Abstract: There is provided a self-directed visual intelligence system, The self-directed visual intelligence system according to an embodiment prepares data necessary for training a visual intelligence model when a change in a visual context of a real world is recognized, configures a visual intelligence model and configures training data of the visual intelligence model, based on the changed visual context of the real world, trains the configured visual intelligence model with the training data, and evaluates performance of the trained visual intelligence model. Accordingly, the visual intelligence model is corrected/improved in a self-directed way according to a change in a visual context of a real world, and is grown/advanced by itself, so that performance of the visual intelligence model is maintained in a best state even in response to any change in the context of the real world.

Abstract: A method and an apparatus for detecting a lane is provided. The lane detection apparatus according to an embodiment includes: an acquisition unit configured to acquire a front image of a vehicle; and a processor configured to input the image acquired through the acquisition unit to an AI model, and to detect information of a lane on a road, and the AI model is trained to detect lane information that is expressed in a plane form from an input image. Accordingly, data imbalance between a lane area and a non-lane area can be solved by using the AI model which learns/predicts lane information that is expressed in a plane form, not in a segment form such as a straight line or curved line.

Abstract: A method and an apparatus for detecting a lane is provided. The lane detection apparatus according to an embodiment includes: an acquisition unit configured to acquire a front image of a vehicle; and a processor configured to input the image acquired through the acquisition unit to an AI model, and to detect information of a lane on a road, and the AI model is trained to detect lane information that is expressed in a plane form from an input image. Accordingly, data imbalance between a lane area and a non-lane area can be solved by using the AI model which learns/predicts lane information that is expressed in a plane form, not in a segment form such as a straight line or curved line.

Abstract: An endoscopic stereo matching method and apparatus using a direct attenuation model is provided. A method for generating a depth image according to an embodiment includes: generating a stereo image; and estimating a depth image from the stereo image based on an attenuation trend of light of an illumination used to generate the stereo image. Accordingly, a dense depth image can be obtained by using images obtained from a capsule endoscope, and thus the geometric structure of the inside of the GI tract can be estimated.

Abstract: An endoscopic stereo matching method and apparatus using a direct attenuation model is provided. A method for generating a depth image according to an embodiment includes: generating a stereo image; and estimating a depth image from the stereo image based on an attenuation trend of light of an illumination used to generate the stereo image. Accordingly, a dense depth image can be obtained by using images obtained from a capsule endoscope, and thus the geometric structure of the inside of the GI tract can be estimated.