The classical SURF algorithm has many disadvantages, such as high dimension of feature descriptor, large amount of computation, and low matching accuracy when the angle of rotation and angle of view is too large. To solve the above problems, an improved algorithm is proposed. Firstly, image preprocessing is carried out by image binarization, feature points are extracted by Hes-sian matrix, and then feature description is carried out by using circular neighborhood of feature points. Har wavelet response is used to establish descriptors for each feature point, and the normalized gray-level difference and second-order gradient in the neighborhood are calculated simultaneously to form a new feature descriptor. Finally, the RANSAC algorithm is used to eliminate the mismatch points. The algorithm does not Compared with the classical SURF algorithm, it has the advantage of speed, and makes full use of the gray level information and the detail information, so it has higher accuracy. Experimental results show that the algorithm has good robustness and stability to image blur, illumination difference, angle rotation, field of view transformation and so on. The algorithm is applied to remote sensing image stitching to obtain the stitched image with no obvious geometric shift and good edge connection. This algorithm is a kind of image registration algorithm with short time and high precision, which can meet the registration requirements of remote sensing image stitching.

This paper introduces the use of pictogram design concept in ancient China for the development of a set of today’s "graphic icons" or "infographics" in modern social network visualization systems. These graphic icons should be close to sensory symbols that derive their expressive power from their ability to use the perceptual processing power of the brain without learning. Therefore, with the use of such a set of "sensory symbols" we aim to achieve the identification of corresponding physical objects (or their attributes) to be performed close to the pre-attentive time.

In the realm of intelligent transportation, pedestrian detection has witnessed significant advancements. However, it continues to grapple with challenging issues, notably the detection of pedestrians in complex lighting scenarios. Conventional visible light mode imaging is profoundly affected by varying lighting conditions. Under optimal daytime lighting, visibility is enhanced, leading to superior pedestrian detection outcomes. Conversely, under low-light conditions, visible light mode imaging falters due to the inadequate provision of pedestrian target information, resulting in a marked decline in detection efficacy. In this context, infrared light mode imaging emerges as a valuable supplement, bolstering pedestrian information provision. This paper delves into pedestrian detection and tracking algorithms within a multi-modal image framework grounded in deep learning methodologies. Leveraging the YOLOv4 algorithm as a foundation, augmented by a channel stack fusion module, a novel multi-modal pedestrian detection algorithm tailored for intelligent transportation is proposed. This algorithm capitalizes on the fusion of visible and infrared light mode image features to enhance pedestrian detection performance amidst complex road environments. Experimental findings demonstrate that compared to the Visible-YOLOv4 algorithm, renowned for its high performance, the proposed Double-YOLOv4-CSE algorithm exhibits a notable improvement, boasting a 5.0% accuracy rate enhancement and a 6.9% reduction in logarithmic average missing rate. This research’s goal is to ensure that the algorithm can run smoothly even on a low configuration 1080 Ti GPU and to improve the algorithm’s coverage at the application layer, making it affordable and practical for both urban and rural areas. This addresses the broader research problem within the scope of smart cities and remote ends with limited computational power.

Network security visualization technology makes use of human vision’s ability to obtain models and structures, and presents abstract network and system data in the form of graphics and images, which is very important in the research of video and image recognition. The purpose of this study is to apply the technology of network security data visualization fusion in image recognition and analyze its role. In this study, Visda-2017 and other data sets are used, and image data preprocessing, image dehumidification, registration and fusion are carried out. After setting the parameters, the parameters are trained by convolution neural network. Finally, the network situation value and the accuracy and false alarm rate of this algorithm are calculated. The results show that when the threshold value δ1 = 2, it has a high detection rate, with an average rate of 98%, and a high false alarm rate of 1.6%; and the image recognition accuracy rate of the classifier of F-CDCGAN model is the highest among the six methods, reaching 99.46%. It is concluded that the image recognition algorithm combined with network security visualization technology fusion, feature points extraction and recognition accuracy and detection rate is high, false alarm rate is low. This study provides an effective method for data security visualization fusion image processing and recognition.

With further development in the fields of computer vision, network security, natural language processing and so on so forth, deep learning technology gradually exposed certain security risks. The existing deep learning algorithms cannot effectively describe the essential characteristics of data, making the algorithm unable to give the correct result in the face of malicious input. Based on current security threats faced by deep learning, this paper introduces the problem of adversarial examples in deep learning, sorts out the existing attack and defense methods of the black box and white box, and classifies them. It briefly describes the application of some adversarial examples in different scenarios in recent years, compares several defense technologies of adversarial examples, and finally summarizes the problems in this research field and prospects for its future development. This paper introduces the common white box attack methods in detail, and further compares the similarities and differences between the attack of the black and white box. Correspondingly, the author also introduces the defense methods, and analyzes the performance of these methods against the black and white box attack.