摘要:
In a hierarchical neural network having a module structure, learning necessary for detection of a new feature class is executed by a processing module which has not finished learning yet and includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure by presenting a predetermined pattern to a data input layer. Thus, a feature class necessary for subject recognition can be learned automatically and efficiently.
摘要:
An output value of neuron within an objective layer of a hierarchical neural network is computed. The data of the output value of neuron is stored in a memory only if the output value of neuron is greater than or equal to a predetermined value by referring to the computed output value of neuron within the objective layer. When the data of the output value of neuron on a former layer of objective layer is read from the memory, the data having a predetermined value is read, instead of the data of the output value of neuron not stored in the memory.
摘要:
Camera information corresponding to the position at which the target to be searched for in the captured image is detected is identified, another camera specified by the identified camera information receives a notification that the target has been detected (S209), and when a notification is received from another camera, the posture of the own camera is controlled so that the area associated with the camera information specifying another camera can be observed (S211).
摘要:
An action recognition apparatus includes an input unit for inputting image data, a moving-object detection unit for detecting a moving object from the image data, a moving-object identification unit for identifying the detected moving object based on the image data, a state detection unit for detecting a state or an action of the moving object from the image data, and a learning unit for learning the detected state or action by associating the detected state or action with meaning information specific to the identified moving object.
摘要:
An image processing apparatus stores model information representing a subject model belonging to a specific category, detects the subject from an input image by referring to the model information, determines a region for which an image correction is to be performed within a region occupied by the detected subject in the input image, stores, for a local region of the image, a plurality of correction data sets representing correspondence between a feature vector representing a feature before correction and a feature vector representing a feature after correction, selects at least one of the correction data sets to be used to correct a local region included in the region determined to undergo the image correction, and corrects the region determined to undergo the image correction using the selected correction data sets.
摘要:
A network camera system is provided that detects an occurrence of a condition change at a periphery of an image pickup apparatus, obtains a direction of the detected condition change, and notifies a user of the occurrence of the condition change and a direction of the condition change on a display of a terminal device.
摘要:
Corresponding partial features are extracted from an input pattern and a registration pattern as a preregistered pattern. A plurality of combinations of the partial features are generated as composited partial features. One of registration pattern classes to which the input pattern corresponds is identified by using the plurality of composited partial features. The identification is performed based on the result of evaluation of the partial features which belong to the composited partial feature and correspond to each other in the input pattern and the registration pattern. In the evaluation, the composited partial feature is evaluated based on the measurement result of the quantity of partial features meeting a predetermined criterion.
摘要:
In an image conversion method, a value which reflects the mutual relationship between the classes of pixel patterns each formed from a pixel classified as one of a plurality of classes and peripheral pixels is set as a converted value corresponding to each of the plurality of classes, a pixel of interest is sequentially selected from the input image, and a pixel pattern formed from the selected pixel of interest and a predetermined number of pixels around it is classified as one of the plurality of classes in accordance with a neighboring pattern obtained based on the relationship between the value of the pixel of interest and the values of peripheral pixels located at predetermined relative positions with respect to the pixel of interest. The value of the pixel of interest is converted into a converted value set for a class to which the pixel of interest has been classified.
摘要:
A face detecting unit detects a person's face from input image data, and a parameter setting unit sets parameters for generating a gradient histogram indicating the gradient direction and gradient magnitude of a pixel value based on the detected face. Further, a generating unit sets a region (a cell) from which to generate a gradient histogram in the region of the detected face, and generates a gradient histogram for each such region to generate feature vectors. An expression identifying unit identifies an expression exhibited by the detected face based on the feature vectors. Thereby, the facial expression of a person included in an image is identified with high precision.
摘要:
A learning apparatus for a pattern detector, which includes a plurality of weak classifiers and detects a specific pattern from input data by classifications of the plurality of weak classifiers, acquires a plurality of data for learning in each of which whether or not the specific pattern is included is given, makes the plurality of weak classifiers learn by making the plurality of weak classifiers detect the specific pattern from the acquired data for learning, selects a plurality of weak classifiers to be composited from the weak classifiers which have learned, and composites the plurality of weak classifiers into one composite weak classifier based on comparison between a performance of the composite weak classifier and performances of the plurality of weak classifiers.