• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.21-32
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• 2015

Since spatio-temporal local features for video representation have become an important issue of modeless bottom-up approaches in action recognition, various methods for feature extraction and description have been proposed in many papers. In particular, BoF(bag of features) has been promised coherent recognition results. The most important part for BoF is how to represent dynamic information of actions in videos. Most of existing BoF methods consider the video as a spatio-temporal volume and describe neighboring 3D interest points as complex volumetric patches. To simplify these complex 3D methods, this paper proposes a novel method that builds BoF representation as a way to learn 2D interest points directly from video data. The basic idea of proposed method is to gather feature points not only from 2D xy spatial planes of traditional frames, but from the 2D time axis called spatio-temporal frame as well. Such spatial-temporal features are able to capture dynamic information from the action videos and are well-suited to recognize human actions without need of 3D extensions for the feature descriptors. The spatio-temporal BoF approach using SIFT and SURF feature descriptors obtains good recognition rates on a well-known actions recognition dataset. Compared with more sophisticated scheme of 3D based HoG/HoF descriptors, proposed method is easier to compute and simpler to understand.

• MALSORI
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• no.27_28
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• pp.27-56
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• 1994

Processes involving the change of vowel height are natural enough to be found in many languages. It is essential to have a better feature specification for vowel height to grasp these processes properly, Standard Phonology adopts the binary feature system, and vowel height is represented by the two features, i.e., [\pm high] and [\pm low]. This has its own merits. But it is defective because it is misleading when we count the number of features used in a rule to compare the naturalness of rules. This feature system also cannot represent more than three degrees of height, We wi31 discard the binary features for vowel height. We consider to adopt the multivalued feature [n high] for the property of height. However, this feature cannot avoid the arbitrariness resulting from the number values denoting vowel height. It is not easy to expect whether the number in question is the largest or not It also is impossible to decide whether a larger number denotes a higher vowel or a lower vowel. Furthermore this feature specification requires an ad hoc condition such as n > 3 or n \geq 2, whenever we want to refer to a natural class including more than one degree of height The altelnative might be Particle Phonology, or Dependency Phonology. These might be apt for multivalued vowel height systems, as their supporters argue. However, the feature specification of Particle Phonology will be discarded because it does not observe strictly the assumption that the number of the particle a is decisive in representing the height. One a in a representation can denote variant degrees of height such as [e], [I], [a], [a ] and [e ]. This also means that we cannot represent natural classes in terms of the number of the particle a, Dependency Phonology also has problems in specifying a degree of vowel height by the dependency relations between the elements. There is no unique element to represent vowel height since every property has to be defined in terms of the dependency relations between two or more elements, As a result it is difficult to formulate a rule for vowel height change, especially when the phenomenon involves a chain of vowel shifts. Therefore, we suggest a new feature specification for vowel height (see Chapter 3). This specification resorts to a single feature H and a few >'s which refer exclusively to the degree of the tongue height when a vowel is pronounced. It can cope with more than three degrees of height because it is fundamentally a multivalued scalar feature. This feature also obviates the ad hoc condition for a natural class while the [n high] type of multivalued feature suffers from it. Also this feature specification conforms to our expection that the notation should become simpler as the generality of the class increases, in that the fewer angled brackets are used, the more vowels are included, Incidentally, it has also to be noted that, by adopting a single feature for vowel height, it is possible to formulate a simpler version of rules involving the changes of vowel height especially when they involve vowel shifts found in many languages.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.435-448
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• 2008

3D posture recognition is a solution to overcome the limitation of 2D posture recognition. There are many researches carried out for 3D posture recognition using 3D data. The 3D data consist of massive surface points which are rich of information. However, it is difficult to extract the important features for posture recognition purpose. Meanwhile, it also consumes lots of processing time. In this paper, we introduced a dimension reduction method that transform 3D surface points of an object to 2D data representation in order to overcome the issues of feature extraction and time complexity of 3D posture recognition. For a better feature extraction and matching process, a cylindrical boundary is introduced in meshless parameterization, its offer a fast processing speed of dimension reduction process and the output result is applicable for recognition purpose. The proposed approach is applied to hand and human posture recognition in order to verify the efficiency of the feature extraction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.12-20
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• 2011

In this paper, we propose an efficient object detection and classification algorithm for video surveillance applications. Previous researches mainly concentrated either on object detection or classification using particular type of feature e.g., Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) etc. In this paper we propose an algorithm that mutually performs object detection and classification. We combinedly use heterogeneous types of features such as texture and color distribution from local patches to increase object detection and classification rates. We perform object detection using spatial clustering on interest points, and use Bag of Words model and Naive Bayes classifier respectively for image representation and classification. Experimental results show that our combined feature is better than the individual local descriptor in object classification rate.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.321-328
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• 2004

Since National Geographic Information System was started, topographic maps have been made with computer aided editing of digital map, instead of etching map-size negative film. topographic mapping system's necessity is growing more and more, because digital map has changed into Ver.2.0 which include attributes of feature. On the basis of the previous study for analyzing correlation between the digital map feature code and the 1/5,000 topographic map specifications and trying to develop fundamental modules which will play a core role in topographic mapping system, in this study, we apply some 1/5,000 digital maps Ver.2.0 to topographic mapping system have implemented and try to analyze the result.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.370-384
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• 2016

Computer-aided materials design requires new modeling approaches to characterize and represent fine-grained geometric structures and material compositions at multiple scales. Recently, a dual-Rep approach was developed to model materials microstructures based on a new basis function, called surfacelet. As a combination of implicit surface and wavelets, surfacelets can efficiently identify and represent planar, cylindrical, and ellipsoidal geometries in material microstructures and describe the distribution of compositions and properties. In this paper, these primitive surfacelets are extended and composite surfacelets are proposed to model more complex geometries. Composite surfacelets are constructed by Boolean operations on the primitives. The surfacelet transform is applied to match geometric features in three-dimensional images. The composition of the material near the identified features can then be modeled. A cubic surfacelet and a v-joint surfacelet are developed to demonstrate the reverse engineering process of retrieving material compositions from material images.

• ETRI Journal
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• v.36 no.3
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• pp.514-517
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• 2014

In this paper, alternative dynamic features for speech recognition are proposed. The goal of this work is to improve speech recognition accuracy by deriving the representation of distinctive dynamic characteristics from a speech spectrum. This work was inspired by two temporal dynamics of a speech signal. One is the highly non-stationary nature of speech, and the other is the inter-frame change of a speech spectrum. We adopt the use of a sub-frame spectrum analyzer to capture very rapid spectral changes within a speech analysis frame. In addition, we attempt to measure spectral fluctuations of a more complex manner as opposed to traditional dynamic features such as delta or double-delta. To evaluate the proposed features, speech recognition tests over smartphone environments were conducted. The experimental results show that the feature streams simply combined with the proposed features are effective for an improvement in the recognition accuracy of a hidden Markov model-based speech recognizer.

• ETRI Journal
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• v.40 no.4
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• pp.499-510
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• 2018

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.305-323
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• 2010

This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, which allows to reduce much computation requirements. The feature matching experiments demonstrate that PPD achieves favorable performance close to that of SIFT and faster building and matching. We also present results showing that the use of PPD descriptors in a mobile image retrieval application results in a comparable performance to SIFT.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.875-883
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• 2010

In this paper, we present a secure biometric hashing scheme for face recognition by random fusion of global and local features. The Fourier-Mellin transform and Radon transform are adopted respectively to form specialized representation of global and local features, due to their invariance to geometric operations. The final biometric hash is securely generated by random weighting sum of both feature sets. A fourfold key is involved in our algorithm to ensure the security and privacy of biometric templates. The proposed biometric hash can be revocable and replaced by using a new key. Moreover, the attacker cannot obtain any information about the original biometric template without knowing the secret key. The experimental results confirm that our scheme has a satisfactory accuracy performance in terms of EER.