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The effort to integrate emotions into human-computer interaction (HCI) system has attracted broad attentions. Automatic emotion recognition enables the HCI to become more intelligent and user friendly. Although numerous studies have been performed in this field, emotion recognition is still an extremely challenging task, especially in real-world practice usage. In this work, probabilistic neural network...
Deep neural networks are typically represented by a much larger number of parameters than shallow models, making them prohibitive for small footprint devices. Recent research shows that there is considerable redundancy in the parameter space of deep neural networks. In this paper, we propose a method to compress deep neural networks by using the Fisher Information metric, which we estimate through...
The cross-point array architecture with resistive synaptic devices has been proposed for on-chip implementation of weighted sum and weight update in the training process of learning algorithms. However, the non-ideal properties of the synaptic devices available today, such as the nonlinearity in weight update, limited ON/OFF range and device variations, can potentially hamper the learning accuracy...
Corrective learning is a technique that applies classification methods for automatically detecting and correcting systematic segmentation errors produced by existing segmentation methods with respect to some gold standard (manual) segmentation. To allow corrective learning more effectively correct errors that require non-local contextual information to capture, we extend the corrective learning technique...
Bone fractures are among the most common traumas in musculoskeletal injuries. They are also frequently missed during the radiological examination. Thus, there is a need for assistive technologies for radiologists in this field. Previous automatic bone fracture detection work has focused on detection of specific fracture types in a single anatomical region. In this paper, we present a generalized bone...
Anatomical structure labeling in echocardiogram images will assist cardiac disease diagnosis by providing a framework for doing geometrical statistics. General labeling algorithms often focus on stationary body structures and do not perform well in echocardiography due to cardiac motion, low signal to noise ratio, and structural deformation caused by diseases. In this paper, we propose a new method...
Action recognition has been a research challenge in multimedia computing and machine vision. Recent advances in deep learning combined with stacked convolutional Independent Subspace Analysis (ISA) has achieved a better performance superior to all previously published results on several public available data sets. Unfortunately, one major issue in large-scale deployment of this new deep learning-based...
Both appearance and shape play important roles in object localization and object detection. In this paper, we propose a new superedge grouping method for object localization by incorporating both boundary shape and appearance information of objects. Compared with the previous edge grouping methods, the proposed method does not subdivide detected edges into short edgels before grouping. Such long,...
A workload-aware low-power neuromorphic controller for dynamic voltage and frequency scaling (DVFS) in very large scale integration (VLSI) systems is presented. The neuromorphic controller predicts future workload values and preemptively regulates supply voltage and frequency based on past workload profile. Our specific contributions include: 1) implementation of a digital and analog version of the...
Object localization in an image is usually handled by searching for an optimal subwindow that tightly covers the object of interest. However, the subwindows considered in previous work are limited to rectangles or other specified, simple shapes. With such specified shapes, no subwindow can cover the object of interest tightly. As a result, the desired subwindow around the object of interest may not...
Metabonomics is an emerging field providing insight into physiological processes and difference. Besides conventional PCA, PLS and OPLS approaches, more and more machine learning classifiers are likely to become the supplements for metabolic profiling data analysis. A comprehensive comparison of PLS, support vector machine (SVM, with linear and quadratic kernels), linear discriminant analysis (LDA),...
A workload-aware low-power neuromorphic controller for dynamic voltage scaling in VLSI systems is presented. The neuromorphic controller predicts future workload values and preemptively regulates supply voltage based on past workload profile. Our specific contributions include: (1) implementation of a digital and analog version of the controller in 45nm CMOS technology, resulting in 3% performance...
Due to rapid advances in video technology and biomedicine, there has been a tremendous growth in the volume of medical video data for recent years. Exploring the full potential of medical information from these data by semantic analysis is highly desirable and very useful. In this paper, we focus on how to classify images into semantic categories effectively and efficiently. There are two major contributions...
Advances in video technology are being incorporated into todaypsilas medical research and education. Medical videos contain important medical events, such as diagnostic or therapeutic operations. Automatic discovery and classification of these events are highly desirable and very useful. In this paper, we present a novel method for multi-class educational medical video event categorization. Our method...
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