• Phonetics and Speech Sciences
• /
• v.1 no.1
• /
• pp.41-46
• /
• 2009

Duration of the preceding vowel is known to vary as a function of the (phonological or phonetic) voicing feature of the following consonant. This study raises a question against this general belief. A spectrographic experiment using 14 Korean obstruents (three sets of stops: /p, p', $p^h$/, /t, t', $t^h$/, /k, k', $k^h$/; one set of affricates: /c, c', $c^h$/; one set of fricatives: /s, s'/) reveals that (1) phonetic voicing in the intervocalic lax consonants /p, t, k, c, s/ has nothing to do with the duration of the preceding vowel; (2) vowel length is significantly shorter before tense consonants than before their lax cognates while tense consonants are significantly longer than their lax cognates. Importantly, Korean obstruents are all phonologically voiceless. Therefore, the voicing feature is rejected as the cause of preconsonantal vowel shortening in Korean both phonetically and phonologically. It is suggested that the temporal phenomenon is basically a kind of physiologically-motivated coarticulation though it is restricted by the phonology of a given language. To meet this assumption, the feature voicing should be replaced with the feature tenseness as the cause, which will enable us to explain the temporal phenomenon on the same basis irrespective of language.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.10
• /
• pp.2632-2649
• /
• 2012

Most approaches to human action recognition is limited due to the use of simple action datasets under controlled environments or focus on excessively localized features without sufficiently exploring the spatio-temporal information. This paper proposed a framework for recognizing realistic human actions. Specifically, a new action representation is proposed based on computing a rich set of descriptors from keypoint trajectories. To obtain efficient and compact representations for actions, we develop a feature fusion method to combine spatial-temporal local motion descriptors by the movement of the camera which is detected by the distribution of spatio-temporal interest points in the clips. A new topic model called Markov Semantic Model is proposed for semantic feature selection which relies on the different kinds of dependencies between words produced by "syntactic " and "semantic" constraints. The informative features are selected collaboratively based on the different types of dependencies between words produced by short range and long range constraints. Building on the nonlinear SVMs, we validate this proposed hierarchical framework on several realistic action datasets.

• Proceedings of the KSPS conference
• /
• 2003.05a
• /
• pp.149-152
• /
• 2003

In this paper, we present a performance comparison of feature parameters and classifiers for speech/music discrimination. Experiments were carried out on six feature parameters and three classifiers. It turns out that three classifiers shows similar performance. The feature set that captures the temporal and spectral structure of the signal yields good performance, while the phone-based feature set shows relatively inferior performance.

• Journal of Korean Neurosurgical Society
• /
• v.57 no.4
• /
• pp.292-294
• /
• 2015

Vestibular schwannoma (VS) usually present the widening of internal auditory canal (IAC), and these bony changes are typically limited to IAC, not extend to temporal bone. Temporal bone invasion by VS is extremely rare. We report 51-year-old man who revealed temporal bone destruction beyond IAC by unilateral VS. The bony destruction extended anteriorly to the carotid canal and inferiorly to the jugular foramen. On histopathologic examination, the tumor showed typical benign schwannoma and did not show any unusual vascularity or malignant feature. Facial nerve was severely compressed and distorted by tumor, which unevenly eroded temporal bone in surgical field. Vestibular schwannoma with atypical invasion of temporal bone can be successfully treated with combined translabyrinthine and lateral suboccipiral approach without facial nerve dysfunction. Early detection and careful dissection of facial nerve with intraoperative monitoring should be considered during operation due to severe adhesion and distortion of facial nerve by tumor and eroded temporal bone.

• Journal of Korean Society for Geospatial Information Science
• /
• v.10 no.5 s.23
• /
• pp.17-28
• /
• 2002

Time is regarded as an essential feature of land information enabling to track historical landmarks of land uses, ownerships, and taxations based on cadastral maps. Object-oriented temporal modeling helps to simulate and imitate time-varying cadastral data in a chronological and persistent manner. The aim of study is to analyze the role of temporal cadastre tracing footprints of foregoing events in response to various needs and demands associated with historical information of cadastral transactions. In this paper, temporal cadastral object model (TCOM) is proposed to delineate object version history. As an evidence of a new approach and conceptual idea for the importance of temporal cadastre, a part of spatio-temporal processes is illustrated to explain major changes of cadastral map. The feasibility and application of the approach is confirmed by proof-of-concept of temporal cadastre in land information management.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.10
• /
• pp.1731-1737
• /
• 2016

In this paper, a method is proposed to recognize human actions as nonverbal expression; the proposed method is composed of two steps which are action representation and action recognition. First, MHI(Motion History Image) is used in the action representation step. This method includes segmentation based on depth information and generates spatio-temporal templates to describe actions. Second, CNN(Convolution Neural Network) which includes feature extraction and classification is employed in the action recognition step. It extracts convolution feature vectors and then uses a classifier to recognize actions. The recognition performance of the proposed method is demonstrated by comparing other action recognition methods in experimental results.

• Phonetics and Speech Sciences
• /
• v.1 no.4
• /
• pp.127-132
• /
• 2009

The feature vectors which are used in conventional speaker recognition (SR) systems may have many correlations between their neighbors. To improve the performance of the SR, many researchers adopted linear transformation method like principal component analysis (PCA). In general, the linear transformation of the feature vectors is based on concatenated form of the static features and their dynamic features. However, the linear transformation which based on both the static features and their dynamic features is more complex than that based on the static features alone due to the high order of the features. To overcome these problems, we propose an efficient method that applies linear transformation and temporal information of the features to reduce complexity and improve the performance in speaker verification (SV). The proposed method first performs a linear transformation by PCA coefficients. The delta parameters for temporal information are then obtained from the transformed features. The proposed method only requires 1/4 in the size of the covariance matrix compared with adding the static and their dynamic features for PCA coefficients. Also, the delta parameters are extracted from the linearly transformed features after the reduction of dimension in the static features. Compared with the PCA and conventional methods in terms of equal error rate (EER) in SV, the proposed method shows better performance while requiring less storage space and complexity.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.23-34
• /
• 2004

In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.3
• /
• pp.1614-1632
• /
• 2017

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1851-1863
• /
• 2015

– In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.