4 results about "Scale variation" patented technology

Provided are a secure two-party data comparison method and apparatus based on scale transformation. The method includes: transmitting, by a computation requesting party, a two-party data comparison request to two participant nodes; performing, by each participant node, scale transformation and linear scaling on private data locally after receiving the two-party data comparison request, to obtain an encrypted vector; determining, by each participant node, a real number locally using a secure two-party dot product protocol based on the local encrypted vector, and sharing the real number with the other participant node; determining, by each participant node, a comparison sign based on the obtained real number and transmitting the comparison sign to the computation requesting party; and determining, by the computation requesting party, a comparison result of the private data of the two participant nodes based on the comparison signs transmitted by the two participant nodes.

The invention discloses an RGB-D salient target detection method based on texture enhancement guidance. The RGB-D salient target detection method is specifically implemented according to the following steps: step 1, constructing a data set and an encoder; step 2, constructing a texture enhancement module; step 3, constructing a dual-path adaptive interaction module; and 4, constructing a dynamic decoding module. In order to solve the key problems of heterogeneous modal feature degradation, depth noise cross-layer propagation, multi-scale semantic mismatch and the like in the prior art, noise suppression of depth features is realized by using high-frequency texture prior constraints, and cross-modal semantic association is established through a channel-space collaborative dynamic interaction mechanism. A deformable cross-scale transformation technology is adopted to realize progressive calibration of multi-level features, and the method shows remarkable boundary integrity and noise suppression advantages in input scenes of multiple targets, low-quality depth and the like.

The invention discloses a few-sample learning target counting method fusing multi-scale detection and example box selection, and the method comprises the steps: 1, carrying out the preprocessing of a query image and an example image with scale information, and carrying out the splicing, and obtaining a spliced image; 2, inputting the spliced image into a visual transformer model to obtain query image features; and 3, inputting the query image features into a regression decoder, and completing target counting in the query image. According to the method, the problem of scale change in the image is effectively solved, and the accuracy and robustness of target detection are remarkably improved.

This invention discloses a small target detection system and method based on context awareness and multi-scale feature fusion. This invention effectively improves the detection accuracy and robustness of small targets in complex scenes, significantly mitigating the false negatives and missed detections caused by weak features, background interference, and scale variations in traditional methods. By enhancing the global semantic consistency of features and suppressing noise, the boundaries and texture details of small targets are clearly preserved. Simultaneously, it overcomes the geometric misalignment and feature dilution phenomena in multi-scale fusion, achieving precise alignment and adaptive fusion of cross-layer features. Ultimately, while maintaining detection sensitivity, it reduces the computational burden and improves the system's practicality and stability in real-world scenarios.