• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1424-1435
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• 2000

A bit-rate controller that can achieve a constant bit rate when coding object-based video sequences is an important part to achieve an adaptation to bit-rate constraints, desired video quality, distribution of bits among objects, relationship between texture and shape coding, and determination of frame skip or not. Therefore we design content-based bit rate controller which will be used for relevant bit-rate control. The implementation is an extension of MPEG-4 rate control algorithm which employs a quadratic rate-quantizer model. The importance of different objects in a video is analyzed and segmented into a number of VOPs which are adaptively bit-allocated using the object-based modelling. Some test sequences are observed by a number of non-experts and interests in each object are analysed. The initial total target bit-rate for all objects is obtained by using the proposed technique. Then the total target bits are jointly analyzed for preventing from overflow or underflow of the buffer fullness. The target bits are distributed to each object in view of its importance, not only of statistical analysis such as motion vector magnitude, size of object shape, and coding distortion of previous frame. The scheme is compared with the rate controller adopted by the MPEG-4 VM8 video coder by representing their statistics and performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.96-105
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• 1998

The aim of this paper is to propose the method of WDHMM(Weighted DHMM), using the MLP-VQ for the improvement of speaker-independent connect-digit recognition system. MLP neural-network output distribution shows a probability distribution that presents the degree of similarity between each pattern by the non-linear mapping among the input patterns and learning patterns. MLP-VQ is proposed in this paper. It generates codewords by using the output node index which can reach the highest level within MLP neural-network output distribution. Different from the old VQ, the true characteristics of this new MLP-VQ lie in that the degree of similarity between present input patterns and each learned class pattern could be reflected for the recognition model. WDHMM is also proposed. It can use the MLP neural-network output distribution as the way of weighing the symbol generation probability of DHMMs. This newly-suggested method could shorten the time of HMM parameter estimation and recognition. The reason is that it is not necessary to regard symbol generation probability as multi-dimensional normal distribution, as opposed to the old SCHMM. This could also improve the recognition ability by 14.7% higher than DHMM, owing to the increase of small caculation amount. Because it can reflect phone class relations to the recognition model. The result of my research shows that speaker-independent connected-digit recognition, using MLP-VQ and WDHMM, is 84.22%.