778557
Speechalator: Two-Way Speech-to-Speech Translation in Your Hand
This demonstration involves two-way automatic speech-to-speech translation on a consumer off-the-shelf PDA. This work was done as part of the DARPA-funded Babylon project, investigating better speech-to-speech translation systems for communication in the field. The development of the Speechalator software-based translation system required addressing a number of hard issues, including a new language for the team (Egyptian Arabic), close integration on a small device, computational efficiency on a limited platform, and scalable coverage for the domain.
17930808
Automatic detection of signs with affine transformation
In this paper, we propose an approach for detecting signs from natural scenes. The approach efficiently embeds multiresolution, adaptive search, and affine rectification algorithms in a hierarchical framework, with different emphases at each layer. We combine in multi-resolution and multi-scale edge detection techniques to effectively detect text in different sizes. By using the cites from text inside the image, we introduce affine rectification transformation to recover deformation of the text region caused by air inappropriate camera view angle. This procedure can significantly improve text detection rate and OCR (Optical Character Recognition) accuracy. Experimental results have demonstrated feasibility of the proposed algorithms. We have applied the proposed approach to a Chinese sign translation system, which can automatically detect Chinese text input from a camera, recognize the text, and translate the recognized text into English or voice stream.
1586306
A PDA-based sign translator
We propose an effective approach for a PDA-based sign system and present the sign translator. Its main functions include three parts: detection, recognition and translation. Automatic detection and recognition of text in natural scenes is a prerequisite for the automatic sign translator. In order to make the system robust for text detection in various natural scenes, the detection approach efficiently embeds multi-resolution, adaptive search in a hierarchical framework with different emphases at each layer. We also introduce an intensity-based OCR method to recognize characters in various fonts and lighting conditions, where we employ the Gabor transform to obtain local features, and LDA for selection and classification of features. The recognition rate is 92.4% for the testing set obtained from the natural sign. A sign is different from the normal used sentence. It is brief with a lot of abbreviations and place nouns. We only briefly introduce a rule-based place name translation. We have integrated all these functions in a PDA, which can capture sign images, auto segment and recognize the Chinese sign, and translate it into English.
6109448
Automatic detection and recognition of signs from natural scenes
In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.
7832131
A robust approach for recognition of text embedded in natural scenes
In this paper, we propose a robust approach for recognition of text embedded in natural scenes. Instead of using binary information as most other OCR systems do, we extract features from intensity of an image directly. We utilize a local intensity normalization method to effectively handle lighting variations. We then employ Gabor transform to obtain local features, and use the linear discriminant analysis (LDA) for selection and classification of features. The proposed method has been applied to a Chinese sign recognition task. The system can recognize a vocabulary of 3755 level I Chinese characters in the Chinese national standard character set GB2312-80 with various print fonts. We tested the system on 1630 test characters in sign images captured from the natural scenes, and the recognition accuracy was 92.46%. We have integrated the system into our automatic Chinese sign translation system.
6635923
Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance
In this paper, we identify a new task for studying the outlying degree (OD) of high-dimensional data, i.e. finding the subspaces (subsets of features) in which the given points are outliers, which are called their outlying subspaces. Since the state-of-the-art outlier detection techniques fail to handle this new problem, we propose a novel detection algorithm, called High-Dimension Outlying subspace Detection (HighDOD), to detect the outlying subspaces of high-dimensional data efficiently. The intuitive idea of HighDOD is that we measure the OD of the point using the sum of distances between this point and itsknearest neighbors. Two heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive top–down, bottom–up and random search methods, and the existing outlier detection methods cannot fulfill this new task effectively.
5348752
Toward Protein Structure Analysis with Self-Organizing Maps
Establishing structure-function relationships on the proteomic scale is a unique challenge faced by bioinformatics and molecular biosciences. Large protein families represent natural libraries of analogues of a given catalytic or protein function, thus making them ideal targets for the investigation of structure-function relationships in proteins. To this end, we have developed a new technique for analyzing large amounts of detailed molecular structure information focusing on the functional centers of homologous proteins. Our approach uses unsupervised machine learning, in particular, self-organizing maps. The information captured by a self-organizing map and stored in its reference models highlights the essential structure of the proteins under investigation and can be effectively used to study detailed structural differences and similarities among homologous proteins. Our preliminary results obtained with a prototype based on these techniques demonstrate that we can classify proteins and identify common and unique structures within a family and, more importantly, identify common and unique structural features of different conformations of the same protein. The approach developed here outperforms many of today’s structure analysis tools. These tools are usually either limited by the number of proteins they can process at the same time or they are limited by the structural resolution they can accommodate, that is, many of the structural analysis tools that can handle multiple proteins at the same time limit themselves to secondary structure analysis and therefore miss fine structural nuances within proteins.
13312738
Autonomous Objects and Bottom-Up Composition in ZOO Applied to a Case Study of Biological Reactivity
As part of our work on the formal analysis of object-oriented models, we turn to systems where many autonomous individuals interact to give rise to complex collective behaviour. We adapt our ZOO [1,2] structuring and apply it to a case study based on a published model of part of the immune system [3]. The formalisation calls for a bottom-up solution with no central control over individual units, and includes an approach to represent feedback channelsenabling broadcast communication between individuals and across levels.
43913104
The transient performance analysis of voice/data integrated networks
Quasi-birth-death (QBD) processes are used as performance models for voice/data integration schemes. It is assumed that voice and data packets of different bandwidths require different numbers of servers for service. Voice packets are lost when there are no available servers; data packets are queued in a buffer when there are not enough servers. A new approach is proposed to find the transient solutions of the QBD processes. The method is applied to a QBD model of a voice/data integration scheme to find time-dependent performance measures such as the expected number of data or voice packets in the system and the voice packet blocking probability.<<ETX>>
3723162
Topic-Level Random Walk through Probabilistic Model
In this paper, we study the problem of topic-level random walk, which concerns the random walk at the topic level. Previously, several related works such as topic sensitive page rank have been conducted. However, topics in these methods were predefined, which makes the methods inapplicable to different domains. In this paper, we propose a four-step approach for topic-level random walk. We employ a probabilistic topic model to automatically extract topics from documents. Then we perform the random walk at the topic level. We also propose an approach to model topics of the query and then combine the random walk ranking score with the relevance score based on the modeling results. Experimental results on a real-world data set show that our proposed approach can significantly outperform the baseline methods of using language model and that of using traditional PageRank.