BEIJING, Oct. 4, 2023 /PRNewswire/––WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that WiMi is working on feature transformation technique for image data augmentation, which is a commonly used method in image data augmentation to increase the diversity and richness of an image by performing a series of feature transformation operations on the image, thus improving the generalization ability of machine learning algorithms. The feature transformation can generate a new image by changing the color, shape, texture and other features of the image, so that the model can be better adapted to different scenes and objects. In practical applications, different feature transformation techniques can be selected and combined according to specific needs to achieve the best effect.
A common feature transformation is image rotation. By performing a image rotation, the angle and orientation of the image can be changed, thus increasing the diversity of the image. For example, when training a target detection model, the image can be randomly rotated by a certain angle, enabling the model to better adapt to targets at different angles. And another common feature transformation technique is image panning. By performing a panning operation on an image, the position and layout of the image can be changed, thus increasing the diversity of the image. For example, when training an image classification model, the image can be randomly translated by a certain distance, enabling the model to better adapt to objects at different locations. In addition to rotation and panning, there are many other feature transformation techniques that can be used for image data augmentation, such as scaling, flipping, and clipping. These techniques can be selected and combined according to specific application scenarios and needs to achieve the best results.
This technique applied in image data augmentation can increase the data samples. For example, by performing feature transformation operations such as rotating, flipping, scaling, and panning on the original image, multiple new image samples can be generated, thereby expanding the size of the training dataset and improving the generalization ability of the model. By increasing the diversity of data, the model is thus better adapted to various noise and missing situations. In addition, the generalization ability of the model can be further improved by applying multiple feature transformation techniques in combination. Through the two feature transformation techniques, rotation transformation and scale transformation, the model can be exposed to more images at different angles and scales during the training process, thus improving its adaptability to rotation and scale transformation, and thus enhancing the performance of the model in practical applications.
The feature transformation technique researched by WiMi for image data augmentation include brightness adjustment, color transformation, geometric transformation, noise addition and so on. Brightness adjustment include histogram equalization, contrast stretching, and adaptive histogram equalization, which can make the details of the image clearer and enhance the visual effect of the image. By changing the color space of the image, the color and tone of the image can be changed. Color transformation include RGB to grayscale conversion, RGB to HSV conversion and RGB to LAB conversion, etc. These methods can make the colors of the image more vivid and increase the visual impact of the image. Geometric transformation refers to changing the shape and structure of an image by performing geometric transformations such as translation, rotation, scaling and flipping to make the shape of the image more diverse and increase the visual variability of the image. Noise addition refers to adding noise to the image to simulate the noise situation in the real scene, thus increasing the complexity of the image, making the image more realistic and enhancing the visual realism of the image.
By comprehensively applying the above feature transformation techniques of WiMi, a large number of image samples can be generated, thus expanding the image dataset and improving the generalization ability of the machine learning algorithm. In practical applications, we can also choose appropriate feature transformation techniques according to the needs of specific tasks and combine them with machine learning algorithms for training and testing.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company’s beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company’s strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company’s goals and strategies; the Company’s future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company’s expectations regarding demand for and market acceptance of its products and services.
Further information regarding these and other risks is included in the Company’s annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.
Source : WiMi is Reaching Feature Transformation Technique for Image Data Augmentation
The information provided in this article was created by Cision PR Newswire, our news partner.The author's opinions and the content shared on this page are their own and may not necessarily represent the perspectives of Thailand Business Directory.