Kyushu Institute of Technology

Aisin Corporation

Equos Research Co., Ltd.

Kabushiki Kaisha Yaskawa Denki

Shuichi Enokida

Hideo Yamada

Masatoshi Shibata

Kazuhiro Kuno

Hakaru Tamukoh

Ryuya Muramatsu

Tomoyuki Horiuchi

Toshiyuki Kono

Hisashi Ideguchi

Eitaku Nobuyama

Tatsuya Hashimoto

Toshiaki Ejima

Masakazu Sadano

Yuta Yamasaki

Noboru Sebe

Shiryu Ooe

Hiromichi Ohtsuka

An image recognition device includes: an image processing device that acquires a feature amount from an image; and an identification device that determines whether a prescribed identification object is present in the image, and identifies the identification object. The identification device includes a BNN that has learned the identification object in advance, and performs identification processing by performing a binary calculation with the BNN on the feature amount acquired by the image processing device. Then, the identification device selects a portion effective for identification from among high-dimensional feature amounts output by the image processing device to reduce the dimensions used in identification processing, and copies low-dimensional feature amounts output by the image processing device to increase dimensions.

Information processing device

An image processing device has a function for plotting a luminance gradient co-occurrence pair of an image on a feature plane and applying an EM algorithm to form a GMM. The device learns a pedestrian image and creates a GMM, subsequently learns a background image and creates a GMM, and calculates a difference between the two and generates a GMM for relearning based on the calculation. The device plots a sample that conforms to the GMM for relearning on the feature plane by applying an inverse function theorem. The device forms a GMM that represents the distribution of samples at a designated mixed number and thereby forms a standard GMM that serves as a standard for image recognition. When this mixed number is set to less than a mixed number designated earlier, the dimensions with which an image is analyzed are reduced, making it possible to reduce calculation costs.

Image processing device, image recognition device, image processing program, and image recognition program

An image processing device converts an image that is a recognition object image to high-resolution, medium-resolution, and low-resolution images. The device sets the pixel of interest of the high-resolution image, and votes the co-occurrence in a gradient direction with offset pixels, the co-occurrence in the gradient direction pixels in the medium-resolution image, and the co-occurrence in the gradient direction pixels in the low-resolution image, to a co-occurrence matrix. The device creates such a co-occurrence matrix for each pixel combination and for each resolution. The device executes the process on each of the pixels of the high-resolution image, and creates a co-occurrence histogram wherein the elements of a plurality of co-occurrence matrices are arranged in a line. The device normalizes the co-occurrence histogram and extracts, as a feature quantity of the image, a vector quantity having as a component a frequency resulting from the normalization.

Image feature amount output device, image recognition device, the image feature amount output program, and image recognition program

An image recognition device executes a Hilbert scan of frame image data constituting moving-image data to generate one-dimensional spatial image data, and further arrays the one-dimensional spatial image data in a time direction to generate two-dimensional spatio-temporal image data that holds spatial information and temporal information. The image recognition device converts the moving-image data into the two-dimensional spatio-temporal image data while holding the spatial and temporal information. By means of a CNN unit, the image recognition device executes a convolution process wherein a two-dimensional filter is used on the spatio-temporal image data to image-recognize a behavior of a pedestrian who is a recognition object. The image recognition device executes behavioral recognition of the pedestrian that requires a three-dimensional analysis consisting of two dimensions for space and one dimension for time by the image recognition process of the two-dimensional image by the CNN, and estimates a state of the pedestrian.

Image data generation device, image recognition device, image data generation program, and image recognition program

A hardware configuration is constructed for calculating at high speed the co-occurrence of luminance gradient directions between differing resolutions for a subject image. In an image processing device, a processing line for high-resolution images, a processing line for medium-resolution images, and a processing line for low-resolution images are arranged in parallel, and the luminance gradient directions are extracted for each pixel simultaneously in parallel from images having the three resolutions. Co-occurrence matrix preparation units prepare co-occurrence matrices by using the luminance gradient directions extracted from these images having the three resolutions, and a histogram preparation unit outputs a histogram as an MRCoHOG feature amount by using these matrices. To concurrently processing the images having the three resolutions, high-speed processing can be performed, and moving pictures output from a camera can be processed in real time.

Method and device to determine image features using multi-resolution gradient analysis

An image recognition device sets an overall observation region which surrounds a whole body of an object and partial observation regions which surround characteristic parts of the object respectively to locations in an image which are estimated to include captured images of the object. The device clips images in the overall observation region and the partial observation regions, and calculates similarity degrees between them and previously learned images on the basis of a combination of two image feature amounts. The device calculates an optimum ratio in combining the HOG feature amount and the color distribution feature amount individually for the regions. This ratio is determined by setting a weight parameter &#x3b1;i for setting a weight used for combining the HOG feature amount and the color distribution feature amount to be included in a state vector and subjecting the result to complete search by a particle filter.

Image recognition by setting a ratio of combining the feature correlation coefficients for the respective acquired image feature amounts

In an object detecting method according to an embodiment, external reference points are set in external space of a model of an object and an internal reference point is set in internal space of the model. A table is stored in which feature quantities on a local surface of the model are associated with positions of the external reference points and the internal reference point. The feature quantity on the local surface of the model is calculated, and the position of the reference point whose feature quantity is identical to the calculated feature quantity is acquired from the table and is converted into a position in a real space. When the converted position is outside the object, the position is excluded from information for estimation and the position and the attitude of the object are estimated.

Device and method to detect an object and controlling a device to pick up the detected object

An embodiment of the present invention provides an object motion estimating device and the like, which solve a problem of assuming translational movement (that is, a spatial gradient is 0) of an optical flow and are suitable to estimation of an optical flow by an image analysis of captured images. The object motion estimating device performs the image analysis of the captured images of the object to estimate motion of the object. The object motion estimating device includes an image analyzing unit. The image analyzing unitassumes time invariance of the optical flow while not assuming the translational movement, and the image analyzing unitestimates the optical flow with intensity at each point of the captured images as measurement variable while simultaneously estimating a spatial gradient of the optical flow. The assumption about the translational movement of the optical flow is an assumption about the optical flow that is actually unknown. The proposed object motion estimating device can relax this assumption of the translational movement of the optical flow as much as possible.

Growing community of inventors

Iizuka, Japan

Shuichi Enokida