Abstract: There is provided with a data processing apparatus that carries out a computation corresponding to a neural network containing a plurality of layers. A processing unit includes a plurality of processors that, through pipeline processing, sequentially calculate data of each of blocks, each block corresponding to a part of a feature plane in one layer. A control unit determines a calculation order for the data of the blocks on the basis of structure information of the neural network, and sends a command that controls the calculation order to the plurality of processors.

Abstract: An apparatus includes a first holding unit and a second holding unit configured to hold first-type data and second-type data, respectively, a first operation unit configured to execute a first product-sum operation based on the first-type data, a branch unit configured to output an operation result of the first product-sum operation in parallel, a sampling unit configured to sample the operation result and to output a sampling result, and a second operation unit configured to execute a second product-sum operation based on the second-type data and the sampling result.

Abstract: An image processing apparatus comprising, a first processing unit configured to process a first image stored in a first memory and output a first processing result in a first size, a conversion unit configured to, if the first size matches a second size, output the first processing result, and if the first size is different from the second size, convert the first processing result into the second size and output a result of the conversion, and a second processing unit configured to process the first processing result outputted from the conversion unit and a second image of the second size stored in a second memory, and to store a second processing result in the second memory in the second size.

Abstract: An image processing apparatus comprising, a first processing unit configured to process a first image stored in a first memory and output a first processing result in a first size, a conversion unit configured to, if the first size matches a second size, output the first processing result, and if the first size is different from the second size, convert the first processing result into the second size and output a result of the conversion, and a second processing unit configured to process the first processing result outputted from the conversion unit and a second image of the second size stored in a second memory, and to store a second processing result in the second memory in the second size.

Abstract: There is provided with a data processing apparatus that carries out a computation corresponding to a neural network containing a plurality of layers. A processing unit includes a plurality of processors that, through pipeline processing, sequentially calculate data of each of blocks, each block corresponding to a part of a feature plane in one layer. A control unit determines a calculation order for the data of the blocks on the basis of structure information of the neural network, and sends a command that controls the calculation order to the plurality of processors.

Abstract: There is provided with an image processing apparatus. An obtaining unit obtains a video with consecutive frame images. A dividing unit divides a frame image of interest into a plurality of divided regions by reference to information of divided regions previously stored in a storage unit. A determining unit determines whether or not a moving object is included in a divided region of interest in the frame image of interest. An updating unit switches whether or not to update information of the divided region of interest stored in the storage unit with information of the divided region of interest of the frame image of interest, according to whether or not a moving object is included in the divided region of interest in the frame image of interest.

Abstract: An apparatus includes a first holding unit and a second holding unit configured to hold first-type data and second-type data, respectively, a first operation unit configured to execute a first product-sum operation based on the first-type data, a branch unit configured to output an operation result of the first product-sum operation in parallel, a sampling unit configured to sample the operation result and to output a sampling result, and a second operation unit configured to execute a second product-sum operation based on the second-type data and the sampling result.

Abstract: Plural block images are generated by dividing an input image into blocks each of which has a predetermined size. Clustering of pixels contained in the plural block images is performed based on a representative point associated with each of those block images to apply region segmentation to those block images. The representative point is a part of plural representative points disposed in the input image. The clustering in a target block image of the region segmentation is performed by referring to a representative point in the target block image, a representative point of a processed block image, and a representative point of an unprocessed block image. The processed and unprocessed block images are adjacent to the target block image.

Abstract: There is provided with an image processing apparatus. An obtaining unit obtains a video with consecutive frame images. A dividing unit divides a frame image of interest into a plurality of divided regions by reference to information of divided regions previously stored in a storage unit. A determining unit determines whether or not a moving object is included in a divided region of interest in the frame image of interest. An updating unit switches whether or not to update information of the divided region of interest stored in the storage unit with information of the divided region of interest of the frame image of interest, according to whether or not a moving object is included in the divided region of interest in the frame image of interest.

Abstract: Plural block images are generated by dividing an input image into blocks each of which has a predetermined size. Clustering of pixels contained in the plural block images is performed based on a representative point associated with each of those block images to apply region segmentation to those block images. The representative point is a part of plural representative points disposed in the input image. The clustering in a target block image of the region segmentation is performed by referring to a representative point in the target block image, a representative point of a processed block image, and a representative point of an unprocessed block image. The processed and unprocessed block images are adjacent to the target block image.

Abstract: Plural block images are generated by dividing an input image into blocks each of which has a predetermined size. Clustering of pixels contained in the plural block images is performed based on a representative point associated with each of those block images to apply region segmentation to those block images. The representative point is a part of plural representative points disposed in the input image. The clustering in a target block image of the region segmentation is performed by referring to a representative point in the target block image, a representative point of a processed block image, and a representative point of an unprocessed block image. The processed and unprocessed block images are adjacent to the target block image.

Abstract: Plural block images are generated by dividing an input image into blocks each of which has a predetermined size. Clustering of pixels contained in the plural block images is performed based on a representative point associated with each of those block images to apply region segmentation to those block images. The representative point is a part of plural representative points disposed in the input image. The clustering in a target block image of the region segmentation is performed by referring to a representative point in the target block image, a representative point of a processed block image, and a representative point of an unprocessed block image. The processed and unprocessed block images are adjacent to the target block image.

Abstract: Provided an acidic oil-in-water type emulsified seasoning comprising egg yolk, the content of oils and fats is 10 to 40 mass %, and the oscillating strain and the loss elastic modulus (G?), which are indicative of dynamic viscoelasticity measured by the following condition with a rheometer, have the following relationship. Condition: At a temperature of 25 to 35° C. and at an angular frequency of 6.2 rad/s Relationship: A peak of the loss elastic modulus (G?) is present in the range of 1 to 100% of the oscillating strain, and the loss elastic modulus at the peak (G?d) is larger than the maximum value of the loss elastic modulus (G?s) in the range of 0.1 to 1% of the oscillating strain.

Abstract: Provided an acidic oil-in-water type emulsified seasoning comprising a cross-linked starch, the content of oils and fats is 10 to 70 mass %, the viscosity is 20,000 to 1,000,000 mPa·s, a first peak is present in the range of 0.5 to 5 micrometers and a second peak is present in the range of 20 to 80 micrometers in a particle size distribution of the acidic oil-in-water type emulsified seasoning measured by using a laser diffraction particle size analyzer, and the cross-linked starch has the following characteristics: the viscosity of a mixture of the cross-linked starch and water prepared under the following condition is 120 to 20,000 mPa·s; and the average particle diameter of the cross-linked starch being present in the mixture is 20 to 40 micrometers. Condition: a mixture of the cross-linked starch and water comprising 8 mass % of the cross-linked starch is heated up to 90° C., and then is maintained at 90° C. for 5 minutes, and then is cooled to 20° C.

Abstract: In the container-packed, oil-in-water type emulsified food product in accordance with the present invention, an oil-in-water type emulsified food comprises edible oils and fats, vinegar and egg yolk, and is packed and sealed in a container with an oxygen barrier property and has a dissolved oxygen concentration immediately after manufacturing is 0.8 to 8.1% O2. This container-packed, oil-in-water type emulsified food product demonstrates excellent flavor balance and small degradation of quality caused by oxidation during storage.

Abstract: A process for producing a potato product includes hermetically enclosing potatoes in a container having an average oxygen permeability of 5 cc/m2·day·atm or less, and heating the potatoes at 60 to 95° C., the potatoes being heated under conditions where dissolved oxygen content in the container becomes 5% O2 or less at least when heating is completed.

Abstract: In the container-packed, oil-in-water type emulsified food product in accordance with the present invention, an oil-in-water type emulsified food comprises edible oils and fats, vinegar and egg yolk, and is packed and sealed in a container with an oxygen barrier property and has a dissolved oxygen concentration immediately after manufacturing is 0.8 to 8.1% O2. This container-packed, oil-in-water type emulsified food product demonstrates excellent flavor balance and small degradation of quality caused by oxidation during storage.

Abstract: An object of the present invention is to provide a method for accurately measuring a three-dimensional shape of a measuring subject independent of the surface shape of the measuring subject, and another object thereof is to shorten the time from the measurements of the measuring subject until three-dimensional shape data is obtained so as to carry out efficient measuring operations. In a three-dimensional measuring system 1 that measures a three-dimensional shape of a measuring subject, two three-dimensional measuring devices 10, 20 are placed. The three-dimensional measuring device 10 measures a measuring subject placed in a measuring space 3 by allowing a laser slit light L1 in a longitudinal direction to scan in a lateral direction. Moreover, the three-dimensional measuring device 20 measures the measuring subject placed in a measuring space 3 by allowing the laser slit light L1 in a lateral direction to scan in a longitudinal direction.

Abstract: An object of the present invention is to provide a method for accurately measuring a three-dimensional shape of a measuring subject independent of the surface shape of the measuring subject, and another object thereof is to shorten the time from the measurements of the measuring subject until three-dimensional shape data is obtained so as to carry out efficient measuring operations. In a three-dimensional measuring system 1 that measures a three-dimensional shape of a measuring subject, two three-dimensional measuring devices 10, 20 are placed. The three-dimensional measuring device 10 measures a measuring subject placed in a measuring space 3 by allowing a laser slit light L1 in a longitudinal direction to scan in a lateral direction. Moreover, the three-dimensional measuring device 20 measures the measuring subject placed in a measuring space 3 by allowing the laser slit light L1 in a lateral direction to scan in a longitudinal direction.

Abstract: A non-invasive glucose concentration measurement apparatus includes: a light source which projects at the sample radiation of a wavelength range including a wavelength at which absorption greatly changes with glucose concentration but does not greatly vary with temperature; a photosensor which receives the radiation which have been projected by the light source, and transmitted through or reflected by the sample, and generates an electrical signal corresponding to a through-transmitted or reflected level of the radiation of the wavelength; and a calculator which calculates a glucose concentration based on the electrical signal. A more accurate glucose concentration can be obtained without the influence of temperature.