Toyota

About: Toyota is a company organization based out in Safenwil, Switzerland. It is known for research contribution in the topics: Internal combustion engine & Exhaust gas. The organization has 40032 authors who have published 55003 publications receiving 735317 citations. The organization is also known as: Toyota Motor Corporation & Toyota Jidosha KK.

Biorefinery

Abstract: The biorefinery produces fuels, solvents, plastics and food for human beings. In some countries, these biorefinery products are made from waste biomass. The main processes in the biorefinery involve ethanol fermentation and lactic acid fermentation. For the biorefinery, many hybrid technologies were developed from different fields, such as bioengineering, polymer chemistry, food science and agriculture.

An intake device of an internal combustion engine

Abstract: An engine having an intake port which comprises a straight port and a helical-shaped port which are separated by a separating wall. A control valve is arranged in the inlet portion of the straight port and opens when the engine is operating under a heavy load. A projection is formed on the side wall of the separating wall, which defines the straight port and is located downstream of the control valve. A bypass passage connected to the helix terminating portion of the helical portion of the helical-shaped port is formed in the separating wall. The projection has an arc-shaped cross-sectional tip face which is directed towards the upstream of the intake port. The inlet opening of the bypass passage is formed on the tip face of the projection.

Building models of animals from video

Abstract: This paper argues that tracking, object detection, and model building are all similar activities. We describe a fully automatic system that builds 2D articulated models known as pictorial structures from videos of animals. The learned model can be used to detect the animal in the original video - in this sense, the system can be viewed as a generalized tracker (one that is capable of modeling objects while tracking them). The learned model can be matched to a visual library; here, the system can be viewed as a video recognition algorithm. The learned model can also be used to detect the animal in novel images - in this case, the system can be seen as a method for learning models for object recognition. We find that we can significantly improve the pictorial structures by augmenting them with a discriminative texture model learned from a texture library. We develop a novel texture descriptor that outperforms the state-of-the-art for animal textures. We demonstrate the entire system on real video sequences of three different animals. We show that we can automatically track and identify the given animal. We use the learned models to recognize animals from two data sets; images taken by professional photographers from the Corel collection, and assorted images from the Web returned by Google. We demonstrate quite good performance on both data sets. Comparing our results with simple baselines, we show that, for the Google set, we can detect, localize, and recover part articulations from a collection demonstrably hard for object recognition