Seksan Mathulaprangsan

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This paper concerns the development of locality-preserving methods for object recognition. The major purpose is consideration of both descriptor-level locality and image-level locality throughout the recognition process. Two dual-layer locality-preserving methods are developed, in which locality-constrained linear coding (LLC) is used to represent an image.(More)
Single-channel source separation is an approach to decomposing a single-channel recording into its sources without understanding how the sources are mixed. This work develops a sparse regularized nonnegative matrix factorization scheme with spatial dispersion penalty (SpaSNMF). To preserve spatial locality structured information on the basis for sound(More)
Nowadays, sound event detection (SED) is a popular study in machine listening area. Detecting overlapping sound events, in which many sound events occur simultaneously, is challenging and interesting topic. Besides, non-negative matrix factorization (NMF) and its derived methods are suitably used to perform SED. This paper presents a survey of recent(More)
Semantic image segmentation is now an exciting area of research owing to its various useful applications in daily life. This paper introduces a hierarchical joint-guided network (HJGN) which is mainly composed of proposed hierarchical joint learning convolutional networks (HJLCNs) and proposed joint-guided and making networks (JGMNs). HJLCNs exhibit high(More)
This research proposes a novel Bayesian sparse representation (BSR) method along with extracting facial parameters of SIFT to create sparse dictionaries, which are invariant to rotation, scale, and shift. By using K-means and information theory, a new dictionary called extended dictionary is developed. Compared with conventional orthogonal matching pursuit(More)
This work proposes a novel method of matrix factorization on the complex domain to obtain both intuitive features and high recognition results in a face recognition system. The real data matrix is transformed into a complex number based on the Euler representation of complex numbers. Base complex matrix factorization (CMF) is developed and two extensions(More)
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