Screen Content Image Segmentation Using Sparse-Smooth Decomposition

Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.Comment: Asilomar Conference on Signals, Systems and Computers, IEEE, 2015, (to Appear

Penyelenggaraan struktur penahan cerun rock shed: langkah mitigasi runtuhan tanah di Simpang Pulai - Blue Valley, Perak

Industri pembinaan merupakan industri yang sangat mencabar bukan sahaja di Malaysia malah di seluruh dunia yang merangkumi skop 3D dirty, difficult and dangerous. Industri ini juga meruapakan antara penyumbang terbesar KDNK iaitu sebanyak 7.4 peratus pada tahun 2016, walaupun industri ini antara penyumbang terbesar dari aspek keselamatan iaitu kemalangan (CIDB, 2017). Justeru itu, pihak yang bertanggungjawab seharusnya memandang serius mengenai masalah-masalah yang dihadapi supaya industri ini mampu bersaing di peringkat antarabangsa

Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

Semantics-Based Content Extraction in Typewritten Historical Documents

This paper presents a flexible approach to extracting content from scanned historical documents using semantic information. The final electronic document is the result of a "digital historical document lifecycle" process, where the expert knowledge of the historian/archivist user is incorporated at different stages. Results show that such a conversion strategy aided by (expert) user-specified semantic information and which enables the processing of individual parts of the document in a specialised way, produces superior (in a variety of significant ways) results than document analysis and understanding techniques devised for contemporary documents

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3