historical buildings...
Visual Field (VF) tests and their corresponding data are commonly used in clinical practices to manage glaucoma. The data represents patient visual acuity, which determines whether the patient has good or impaired vision. Developing machine learning and data mining algorithms that explore the spatial and temporal aspects of visual filed data could vastly improve early diagnosis as well as assisting...
Multimodal recognition has recently become more attractive and common method in multimedia information retrieval. In many cases it shows better recognition results than using only unimodal methods. Most of current multimodal recognition methods still depend on unimodal recognition results. Therefore, in order to get better recognition performance, it is important to choose suitable features and classification...
In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation...
It is with the advancement of overwhelming wireless internet access in mobile environments, users and usage data has become huge and voluminous on regular basis. For instance, the financial transactions performed via online by users are unsecure and unauthenticated in many contexts. Methods and algorithms exist for secure data transmission over different channels, perhaps lacks to achieve high performance...
Studies of time-varying or dynamic brain connectivity (BC) using functional magnetic resonance imaging (fMRI) are crucial to understand the relationship between different brain regions. This paper presents a novel method for estimating dynamic BC using a time-varying multivariate autoregressive (AR) model with spatial sparsity and temporal continuity constraints. The problem is formulated as a maximum...
In this paper, we present a novel visual content search approach that can query quite fast, have low memory need and achieve successful results particularly for the instances with minor content changes. For this purpose, first, the content of a video is represented with sparsely sampled edge energy variations among video frames. Then, these high dimensional features are converted into simple signatures...
Recently, deep Convolutional Neural Networks (CNNs) have been used to achieve state-of-the-art performance on a wide range of visual learning tasks. However, when facing some imbalanced learning tasks where the training samples are unevenly distributed among different classes, CNNs tend to produce performance bias toward the majority class, making them not suitable for applications in which the recognition...
Similarity learning between textual query and visual images is a fundamental problem in large scale image retrieval. Traditional methods primarily rely on the surrounding texts of images for image search. However, as the volume of images on the web grows to new levels, it is likely that the surrounding textual information is noisy or even unavailable. Thus, determining how to bridge the semantic gap...
Interactive visualization has become a popular approach to support decision makers coping with complex decision problems in the commercial world. However, it is not clear that these interactive visualizations result in better decision making. Human judgment under uncertainty is known to fall victim to a number of biases that result from the heuristics that we employ, yet the design of many interactive...
Audio/visual recognition and retrieval applications have recently garnered significant attention within Internet-of-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise...
We report the findings of a study designed to evaluate the effect of stereopsis and field of regard (FOR) in two different mixed reality (MR) simulation platforms: a head-mounted display (HMD) and a CAVE. We compared the performance of participants on two levels of stereopsis (mono and stereo) and two levels of FOR (90 degrees and 270 degrees) using a variety of scientific visualization tasks. Among...
Deformable part models exhibit excellent performance in tracking non-rigidly deforming targets, but are usually outperformed by holistic models when the target does not deform or in the presence of uncertain visual data. The reason is that part-based models require estimation of a larger number of parameters compared to holistic models and since the updating process is self-supervised, the errors...
In order to provide a watching service for the elderly with dementia, recognizing and assessing their cognitive and health status are indispensable. In this study, we propose a prediction model for assessing the communication attitude of the elderly during interacting with a virtual agent. We define speech features and head motion features using frequency analysis and apply them to a linear regression...
Currently, how to deeply distill potential attributes of big data has become a great challenge for structured, semi-structured and unstructured data (SSU data) with a unified model. Structured data refers to any data that resides in a fixed field within a record or file including data contained in relational databases and spreadsheets. Unstructured data refers to data from text, pictures, audio, video,...
Attributes are semantic visual properties shared by objects. They have been shown to improve object recognition and to enhance content-based image search. While attributes are expected to cover multiple categories, e.g. a dalmatian and a whale can both have "smooth skin", we find that the appearance of a single attribute varies quite a bit across categories. Thus, an attribute model learned...
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