Abstract:
A learning apparatus, a learning method, and a program are provided to learn the dynamics of a network in a self organizing manner according to the value corresponding to the learning weighting value through a learning unit. A learning apparatus comprises a memory unit, a learning unit(1-4), a winner node determining unit(7-2), and a weighting value determining unit(7-3). The memory unit memorizes the network formed by plural nodes maintaining the dynamics. The learning unit learns the dynamics of the network in a self-organizing manner on the basis of the time-series data. The winner node determining unit determines a winner node which is the node having the dynamics proper to the time-series data. The weighting value determining unit determines the weighting value of the dynamics according to the distance of the individual node from the winner node.
Abstract:
A plane detecting unit (3) of a plane detector has a line segment extracting section (4) which selects a group of distance data points on the same plane from among distance data points forming an image and extracts line segments from the distance data point group and a region expanding section (5) which detects one or more plane regions present in the image from a line segment group of all the line segments extracted by the line segment extracting section (4). The line segment extracting section (4) draws a line segment (L1) connecting the ends of the distance data point group, seeks a point of interest (brk) from which the distance to the line segment (L1) is the largest, divides the data point group at the point of interest if the distance is a predetermined value or more, determines a line segment (L2) by the least squares method if the distance is below the predetermined value, judges that the line segment is of a zigzag shape if a predetermined number of data points are continuously present on one side of the line segment (L2), divides the data point group at the point of interest (brk), and repeats the above processings. With this, planes are correctly detected at a time from the distance data including measurement noise robustly against noise.
Abstract:
A robot includes a face extracting section for extracting features of a face included in an image captured by a CCD camera, and a face recognition section for recognizing the face based on a result of face extraction by the face extracting section. The face extracting section is implemented by Gabor filters that filter images using a plurality of filters that have orientation selectivity and that are associated with different frequency components. The face recognition section is implemented by a support vector machine that maps the result of face recognition to a non-linear space and that obtains a hyperplane that separates in that space to discriminate a face from a non-face. The robot is allowed to recognize a face of a user within a predetermined time under a dynamically changing environment.
Abstract:
An object detecting device 1 comprises a scaling section 3 for generating scaled images by scaling down a gradation image input from an image output section 2, a scanning section 4 for sequentially manipulating the scaled images and cutting out window images from them and a discriminator 5 for judging if each window image is an object or not. The discriminator 5 includes a plurality of weak discriminators that are learnt in a group by boosting and an adder for making a weighted majority decision from the outputs of the weak discriminators. Each of the weak discriminators outputs an estimate telling the likelihood of a window image to be an object or not by using the difference of the luminance values of two pixels. The discriminator 5 suspends the operation of computing estimates for a window image that is judged to be a non-object, using a threshold value that is learnt in advance.
Abstract:
An object detecting device 1 comprises a scaling section 3 for generating scaled images by scaling down a gradation image input from an image output section 2, a scanning section 4 for sequentially manipulating the scaled images and cutting out window images from them and a discriminator 5 for judging if each window image is an object or not. The discriminator 5 includes a plurality of weak discriminators that are learnt in a group by boosting and an adder for making a weighted majority decision from the outputs of the weak discriminators. Each of the weak discriminators outputs an estimate telling the likelihood of a window image to be an object or not by using the difference of the luminance values of two pixels. The discriminator 5 suspends the operation of computing estimates for a window image that is judged to be a non-object, using a threshold value that is learnt in advance.
Abstract:
A robot includes a face extracting section for extracting features of a face included in an image captured by a CCD camera, and a face recognition section for recognizing the face based on a result of face extraction by the face extracting section. The face extracting section is implemented by Gabor filters that filter images using a plurality of filters that have orientation selectivity and that are associated with different frequency components. The face recognition section is implemented by a support vector machine that maps the result of face recognition to a non-linear space and that obtains a hyperplane that separates in that space to discriminate a face from a non-face. The robot is allowed to recognize a face of a user within a predetermined time under a dynamically changing environment.
Abstract:
A robot includes a face extraction unit for extracting a feature of a face contained in an image picked up by a CCD camera and a face recognition unit for recognizing a face according to the face extraction result obtained by the face extraction unit. The face extraction unit is composed of a Gabor filter for filtering an image by using a plurality of filters having direction selectivity and different frequency components. The face recognition unit is composed of a support vector machine for mapping the face extraction result onto a non-linear space and obtaining a hyperplane for separation in the space, thereby distinguishing face from non-face. The robot can recognize a user face in a dynamically changing environment within a predetermined time.
Abstract:
A display apparatus includes a frame and an image display apparatus for a left eye 3 and an image display apparatus for a right eye which are attached to the frame. Each image display apparatus 30 includes an image forming apparatus 40 and an optical system 50 configured to guide an image from the image forming apparatus 40 to a pupil of an observer 10. The optical system 50 includes a reflecting mirror 51 and a lens group 52. The image forming apparatus 40 is curved. A normal NLL of the reflecting mirror 51 included in the optical system of the image display apparatus for a left eye and a normal NLR of the reflecting mirror 51 included in the optical system of the image display apparatus for a right eye intersect with each other in a space on an opposite side of the observer 10 with respect to the reflecting mirror 51.