We propose a new field of application for machine vision, a machine-vision-based cash-register system. We show that the overall system of color analysis for such an application should include the method of color distribution analysis which we propose, and that the analysis of shape and size is important. We present our test results and identify a few technical issues which may have to be considered for its practical utilization.

This paper proposes a method for reinforcing noun countability prediction, which plays a crucial role in demarcating correct determiners in machine translation and error detection. The proposed method reinforces countability prediction by introducing a novel heuristics called one countability per discourse. It claims that when a noun appears more than once in a discourse, all instances will share identical countability. The basic idea of the proposed method is that mispredictions can be corrected by efficiently using one countability per discourse heuristics. Experiments show that the proposed method successfully reinforces countability prediction and outperforms other methods used for comparison. In addition to its performance, it has two advantages over earlier methods: (i) it is applicable to any countability prediction method, and (ii) it requires no human intervention to reinforce countability prediction.

In this paper, we propose a statistical model for detecting article errors, which Japanese learners of English often make in English writing. It is based on the three head words--the verb head, the preposition, and the noun head. To overcome the data sparseness problem, we apply the backed-off estimate to it. Experiments show that its performance (F-measure=0.70) is better than that of other methods. Apart from the performance, it has two advantages: (i) Rules for detecting article errors are automatically generated as conditional probabilities once a corpus is given; (ii) Its recall and precision rates are adjustable.

This paper proposes a method for detecting determiner errors, which are highly frequent in learner English. To augment conventional methods, the proposed method exploits a strong tendency displayed by learners in determiner usage, i.e., mistakenly omitting determiners most of the time. Its basic idea is simple and applicable to almost any conventional method. This paper also proposes combining the method with countability prediction, which results in further improvement. Experiments show that the proposed method achieves an F-measure of 0.684 and significantly outperforms conventional methods.