摘要:
The object detection apparatus according to the invention detects an object based on input images that are captured sequentially in time in a moving unit. The apparatus generates an action command to be sent to the moving unit, calculates flow information for each local area in the input image, and estimates an action of the moving unit based on the flow information. The apparatus calculates a difference between the estimated action and the action command and then determines a specific local area as a figure area when such difference in association with that specific local area exhibits an error larger than a predetermined value. The apparatus determines presence/absence of an object in the figure area.
摘要:
An object detection apparatus and method capable of detecting objects based on visual images captured by a self-moving unit. A sequential images output section makes a train of a first input image and a second input image sequential to the first input image and outputs said train. A local area image processor calculates local flows based on said first input image and said second input image. An inertia information acquiring section measures self-motion of the unit to calculate inertia information thereof. A global area image processor uses said inertia information to estimate global flow, which is a motion field of the entire view associated to the self-motion, using said global flow and said first input image and creates a predictive image of said second input image. The global area image processor then calculates differential image data, which is a difference between said predictive image and said second input image. A figure-ground segregation section uses said differential image data to refine said local flows and compares the refined local flows with a predetermined threshold value to extract a figure candidate area, which is the area having a high probability of an object existing in the input image. An object presence/absence determination section determines presence/absence of objects in said figure candidate area.
摘要:
An object detection apparatus is provided for detecting both stationary objects and moving objects accurately from an image captured from a moving mobile unit.The object detection apparatus of the present invention applies Gabor filter to two or more input images captured by an imaging device such as CCD camera mounted on a mobile unit, and calculates optical flow of local areas in the input images. Then the object detection apparatus closely removes optical flow produced by motion of the mobile unit by estimating optical flow produced from background of the input images. In other words, the object detection apparatus clarifies the area where object is not present (“ground”) in the input images. By removing such “ground” part, the area where objects seems to be present (“feature”) is extracted from the input images. Finally, the object detection apparatus determines whether objects are present or not using flow information of the extracted “feature” part.
摘要:
An object detection apparatus and method capable of detecting objects based on visual images captured by a self-moving unit. A sequential images output section makes a train of a first input image and a second input image sequential to the first input image and outputs said train. A local area image processor calculates local flows based on said first input image and said second input image. An inertia information acquiring section measures self-motion of the unit to calculate inertia information thereof. A global area image processor uses said inertia information to estimate global flow, which is a motion field of the entire view associated to the self-motion, using said global flow and said first input image and creates a predictive image of said second input image. The global area image processor then calculates differential image data, which is a difference between said predictive image and said second input image. A figure-ground segregation section uses said differential image data to refine said local flows and compares the refined local flows with a predetermined threshold value to extract a figure candidate area, which is the area having a high probability of an object existing in the input image. An object presence/absence determination section determines presence/absence of objects in said figure candidate area.
摘要:
An image recognizing apparatus and method is provided for recognizing behavior of a mobile unit accurately with an image of external environment acquired during the mobile unit is moving.Behavior command output block 12 outputs behavior commands to cause the mobile unit 32 move. Local feature extraction block 16 extracts features of local areas of the image from the image of external environment acquired on the mobile unit 32 when the behavior command is output. Global feature extraction block 18 extracts feature of global area of the image using the features of local areas. Learning block 20 calculates probability models for recognizing behavior given to the mobile unit 32 based on the feature of global area of the image. After learning is finished, behavior of the mobile unit 32 may be recognized rapidly and accurately by applying the probability models to an image of external environment acquired in mobile unit 32 afresh.
摘要:
The invention relates to a behavior control apparatus and method for autonomously controlling a mobile unit based on visual information in practical application without the needs of a greatdeal of preparation or computational cost and limiting the type of target object. According to one aspect of the invention, a method for controlling behavior of a mobile unit using behavior command is provided. First, sensory inputs are captured and then the motion of the mobile unit is estimated. The portion which includes a target object to be target for behavior of the mobile unit is segregated from the sensory inputs. The target objects extracted from the segregated portion and the location of the target object is acquired. Finally, the mobile unit is controlled based on the location of target object.
摘要:
A face recognition system includes a component learning/extraction module, component classifier training module, knowledge base for component classification (KBCC), component extraction module (CEM), object identification training module (OITM), knowledge base for face identification (KBFI), and object identification module (OIM). The CEM receives image data of faces at various viewpoints and extracts outputs of classification of the component data, using the results of classifier training of the component data, stored in the KBCC. The OITM receives the outputs of classification of the component data and determines indicator component for each person by Bayesian estimation so that posterior probability of a predetermined attention class is maximized under the outputs of classification of the component data at various viewpoints. The KBFI stores indicator components for the individuals. The OIM receives the outputs of classification of the component data and identifies faces using the indicator components stored in the KBFI.
摘要:
A production device and method which produce a multiple-system film having metal components such as TiAlN greatly different in melting point by a melting-evaporation type ion plating method that provides a high material utilization efficiency and a good film quality. Power needed to evaporate a material (4) is first supplied, and then power gradually increased over the initail power is repeatedly supplied until a needed maximum power is reached. Concurrently, a plasma control is performed for converging plasma (7) onto an initial area needed to evaporate the material, and then a plasma control is performed for continuously and sequentially moving/expanding plasma from the initial plasma area up to a maximum plasma area to thereby gradually melt the non-melted portion of the material.
摘要:
An agent learning apparatus comprises a sensor (301) for acquiring a sense input, an action controller (307) for creating an action output in response to the sense input and giving the action output to a controlled object, an action state evaluator (303) for evaluating the behavior of the controlled object, a selective attention mechanism (304) for storing the action output and the sense input corresponding to the action output in one of the columns according to the evaluation, calculating a probability model from the action outputs stored in the columns, and outputting, as a learning result, the action output related to a newly given sense input in the column where the highest confidence obtained by applying the newly given sense input to the probability model is stored. By thus learning, the selective attention mechanism (304) obtains a probability relationship between the sense input and the column. An action output is calculated on the basis of the column evaluated as a stable column. As a result, the dispersion of the action output is quickly minimized, and thereby the controlled object can be stabilized.
摘要:
A trajectory planning system obtains a trajectory for controlling a state of an object toward a goal state. The system includes a search tree generating section which registers a state of the object as a root of a search tree in a state space, registers a next state of the object after a lapse of a predetermined time interval obtained through dynamical relationships during the time interval as a branch of the search tree in the state space. The system further includes a known-state registration tree storing section which stores a known-state registration tree and a known-state registration tree generating section which determines a cell to which the next state belongs among a plurality of cells previously prepared by segmenting the state space, determines whether or not a state which belongs to the cell has already been registered as a branch of the known-state registration tree, discards the next state when a state which belongs to the cell has been registered, and registers the next step as a branch of the known-state registration tree when a state which belongs to the cell has not been registered. The system further includes a trajectory generating section which selects a state whose distance to the goal state is minimum among states registered as branches of the known-state registration tree and obtains a trajectory using a sequence of states in a backward direction from the state toward the root of the known-state registration tree.