The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Even small changes of electrode recording sites after training a classifier heavily influence robustness and usability of traditional pattern recognition-based myoelectric control schemes. This effect occurs during donning and doffing of the prosthesis or when changing the arm position and generally leads to a significant decrease of classification accuracy. On the other hand, image representations...
Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this...
Pattern recognition based myoelectric control has been studied by many researchers. However, the classification accuracy was pretty low for amputees towards multifunctional prosthesis control in practice. In this work, a novel method of transcranial direct current stimulation (tDCS) which can modulate brain activity was used to enhance performance for myoelectric prosthesis control. The pilot study...
In this paper, a modified K-means algorithm is proposed to categorize a set of data. K-means algorithm is a simple and easy clustering method which can efficiently classify a large number of continuous numerical data of high-dimensions. Moreover, the data in each cluster are similar to one another. However, it is vulnerable to outliers and noisy data, and it spends much executive time in classifying...
We propose a method to effectively extract lattice openings (windows and doors) of traditional Japanese buildings from terrestrial light detection and ranging data. First, the points on a wall are extracted and edges that indicate non-flat surfaces are selected by examining shifts that occur when the data are smoothed. Next, wall direction is determined. Following this, the edges are grouped along...
This paper proposes a method for estimating the green space ratio in urban areas by using airborne LiDAR and aerial photographs. The index is defined as the ratio of an area occluded by vegetation to the whole of an area in an azimuth-elevation angle space. Vegetation is detected by a combination of segmented LiDAR data point clouds and image brightness data. The occlusion by vegetation is calculated...
In this paper, we propose a framework which self-organizes the cascaded object detection filters for fast object detection with maintaining high accuracy. The proposed scheme consists of root and part filter modules, which are cascaded in a self-organizing structure. The pruning of non-object regions in low resolution at the root cascade stage is critical for the object detection speed. At root stage,...
Clustering is a hotspot issues in the field of data mining. There is abundant digital image information in the image acquisition equipment, the image database or the Internet. Facing the large scale image information with rich semantics, it is difficult to obtain accurate information as soon as possible. Therefore, it is essential for us to study efficient image clustering algorithms, in which how...
Context information has been widely studied for recognizing collective activities. Most existing works assume that all individuals in a single image share the same activity label. However, in many cases, multiple activities can be coexisted and serve as the context for each other in real-world scenarios. Based on this observation, we propose a novel approach to model both the intra-class and inter-class...
Human action recognition using depth information is a trending technology especially in human computer interaction. Depth information may provide more robust features to increase accuracy of action recognition. This paper presents an approach to recognize basic human actions using the depth information from RGB-D sensors. Features obtained from a trained skeletal model and raw depth data are studied...
In this paper a method for Early Recognition (ER) of Motion Templates (MTs) is presented. We define ER as an algorithm to provide recognition results before a motion sequence is completed. In our experiments we apply Long Short-Term Memory (LSTM) and optimize the training for the task of recognizing the motion template as early as possible. The evaluation has shown that the recognition accuracy for...
Inspired by the recent success of hierarchical representation, we propose a new hierarchical variant of latent Dirichlet allocation (h-LDA) for action recognition. The model consists of an appearance group and a motion group, and we introduce a new hierarchical structure including two-layer topics in each group to learn the spatial temporal patterns (STPs) of human actions. The basic idea is that...
This paper presents a Convolutional Neural Network (CNN) for document image classification. In particular, document image classes are defined by the structural similarity. Previous approaches rely on hand-crafted features for capturing structural information. In contrast, we propose to learn features from raw image pixels using CNN. The use of CNN is motivated by the the hierarchical nature of document...
Spectral clustering has been successfully used in various applications, thanks to its properties such as no requirement of a parametric model, ability to extract clusters of different characteristics and easy implementation. However, it is often infeasible for large datasets due to its heavy computational load and memory requirement. To utilize its advantages for large datasets, it is applied to the...
We proposed a framework for human action recognition by learning pose dictionary as the human appearance representation. At first, the shape based pose feature is constructed based on the contour points of the human silhouette and invariant to translation and scaling. After the local pose features are extracted from the original videos, the class-specific dictionaries are learned individually on the...
There has been an ongoing effort in improving reliability and consistency of pathology test results due to their critical role in making an accurate diagnosis. One way to do this is by applying image-based Computer Aided Diagnosis (CAD) systems. This paper proposes a comprehensive benchmarking platform comprising over 1,000 images to evaluate CAD systems for the Anti-Nuclear Antibody (ANA) test via...
Recent works investigated the possibility to design solutions for pattern recognition problems by exploiting the huge amount of work done in bioinformatics. If the pattern recognition problem is cast in biological terms, then a huge range of algorithms, exploitable for classification, detection, visualization, etc. can be effectively borrowed. In this paper, we exploit biological sequence alignment...
Myoelectric pattern recognition applied to high-density surface electromyographic (sEMG) recordings from paretic muscles has been proven to identify various movement intents of stroke survivors, thus facilitating the design of myoelectrically controlled robotic systems for recovery of upper-limb dexterity. Aiming at effectively decoding neural control information under the condition of neurological...
Myoelectric control of prosthetic devices tend to rely on classification schemes of extracted features of EMG data. Those features however, may be sensitive to arm position resulting in decreased performance in real-world applications. The effect of varying limb position in a pattern recognition system have been illustrated by documenting the change in classification accuracy as the user achieves...
Pattern recognition based myoelectric prostheses (MP) need a training procedure for calibrating the classifier. Due to the non-stationarity inhered in surface electromyography (sEMG) signals, the system should be retrained day by day in long-term use of MP. To boost the training procedure in later periods, we propose a method, namely Mixed-LDA, which computes the parameters of LDA through combining...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.