Canon Kabushiki Kaisha

Katsuhiko Mori

Masakazu Matsugu

Yusuke Mitarai

Yuji Kaneda

Takashi Suzuki

Masami Kato

Katsumi Iijima

Kotaro Yano

Yoshinori Ito

Osamu Nomura

Takahisa Yamamoto

Mie Ishii

Kan Torii

Sunao Kurahashi

Shunta Tate

Masayoshi Sekine

Shigeki Okauchi

Motohiro Ishikawa

Takeo Sakimura

Tatsushi Katayama

Toshiaki Kondo

Hiroto Yoshii

Atsushi Nogami

Tadashi Hayashi

Yasuhiro Okuno

Masafumi Takimoto

Takayuki Saruta

Masao Yamanaka

Tetsuhiro Takanezawa

Hiroyuki Ohsawa

Hiroshi Sato

Makoto Sekita

Hirofumi Totsuka

Hideki Morishima

Kazutaka Inoguchi

Jun Tokumitsu

Shunsuke Nakano

Shoichi Hoshino

Takashi Morie

Tomotaka Fujimori

Miho Ishigami

Masakazu Matsuga

An information processing apparatus includes a reception unit configured to receive an input specifying a position of a detection target included in an image, an acquisition unit configured to acquire a storage amount of training data including a pair of information indicating the image and information indicating the position specified by the input, a training unit configured to train a training model to detect the detection target from the image based on the stored training data, and a display control unit configured to control a display unit to display the storage amount, and a reference amount of the training data set as an amount of the training data necessary for the training unit to train the training model, in a comparable way.

Information processing apparatus related to machine learning for detecting target from image, method for controlling the same, and storage medium

A data processing apparatus includes a storage unit configured to store reference data of a filter operation and coefficient data of filters, a plurality of operators configured to parallelly perform the filter operation on the reference data and the coefficient data of the filters, a first supply unit configured to supply reference data transferred from the storage unit to the plurality of operators, a second supply unit configured to supply coefficient data of a first and second filters transferred from the storage unit to the plurality of operators, and a control unit configured to control the second supply unit to supply the coefficient data of the first filter to a first operator, and supply the coefficient data of the second filter to a second operator.

Convolutional neural network based data processing apparatus, method for controlling the same, and storage medium storing program

An image sensing device is provided. The device comprises pixels including a first pixel which belongs to a first row and a first column, a second pixel which belongs to a second row and the first column and a third pixel which belongs to the second row and a second column, and readout units including a first readout circuit connected to the first and second pixels and a second readout circuit connected to the third pixel. The device performs a first operation and a second operation after the first operation. In the first operation, signal readout from the first and third pixels are performed. In the second operation, signal readout from the second pixel is performed. A controller determines, based on the signal generated by the first operation, a control parameter using to control the second operation.

Image sensing device, camera, and transportation equipment

An object detection apparatus first sets a first partial region having a preset size and a second partial region in a given point (pixel) in an input image. In addition, the object detection apparatus calculates a first information amount in the second partial region, and sets a third partial region based on the size of the first information amount. Furthermore, the object detection apparatus calculates a score based on a salience degree that is based on a difference in statistical feature amount distribution between the first partial region and the second partial region, and based on an information amount of feature amount in the third partial region. Lastly, the object detection apparatus detects a main object by calculating scores on the respective points in the image and applying a predetermined statistical process to the scores.

Object detection apparatus, object detection method, and storage medium

Convolutional neural network based image data processing apparatus, method for controlling the same, and storage medium storing program

To enable a response to an urgent call-out caused by a failure occurrence, the degree of an occurrence of a failure in each of a plurality of apparatuses installed at different locations is predicted and a visit plan of each of a plurality of servicemen is created based on a prediction result.

Information processing apparatus, visit plan creating method, and storage medium

To present a determination result with respect to input data and also a reason of the determination result to a user, an extraction unit configured to extract a plurality of feature amounts from an image including an inspection target object, a determination unit configured to determine an anomaly degree of the inspection target object on the basis of the extracted feature amounts, and an image generation unit configured to generate a defect display image representing a defect included in the inspection target object on the basis of contribution degrees of the respective feature amounts with respect to the determined anomaly degree are provided.

Information processing apparatus, information processing method, and storage medium

There is provided with a convolution operation apparatus. A first product-sum calculator includes a plurality of first multipliers, a plurality of first accumulators, a first data supplier which supplies first data to the plurality of first multipliers, and a second data supplier which supplies a plurality of second data to the plurality of first multipliers. A second product-sum calculator includes a plurality of second multipliers, a plurality of second accumulators, a third data supplier which supplies third data to the plurality of second multipliers, and a fourth data supplier which receives and holds an output from each of the plurality of first accumulators and supply the outputs as fourth data to a corresponding one of the plurality of second multipliers.

Convolution operation apparatus

The accuracy of estimating the category of an object in an image and its region is improved. The present invention detects as to whether each of a plural types of objects is included in an object image, forms a plurality of local regions in a region including the object detected, and calculates a feature quantity of the plurality of local regions formed. Furthermore, the present invention selects a discriminant criterion adapted to the type of the object detected, from a plurality of discriminant criteria for discriminating the plural types of objects, and determines, based on the discriminant criterion selected and the feature quantity calculated, a region of the object detected from the plurality of local regions.

Image recognizing apparatus, image recognizing method, and storage medium

An image processing apparatus, an image processing method, and a recording medium realize identification of an object region and a region category with high precision, estimate a relationship between partial regions in an acquired image by using a database about relationships between the images, and determine a target region in the acquired image based on the relationship.

Image processing apparatus, image processing method, and recording medium

A recognition apparatus according to an embodiment of the present invention includes: a candidate region extraction unit configured to extract a subject candidate region from an image; a feature value extraction unit configured to extract a feature value related to an attribute of the image from the subject candidate region extracted by the candidate region extraction unit; an attribute determination unit configured to determine an attribute of the subject candidate region extracted by the candidate region extraction unit on the basis of the feature value extracted by the feature value extraction unit; and a determination result integration unit configured to identify an attribute of the image by integrating determination results of the attribute determination unit.

Recognition apparatus and recognition method

Object detection apparatus object detection method and storage medium

A pattern recognition apparatus that is lightweight for mounting, and reduces the effects of registration conditions or check conditions on recognition accuracy. Similarity sets for respective local features are calculated from a local feature of input data and local features of a plurality of pieces of dictionary data corresponding to the local feature of the input data. Integrated similarities are calculated by integrating a plurality of similarity sets in the local features according to a registration condition or a check condition. Dictionary data corresponding to the input data is identified based on the calculated integrated similarities.

Pattern recognition apparatus and pattern recognition method that reduce effects on recognition accuracy, and storage medium

An information processing apparatus and method enables a pattern discriminator to learn. The apparatus establishes a branch structure appropriate for learning a discriminator having the branch structure without increasing processing time. The apparatus includes a preliminary learning unit to learn a preliminary discriminator for a respective one of a plurality of combinations of variations in variation categories in a discrimination target pattern. A branch structure determination unit is provided to perform discrimination processing using the preliminary discriminator and to determine a branch structure of a main discriminator based on a result of the discrimination processing. A main learning unit is included to learn the main discriminator based on the branch structure.

Information processing apparatus enabling discriminator to learn and method thereof

In an image within which a face pattern is detected, when a ratio of a skin color pixel is equal to or smaller than a first threshold value in a first region and a ratio of a skin color pixel is equal to or greater than a second threshold value in a second r region, the vicinity of the first region is determined to be a face candidate position at which the face pattern can exist. Face detection is carried out on the face candidate position. The second region is arranged in a predetermined position relative to the first region.

Method and apparatus for detecting and processing specific pattern from image

Growing community of inventors

Kawasaki, Japan

Katsuhiko Mori