Denso Corporation

Nara Institute of Science and Technology

Mikio Shimizu

Takashi Bando

Naoki Fukaya

Shin Ishii

Tomohiro Shibata

Naoki Kawasaki

Shunsuke Suzuki

Hiroshi Mizuno

Yusuke Tanaka

Kenji Muto

Kojiro Tateishi

Mitsuru Ambai

A relatedness determination device includes: a feature vector acquisition portion that acquires a binarized feature vector; a basis vector acquisition portion that acquires a plurality of basis vectors obtained by decomposing a real vector into a linear sum of the basis vectors, which have a plurality of elements including only binary or ternary discrete values; and a vector operation portion that sequentially performs inner product calculation between the binarized feature vector and each of the basis vectors to determine relatedness between the real vector and the binarized feature vector.

Relatedness determination device, non-transitory tangible computer-readable medium for the same, and relatedness determination method

In a location estimation device, a change detection unit detects a point of change in the width of a roadside structure as a width change point. A width correction unit calculates a width deviation between a first relative location of the roadside structure on a map data segment and a second relative location of the roadside structure obtained based on shape information of the roadside structure in the width direction of a vehicle. The width correction unit corrects, as a function of the width deviation and a positional accuracy of the vehicle in the travelling direction, an estimated location of the vehicle in the width direction when it is determined that there is the point of change in the width of the roadside structure in the travelling direction of the vehicle.

Location estimation device

A face model providing portion provides an stored average face model to an estimation portion estimating an affine parameter for obtaining a head pose. An individual face model learning portion obtains a result of tracking feature points by the estimation portion and learns an individual face model. The individual face model learning portion terminates the learning when a free energy of the individual face model is over a free energy of the average face model, and switches a face model provided to the estimation portion from the average face model to the individual face model. While learning the individual face mode, an observation matrix is factorized using a reliability matrix showing reliability of each observation value forming the observation matrix with emphasis on the feature point having higher reliability.

Three dimensional shape reconstitution device and estimation device

An estimation device estimates a hidden state of an estimation subject from an observable state in a manner of a time series. The observable state is observed from the hidden state of the estimation subject under a procedure that has a hierarchical structure, which includes the hidden state of the estimation subject, the observable state, and an intermediate hidden state therebetween. The estimation device includes an estimation subject hidden state predicting means, an intermediate hidden state predicting means based on the state transition structure of the hidden state of the estimation subject, an intermediate hidden state likelihood observing means, an intermediate hidden state estimating means, an estimation subject hidden state likelihood observing means, estimation subject hidden state estimating means, an intermediate hidden state predicting means based on the state transition structure of the intermediate hidden state, and the mixing means.

Estimation device

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Kariya, Japan

Mikio Shimizu