With the continuous expansion and deepening of the human knowledge system, various compound words have been created to express new concepts. Since the combined words cannot be recorded in the thesaurus in time, which lead to the word segmentation system cannot recognize them. Hence they are generally recognized as the unite of the smallest word (atomic word). So it is very urgent and meaningful to study the recognition method of compound words. In this paper, we propose a word structure based combinatorial word discovery algorithm, which makes full use of the following three word structure characteristics: word spacing, word frequency, and grammatical rules. According to the distance and position relationship between different words, the algorithm makes a comprehensive evaluation based on the rule judgment and the occurrence frequency of words. By experiments on different corpora, the results show that this method has higher accuracy.
How to choose effective fusion frames and how to obtain effective fusion coefficients are key problems in image fusion. A novel image fusion scheme is presented based on multiscale decomposition and directional filter banks (DFBs). First, contrast pyramid (CP) decomposition is used for each level of each original image. Then, DFBs are constructed for filter each image. Furthermore, a kind of evolution computation method—the immune clonal selection (ICS) algorithm—is introduced to optimize the fusion coefficients for better fusion products. By applying this technique to fusion of infrared thermal and visual light images, simulation results clearly demonstrate the superiority of this new approach. Fusion performance is evaluated through subjective inspection, as well as objective performance measurements. Experimental results show that the fusion scheme is effective and the fused images are more suitable for further human visual or machine perception.
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