4results about How to "Rich texture information" patented technology

The invention relates to an image enhancement method, in particular to an infrared image enhancement method based on polarization information. The method comprises the following steps: 1, obtaining polarization images of a target scene at four different angles of an infrared band; 2, acquiring Stokes vector images S0, S1 and S2, a polarization degree image and a polarization angle image; 3, acquiring a temperature image T; 4, acquiring a material image M; 5, obtaining a texture image C; and step 6, combining the material image M, the temperature image T and the texture image C to generate an HSV enhanced image of the target scene so as to realize infrared image enhancement. According to the method, four-angle polarization images of a target scene in an infrared band are obtained through an infrared polarization camera, detection perception of environment target temperature, materials and textures is achieved through polarization information, and then an HSV enhanced image rich in contrast ratio and texture information is obtained through an HSV color space fusion method according to the three-dimensional physical information.

The invention discloses an indoor robot sensing method based on point cloud multi-sensor fusion mapping, and relates to an indoor intelligent robot sensing method, which comprises the following steps of: obtaining multi-frame point cloud images of surrounding environment and obstacles by using a binocular camera and a laser radar; processing and fusing the three-dimensional point cloud images respectively acquired by the two three-dimensional point cloud images; in order to improve the robustness of perception, the three-dimensional point cloud is preprocessed before fusion, and interference of point cloud noise on subsequent matching is avoided; based on the target point cloud data corresponding to each frame of image, the point cloud map is accurately registered, so that the intelligent robot can specifically understand the external environment. According to the method, fusion mapping is carried out based on the point cloud data obtained by different sensors, the environment information of the intelligent equipment is determined, the wrong point cloud determined according to the depth corresponding to each point cloud is filtered out from the target point cloud data, and the mapping effect is optimized by filtering out the wrong point cloud influencing the accuracy of the volume measurement result, so that the accuracy of the volume measurement result is improved. The mapping error is obviously reduced, the sensing precision of the robot is improved, and the robustness of the system is improved.

This application discloses a radar echo extrapolation method based on temporal motion decomposition and dominant fusion, relating to the field of spatiotemporal sequence prediction technology. The method proposes an innovative network fusion paradigm, specifically including: acquiring implicit long-term / short-term temporal features and explicit global / transient motion features during processing using stacked hierarchical recurrent networks; then, innovatively constructing query vectors using temporal features and key-value vectors using motion features, and fusing them using a heterogeneous cross-attention mechanism to form a fusion architecture "dominated by an implicit state modeling network and referenced by an explicit motion modeling network." Based on this, through a series of collaborative designs such as directional decoupling, reference confidence estimation, and long-term feature constraints on short-term features, noise in long-term / short-term motion features is effectively suppressed, enhancing system stability. This method can improve the problems of blurred texture details and attenuation in high-value echo regions in predicted images.

The application discloses a hand model reconstruction method based on feature interaction, introduces a processing mode of feature interaction in a hand posture estimation stage, so that hand features and skeleton features can effectively learn from each other, so that in the case of occlusion, the hand gesture recognition result can also have higher accuracy. At the same time, in the aspect of feature extraction, the application of the subnetwork also reduces the parameters of the network and accelerates the convergence speed of the model. Finally, the output of the network is a hand shape containing surface texture information, compared with the traditional hand posture estimation result which only presents a hand shape composed of points and lines, the hand reconstruction model obtained by the application is more in line with the hand shape of a person, and when human-computer interaction is realized, a more real interactive experience can be realized, and the operation accuracy can be improved.