Nec Corporation

Tdk Corporation

Nikon Corporation

Sumitomo Metal Mining Company, Ltd.

Renesas Electronics Corporation

Leica Microsystems Cms Gmbh

International Business Machines Corporation

Drvision Technologies LLC

Epcos Ag

Leica Microsystems Inc.

Hideki Sasaki

Takashi Harada

Shih-Jong James Lee

Toshihide Kuriyama

Yasumasa Kan

Hideya Inoue

Yutaka Iwasaki

Hiroyuki Yamada

Tomohide Hamada

Hiroyuki Mitsui

Toru Iwaoka

Michiko Noborisaka

Masayuki Hatori

Tatsuaki Tsukuda

Hiroshi Suzuki

Eiji Hankui

Kazunori Arimitsu

Masaki Imamura

Muneo Fukaishi

Hiroyuki Takenoshita

Go Ohara

Takashi Horai

Takahiro Yaguchi

Shigeyo Miyamori

Chi-Chou Huang

Seiji Koyama

Masatoshi Uchida

Naoyuki Nagashima

Tomohisa Nakamura

Kiyoshi Asao

Satoshi Tanaka

Katsumi Kikuchi

Hiroyuki Tanaka

Makoto Iwasaki

Kazushi Tanaka

Takuo Funaya

Yoshitomo Ozaki

Shigeo Kurose

Tadashi Shimizu

Osamu Inoue

Tatsuya Higaki

Toshiharu Okamoto

Hideaki Kobayashi

Kei Ito

Yuma Komaya

Yasufumi Takasugi

Toru Taura

Shunji Sato

Takayoshi Kuwajima

Takashi Yoshinaga

Fumitaka Baba

Takahide Kurahashi

Masafumi Mimura

Kenichi Kitamura

Daisuke Ohshima

Tsunehiko Ikarashi

Seiichi Endo

Hiroki Shibuya

Masaharu Imazato

Kazuhiro Kuroda

Hirofumi Shouji

Shinji Nishizono

Yutaka Hatakeyama

Masaru Kumagai

Masahide Kohno

Manabu Enomoto

Yoshihiro Masumura

Anneliese Drespling

Seishi Eya

Joern Schliewe

Akira Wakui

Yasushi Ishikawa

Tomonao Suto

Hitoshi Maro

Keiichi Morimoto

Joachim Nassal

Yuuki Fujimura

Yuji Muramatsu

Toshio Kaku

Hidenori Muramatsu

Shuichi Oe

Takahiko Kikukawa

Yuki Takahashi

Masahiro Dobashi

Hiroko Kubota

Yuji Kokumai

Kazuhiro Yano

In a database, product image data is accumulated. A search portion acquires product image data having the image characteristics information that is the same as or similar to the image characteristics information that indicates the characteristics of the image of input image data from the database for the input image data. A search server outputs information on another product that is different from the product corresponding to the product image data together with the product image data acquired by the search portion.

Search supporting system, search supporting method and search supporting program

A computerized model compatibility regulation method for imaging applications first performs a target domain B application by computing means using at least one image X and target domain B image analytics to generate a target domain B application output for X. The method then applies a reference domain A application by computing means to generate reference domain A application output for X. The method further performs a compatibility assessment to generate at least one compatibility result for X. In addition, the method checks the compatibility result for X and if the check output is incompatible, the method performs online correction to generate a corrected application output for X.

Image and data analystics model compatibility regulation methods

A computerized prediction guided learning method for classification of sequential data performs a prediction learning and a prediction guided learning by a computer program of a computerized machine learning tool. The prediction learning uses an input data sequence to generate an initial classifier. The prediction guided learning may be a semantic learning, an update learning, or an update and semantic learning. The prediction guided semantic learning uses the input data sequence, the initial classifier and semantic label data to generate an output classifier and a semantic classification. The prediction guided update learning uses the input data sequence, the initial classifier and label data to generate an output classifier and a data classification. The prediction guided update and semantic learning uses the input data sequence, the initial classifier and semantic and label data to generate an output classifier, a semantic classification and a data classification.

Prediction guided sequential data learning method

A computerized method of deep model matching for image transformation includes inputting pilot data and pre-trained deep model library into computer memories; performing a model matching scoring using the pilot data and the pre-trained deep model library to generate model matching score; and performing a model matching decision using the model matching score to generate a model matching decision output. Additional pilot data may be used to perform the model matching scoring and the model matching decision iteratively to obtain improved model matching decision output. Alternatively, the pre-trained deep model library may be pre-trained deep adversarial model library in the method.

Deep model matching methods for image transformation

A computerized domain matching image conversion method for transportable imaging applications first performs a target domain A to source domain B matching converter training by computing means using domain B training images and at least one domain A image to generate an A to B domain matching converter. The method then applies the A to B domain matching converter to a domain A application image to generate its domain B matched application image. The method further applies a domain B imaging application analytics to the domain B matched application image to generate an imaging application output for the domain A application image.

Domain matching methods for transportable imaging applications

A computerized method of artifact regulation in deep model training for image transformation first performs one cycle of deep model training by computing means using a training data, a validation data, a similarity loss function, an artifact regulation loss function and a weight of loss functions to generate similarity loss and artifact regulation loss and a deep model. The method then performs a training evaluation using the similarity loss and the artifact regulation loss thus obtained to generate a training readiness output. Then, depending upon the training readiness output, the method may be terminated if certain termination criteria are met, or may perform another cycle of deep model training and training evaluation, with or without updating the weight, until the termination criteria are met. Alternatively, the deep model training in the method may be a deep adversarial model training or a bi-directional deep adversarial training.

Artifact regulation methods in deep model training for image transformation

Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Optimal and efficient machine learning method for deep semantic segmentation

Provided is a production method for refining iron oxide (hematite), which has such a low sulfur content as to be used as a raw material for ironmaking from a leach residue containing iron oxide, the leach residue being produced by a high pressure acid leach (HPAL) process and being a raw material that can be cheaply and stably procured. In the method of producing (high purity) hematite for ironmaking by a process of adding an oxidant and sulfuric acid to nickel oxide ore and then leaching nickel, a leach residue obtained after the leaching of nickel is heated to 600&#xb0; C. or more, and preferably 800&#xb0; C. or more and 1400&#xb0; C. or less.

Production method for hematite for iron production

A loop antennaincludes: a first electrode terminal; a second electrode terminalarranged to make a pair with the first electrode terminal; and a loop-shaped memberwhich has one end connected to the first electrode terminaland the other end connected to the second electrode terminal, is wound a plurality of times, and is made of a conductive material. The first electrode terminaland the second electrode terminalare arranged so as to make a pair with respect to a center lineof the loop-shaped member. Further, the loop-shaped memberincludes a first loop-shaped member, a second loop-shaped member, and an intersection part. The intersection partis arranged on the center linein a plan view, and the loop-shaped memberis continuously connected and formed to be symmetrical with respect to the center line

Loop antenna and communication control device

To achieve a reduction of noise in a wireless system of an electronic device for communication. The electronic device for communication includes: a bottom lid; a loop antenna that forms an electromagnetic field; a communication circuit coupled to the loop antenna; a battery pack that is a metallic part; a magnetic sheet arranged between the loop antenna and the metallic part, the magnetic sheet including a protruding portion that protrudes outside an outer peripheral portion of the loop antenna; a wiring substrate arranged over the battery pack and having an IC and the like mounted thereon; and a lid with a display arranged over the wiring substrate, in which, with the outer peripheral portion of the loop antenna as a base point, a protruding amount from the base point of the protruding portion of the magnetic sheet is twice or more a wiring width of the loop antenna.

Electronic device for communication

A method of producing (high-purity) hematite for ironmaking includes adding an oxidant and sulfuric acid to nickel oxide ore and then leaching nickel. The method further includes forming a neutralized residue having a sulfur grade exceeding 1.0% by weight by adding a neutralizing agent to leach slurry and being subjected to solid-liquid separation. The neutralizing agent functions to form plaster by reaction with a free sulfuric acid of surplus acid contained in the leach slurry. The leach slurry is a mixture of a leachate and a leach residue obtained after leaching the nickel. The method then includes heating the neutralized residue at a heating temperature of 600&#xb0; C. or more and 1400&#xb0; C. or less to form hematite having a sulfur grade of 1.0% or less by weight.

Method for producing hematite for ironmaking

Provided is a method for producing hematite for ironmaking, capable of using a conventional Ca-based neutralizing agent and a base rock-derived neutralizing agent other than the Ca-based neutralizing agent. The method is performed by a process of adding a mineral acid and an oxidizing agent to an ore containing iron and a valuable metal and then leaching the valuable metal under high temperature and pressure, and includes (1) a high-pressure acid leaching step, (2) a preliminary neutralization step, (3) a first solid-liquid separation step, (4) a neutralization step, (5) a second neutralization step, (6) a third solid-liquid separation step, (7) a step of adding part of the Fe-enriched slurry as a seed crystal in the neutralization step (4), and (8) a second solid-liquid separation step.

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Tokyo, Japan

Hideki Sasaki