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Electrocardiogram (ECG) as a biological information, it has some special feature. Different people will have different ECG information, even one person has different ECG when he is under different body state. In this paper we use the Electrocardiogram (ECG) to identify disease or to detect different person. Firstly, we collect the ECG information form different body state of the different people....
In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations...
This paper deals with a post-processing phase of automatic transcription of spoken documents stored in the large Czech Radio audio archive (containing hundreds of thousands of recordings). The ultimate goal of the project is to transcribe them and to allow public access to their content. In this paper we focus on methods and algorithms for unsupervised post-processing of automatically recognized recordings...
Human action classification is an important task in computer vision. The Bag-of-Words model uses spatio-temporal features assigned to visual words of a vocabulary and some classification algorithm to attain this goal. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have applied this method to the KTH dataset to obtain a vocabulary with...
Is low-cost tracking precise enough for recognition of pointing actions? We investigate the quality of the human body tracking available with a Kinect camera by comparing it to a state-of-the-art motion capture system. The application is action recognition with parametric hidden Markov Models (PHMMs) for programming industrial robots. The data from the Kinect is overall more noisy and potentially...
In this paper, we examined the feasibility of articulatory phonetic inversion (API) conditioned on the auditory qualities for improved speech recognition. And we introduced an efficient data-driven heuristic learning algorithm to capture the articulatory-phonetic features (APFs) of English speech. Then we reported the performance of the combined auditory and articulatory processing methods in the...
Human pose estimation is a classic problem in computer vision. Statistical models based on part-based modelling and the pictorial structure framework have been widely used recently for articulated human pose estimation. However, the performance of these models has been limited due to the presence of self-occlusion. This paper presents a learning-based framework to automatically detect and recover...
The measurement or evaluation and clinical significance of human sperm morphology has always been and still is a controversial aspect of the semen analysis for the determination of a male's fertility potential. The evaluation of sperm size, shape and morphological smear characteristics should be assesed by carefully observing a stained sperm sample under a microscope. In order to avoid subjectivity,...
We propose a method for human head pose estimation based on images acquired by a depth camera. During an initialization phase, a reference depth image of a human subject is obtained. At run time, the method searches the 6-dimensional pose space to find a pose from which the head appears identical to the reference view. This search is formulated as an optimization problem whose objective function quantifies...
To detect human sex from complex background, illumination variations and objects by machine is very difficult but important for adaptive information service. In this research, we present a preliminary design and experimental results of gender recognition from walking movements that utilizes gait energy image(GEI) with denoised energy image(DEI) pre-processing as support vector machine(SVM) classifier...
Falls are a major threat to the independence and quality of life of elderly people. As the worldwide population of elderly increases each year, responding to falls is essential. Computer vision systems provide a new promising solution in responding falls through detecting fall events. This paper presents a new technique in detecting falls based on human shape variation. The proposed visual based fall...
The paper presents a new online incremental zero-shot learning method for applications in robotics and mobile communications where attribute labeling is obtained via online interaction with users, and where the potential for inconsistency exists. Unique to most previous offline batch learning methods, the proposed method is based on the indirect-attribute-prediction (IAP) model instead of the direct-attribute-prediction...
Visually tracking a large number of objects remains a trade-off between accuracy and amount of data. For applications where high accuracy in both position and orientation of points in space is required, optical tracking systems with passive marker systems are suitable. However, the placement of the marker dots remains problematic, as distin-guishability between the marker alignments considerably reduces...
Our primary motivation in this paper is to determine whether evolved texture feature extraction programs are competitive with human derived programs for a difficult real world texture classification problem. The problem involves distinguishing images of three classes of bulk malt. There are subtle differences between the three classes. We have used a number of human derived methods, Haralick, Gabor,...
We propose a method of clustering images that combines algorithmic and human input. An algorithm provides us with pairwise image similarities. We then actively obtain selected, more accurate pairwise similarities from humans. A novel method is developed to choose the most useful pairs to show a person, obtaining constraints that improve clustering. In a clustering assignment elements in each data...
This paper addresses the problem of scene categorization while arguing that better and more accurate results can be obtained by endowing the computational process with perceptual relations between scene categories. We first describe a psychophysical paradigm that probes human scene categorization, extracts perceptual relations between scene categories, and suggests that these perceptual relations...
The Gaussian Q-function is of great importance in the field of communications, where the noise is often characterized by the Gaussian distribution. However, no simple exact closed form of the Q-function is known. Consequently, a number of approximations have been proposed over the past several decades. In this paper, we use Genetic Programming with semantic based crossover to approximate the Q-function...
In this paper, we propose an effective method to recognize human actions from 3D positions of body joints. With the release of RGBD sensors and associated SDK, human body joints can be extracted in real time with reasonable accuracy. In our method, we propose a new type of features based on position differences of joints, EigenJoints, which combine action information including static posture, motion,...
Many human action recognition tasks involve data that can be factorized into multiple views such as body postures and hand shapes. These views often interact with each other over time, providing important cues to understanding the action. We present multi-view latent variable discriminative models that jointly learn both view-shared and view-specific sub-structures to capture the interaction between...
Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study five supervoxel algorithms in...
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