We have developed a CT guided needle puncture robot (Zerobot) to assist in interventional radiology surgery. Currently, Zerobot is operated remotely, and the next goal is to perform automatic needle puncture surgery. There is a challenge that automatic detection of puncture needle from CT images for first step of automatic puncture surgery. Because there is the case that the form of puncture needle is curved, it is necessary to detect the needle from CT image instead of estimating the needle position from arm of Zerobot. First, the method detects ROI with ResNet. Next, difference of the CT value is calculated for each pixel in the ROI, and a linear approximation is performed for to detect the needle shape. Images from animal experiments were used to evaluate the learner and image processing. We confirmed that the proposed method can detect needles in a single image and in multiple images.

This article reconsiders the task of MRD-based word sense disambiguation, in extending the basic Lesk algorithm to investigate the impact on WSD performance of different tokenization schemes and methods of definition extension. In experimentation over the Hinoki Sensebank and the Japanese Senseval-2 dictionary task, we demonstrate that sense-sensitive definition extension over hyponyms, hypernyms, and synonyms, combined with definition extension and word tokenization leads to WSD accuracy above both unsupervised and supervised baselines. In doing so, we demonstrate the utility of ontology induction and establish new opportunities for the development of baseline unsupervised WSD methods.

In this paper we describe the current state of a new Japanese lexical resource: the Hinoki treebank. The treebank is built from dictionary definition sentences, and uses an HPSG based Japanese grammar to encode both syntactic and semantic information. It is combined with an ontology based on the definition sentences to give a detailed sense level description of the most familiar 28,000 words of Japanese.

In this paper we present a quantitative and qualitative analysis of annotation in the Hinoki treebank of Japanese, and investigate a method of speeding annotation by using part-of-speech tags. The Hinoki treebank is a Redwoods-style treebank of Japanese dictionary definition sentences. 5,000 sentences are annotated by three different annotators and the agreement evaluated. An average agreement of 65.4% was found using strict agreement, and 83.5% using labeled precision. Exploiting POS tags allowed the annotators to choose the best parse with 19.5% fewer decisions.

We propose a method to alleviate the problem of referential granularity for Japanese zero pronoun resolution. We use dictionary definition sentences to extract ‘representative’ arguments of predicative definition words; e.g. ‘arrest’ is likely to take police as the subject and criminal as its object. These representative arguments are far more informative than ‘person’ that is provided by other valency dictionaries. They are auto-extracted using both Shallow parsing and Deep parsing for greater quality and quantity. Initial results are highly promising, obtaining more specific information about selectional preferences. An architecture of zero pronoun resolution using these representative arguments is described.

We present a method for compositionally translating Japanese NN compounds into English, using a word-level transfer dictionary and target language monolingual corpus. The method interpolates over fully-specified and partial translation data, based on corpus evidence. In evaluation, we demonstrate that interpolation over the two data types is superior to using either one, and show that our method performs at an F-score of 0.68 over translation-aligned inputs and 0.66 over a random sample of 500 NN compounds.

We propose a real-time algorithm for path planning in a dynamically changing 3-D environment where the motion of the obstacles cannot be predicted. The method represents everything in the environment (an arbitrarily shaped robot and static/dynamic obstacles) with an octree that makes no distinction of the movability of objects. A collision free path for a robot with arbitrary motion (translation and rotation) in a dynamic 3-D environment is efficiently sought by using the real-time A* algorithm and the potential field generated from each black cell of the octree. Parallel computation is used to speed up the generation of the potential field. Finally, the experimental results demonstrate the efficiency of the proposed path planning method for a dynamically-changing 3-D environment with arbitrary motion.

Proposes an efficient and simple method for finding a collision-free path and orientation for a rigid robot in a dynamic observable 3-D environment for unmanned aerial vehicles (UAVs). The method uses an octree for representing every object (robot and static/dynamic obstacles) in the environment without any distinction of the movability of objects; therefore, all objects are dealt with in the same way. The path of a robot from its starting position to the given goal with arbitrary motion (i.e., translation and rotation) in a 3-D environment is efficiently searched for in successive adjoining regions (octree white cells) by using the potential field generated from each black cell of the octree. The algorithm is simple, so it can easily be accelerated by using parallelization techniques. Experimental results obtained under several conditions, such as a point shaped robot and arbitrarily shaped robots in static/dynamic environments, are reported.