Kernel regression with sparse metric learning

Fuzzy Medical Computer Vision Image Restoration and Visual Application

In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration.

Representation Learning for Fine-Grained Change Detection

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.