Image Information Research Articles

Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology.

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades.

Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network

This Data fusion has become a significant issue in diagnostic imaging, particularly in medical applications like radiation and guided image surgery. Medical image fusion aims to enhance the precision of tumor diagnosis, by preserving the salient information and characteristics of the original images in the fused image. It has been shown that guided filters are capable of maintaining edges well. In this paper, we propose a novel cross-guided filter-based fusion approach for multimodal medical images utilizing convolutional neural networks. The cross-guided filter is used in the proposed algorithm to extract the detailed features from the source images. Convolutional neural networks are used to generate the feature weights of source images derived from the detail layers. The weighted average rule is used to merge the source images based on these weights. We used thirty distinct types of medical images from diverse sources to compare the effectiveness of the proposed strategy to that of existing methods, both numerically and visually. The experimental findings demonstrated that, in terms of both objective evaluation and qualitative image quality, the suggested system performs better than other standard methods already in use. The quantitative results show that compared to existing methods under consideration for comparison, the proposed algorithm improves mutual information by 25%, image entropy by 9.5%, spatial frequency by 21%, standard deviation by 18.1%, structural similarity index by 30%, and edge strength of the fused image by 39%.

CNCAN: Contrast and normal channel attention network for super-resolution image reconstruction of crops and weeds

Numerous studies have been performed to apply camera vision technologies in robot-based agriculture and smart farms. In particular, to obtain high accuracy, it is essential to procure high-resolution (HR) images, which requires a high-performance camera. However, due to high costs it is difficult to widely apply the camera in agricultural robots. To overcome this limitation, we propose contrast and normal channel attention network (CNCAN) for super-resolution reconstruction (SR), which is the first research for the accurate semantic segmentation of crops and weeds even with low-resolution (LR) images captured by low-cost and LR camera. Attention block and activation function that considers high frequency and contrast information of images are used in CNCAN, and the residual connection method is applied to improve the learning stability.As a result of experimenting with three open datasets, namely, Bonirob, rice seedling and weed, and crop/weed field image (CWFID) datasets, the mean intersection of union (MIOU) results of semantic segmentation for crops and weeds with SR images through CNCAN were 0.7685, 0.6346, and 0.6931 in the Bonirob, rice seedling and weed, and CWFID datasets, respectively, confirming higher accuracy than other state-of-the-art methods for SR.

Lightweight Indoor Positioning System Based on Multiple Self-Learning Features and Key Frame Classification

Abstract. Traditional indoor positioning technologies mostly require advanced installation of hardware devices, resulting in high costs and long-term maintenance. With advancements in image recognition and deep learning technologies, indoor visual positioning based on image recognition has become increasingly mature. This method offers the benefits of low cost and does not require additional hardware installation. However, it still has inherent defects, such as cumbersome data collection, complex algorithms, and universality. To minimize indoor information pre-collection cost, improve versatility, and enable rapid deployment in low-performance mobile devices, this paper proposes a lightweight indoor positioning system based on multiple self-learning features and key frame classification. The system is divided into two stages: preprocessing and real-time positioning. In the preprocessing stage, image information is collected for the entire indoor environment, and a key-frame recognizer is trained based on the image information. Simultaneously, an environmental feature information database is established. In the real-time positioning stage, the system first uses mobile devices such as smartphones to obtain real-time video streams. A key frame recognizer based on convolutional neural networks identifies key frames in each video stream frame, thereby obtaining approximate positions for rough positioning. Second, feature points are identified in each frame of the video stream and matched with feature points with location information in the built environmental feature information database to calculate precise positions for fine positioning. It has significant optimizations compared with conventional visual solutions in terms of preprocessing data collection, algorithm performance consumption, and versatility.

Unsafe behavior identification on construction sites by combining computer vision and knowledge graph–based reasoning

PurposeWorkers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.Design/methodology/approachThis study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.FindingsA case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.Originality/valueExisting studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

Revolution or risk?-Assessing the potential and challenges of GPT-4V in radiologic image interpretation.

ChatGPT-4 Vision (GPT-4V) is a state-of-the-art multimodal large language model (LLM) that may be queried using images. We aimed to evaluate the tool's diagnostic performance when autonomously assessing clinical imaging studies. A total of 206 imaging studies (i.e., radiography (n = 60), CT (n = 60), MRI (n = 60), and angiography (n = 26)) with unequivocal findings and established reference diagnoses from the radiologic practice of a large university hospital were accessed. Readings were performed uncontextualized, with only the image provided, and contextualized, with additional clinical and demographic information. Responses were assessed along multiple diagnostic dimensions and analyzed using appropriate statistical tests. With its pronounced propensity to favor context over image information, the tool's diagnostic accuracy improved from 8.3% (uncontextualized) to 29.1% (contextualized, first diagnosis correct) and 63.6% (contextualized, correct diagnosis among differential diagnoses) (p ≤ 0.001, Cochran's Q test). Diagnostic accuracy declined by up to 30% when 20 images were re-read after 30 and 90 days and seemed unrelated to the tool's self-reported confidence (Spearman's ρ = 0.117 (p = 0.776)). While the described imaging findings matched the suggested diagnoses in 92.7%, indicating valid diagnostic reasoning, the tool fabricated 258 imaging findings in 412 responses and misidentified imaging modalities or anatomic regions in 65 images. GPT-4V, in its current form, cannot reliably interpret radiologic images. Its tendency to disregard the image, fabricate findings, and misidentify details, especially without clinical context, may misguide healthcare providers and put patients at risk. Question Can Generative Pre-trained Transformer 4 Vision (GPT-4V) interpret radiologic images-with and without clinical context? Findings GPT-4V performed poorly, demonstrating diagnostic accuracy rates of 8% (uncontextualized), 29% (contextualized, most likely diagnosis correct), and 64% (contextualized, correct diagnosis among differential diagnoses). Clinical relevance The utility of commercial multimodal large language models, such as GPT-4V, in radiologic practice is limited. Without clinical context, diagnostic errors and fabricated findings may compromise patient safety and misguide clinical decision-making. These models must be further refined to be beneficial.

A Geometric Significance-Aware Deep Mutual Learning Network for Building Extraction from Aerial Images

Knowledge-driven building extraction method exhibits a restricted adaptability scope and is vulnerable to external factors that affect its extraction accuracy. On the other hand, data-driven building extraction method lacks interpretability, heavily relies on extensive training data, and may result in extraction outcomes with building boundary blur issues. The integration of pre-existing knowledge with data-driven learning is essential for the intelligent identification and extraction of buildings from high-resolution aerial images. To overcome the limitations of current deep learning building extraction networks in effectively leveraging prior knowledge of aerial images, a geometric significance-aware deep mutual learning network (GSDMLNet) is proposed. Firstly, the GeoSay algorithm is utilized to derive building geometric significance feature maps as prior knowledge and integrate them into the deep learning network to enhance the targeted extraction of building features. Secondly, a bi-directional guidance attention module (BGAM) is developed to facilitate deep mutual learning between the building feature map and the building geometric significance feature map within the dual-branch network. Furthermore, the deployment of an enhanced flow alignment module (FAM++) is utilized to produce high-resolution, robust semantic feature maps with strong interpretability. Ultimately, a multi-objective loss function is crafted to refine the network’s performance. Experimental results demonstrate that the GSDMLNet excels in building extraction tasks within densely populated and diverse urban areas, reducing misidentification of shadow-obscured regions and color-similar terrains lacking building structural features. This approach effectively ensures the precise acquisition of urban building information in aerial images.

Ship detection method based on attention guidance and multi-sample decision making

Single-stage target detection methods have the characteristics of fast training speed and short detection time. However, its feature pyramid network is difficult to suppress the background and noise information of SAR ship images, and the detection head has prediction errors. To address this problem, this paper proposes a detection model based on attention guidance and multi-sample decision for synthetic aperture radar ship detection. Firstly, an attention guidance network is proposed and added to the highest level of the feature pyramid to suppress background and noise interference, thereby improving the representation ability of features. Secondly, a multi-sample decision network is proposed to participate in the prediction of target position. This network alleviates the impact of prediction errors on detection results by increasing the number of samples output in the regression branch. Finally, a novel maximum likelihood loss function is designed. This loss function constructs a maximum likelihood function using the samples output from the multi-sample decision network, which is used to standardize the training of the decision network and further improve the accuracy of target positioning. Taking the RetinaNet network model as the baseline method, compared with the baseline method and the current advanced target detection methods, this method shows the highest detection accuracy on the ship detection dataset SSDD, with AP reaching 52.8%. Compared with the baseline method, the proposed method improves the AP evaluation index 3.4% ∼ 5.7%, and the training parameter Params only increases by 2.03 M, and the frame rate FPS only decreases 0.5Iter/s.

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

On-orbit Moving Target Detection Method Based on Dual Images from Taijing-IV 01 Satellite

Abstract. With the evolution of satellite video technology, the domain has garnered increasing attention. Concurrently, advancements in deep learning have yielded numerous outcomes in target detection. This paper introduces a novel method for detecting moving targets, offering a broader detection range compared to traditional satellite video techniques, facilitating orbital target recognition from dual panchromatic image strips. Our experimental setup on the Taijing-IV 01 satellite, launched on February 27, 2022, successfully acquired two image strips separated by one second. These strips contain speed and directional information of moving objects, extractable through the frame differencing technique. We propose combining frame differencing with lightweight deep learning for target detection, extracting regions of interest (ROIs) to focus on areas with potential moving targets. This approach reduces the workload of wholeimage target detection, decreasing data processing volume by 89%. By optimizing the YOLOv8 network and using techniques like feature map fusion of low-level and high-resolution features, we enhance sensitivity to small targets. Consequently, the model size is reduced by 79%, the mean Average Precision (mAP) increases by approximately 1.8% and 4.5%, and detection speed rises by 26%. This method introduces a new paradigm in remote sensing data services, facilitating rapid acquisition and real-time transmission of positions and image information of moving targets to the ground. This significantly reduces bandwidth requirements for transmitting remote sensing information, presenting a novel strategy for data acquisition and processing in large-scale Earth observation systems and geoscientific applications.

Image Captioning Based on Semantic Scenes.

With the development of artificial intelligence and deep learning technologies, image captioning has become an important research direction at the intersection of computer vision and natural language processing. The purpose of image captioning is to generate corresponding natural language descriptions by understanding the content of images. This technology has broad application prospects in fields such as image retrieval, autonomous driving, and visual question answering. Currently, many researchers have proposed region-based image captioning methods. These methods generate captions by extracting features from different regions of an image. However, they often rely on local features of the image and overlook the understanding of the overall scene, leading to captions that lack coherence and accuracy when dealing with complex scenes. Additionally, image captioning methods are unable to extract complete semantic information from visual data, which may lead to captions with biases and deficiencies. Due to these reasons, existing methods struggle to generate comprehensive and accurate captions. To fill this gap, we propose the Semantic Scenes Encoder (SSE) for image captioning. It first extracts a scene graph from the image and integrates it into the encoding of the image information. Then, it extracts a semantic graph from the captions and preserves semantic information through a learnable attention mechanism, which we refer to as the dictionary. During the generation of captions, it combines the encoded information of the image and the learned semantic information to generate complete and accurate captions. To verify the effectiveness of the SSE, we tested the model on the MSCOCO dataset. The experimental results show that the SSE improves the overall quality of the captions. The improvement in scores across multiple evaluation metrics further demonstrates that the SSE possesses significant advantages when processing identical images.

Visual Communication Method of Multi-Frame Film and Television Special Effects Images Based on Deep Learning

In the view of art form change, there is a perspective problem in the detail part of multi-frame film and television special effects (TSEs) images in the special effects rendering module, which leads to the greater ambiguity of multi-frame film and TSEs images. In order to advance the talent of identifying the detail structures and characteristics of multi-frame film and TSEs images, a visual communication technology of multi-frame film and TSEs images in the view of art form change grounded on deep learning is suggested. The transmission relation model of detail characteristics of multi-frame film and TSEs images in the field of artistic form change is constructed, and the attenuation treatment of detail action special effects of multi-frame film and TSEs images in the field of artistic form change is carried out with the assistance of deep learning approaches. The detailed information and color of multi-frame film and TSEs images are obtained by using prior map knowledge instead of rendering basic color. Over the technique of filtering and preserving, the multi-frame film and TSEs images are directed to screen the detailed feature quantity of the input film and TSEs images in the field of artistic form change. In addition, the Sobel operator is practiced to perceive the scrawny edge information of multi-frame film and TSEs images, and block fitting interpolation and depth filtering analysis are used in the edge area to realize the visual communication and color feature parameter identification and amplification of multi-frame film and TSEs images in the field of artistic form change, so as to improve the fuzzy enhancement of multi-frame film and TSEs images. The test results and results indicate that the average Peak signal-to-noise ratio (PSNR) of the proposed method is about 37.7166[Formula: see text]dB, which is 49.08% and 52.08% higher than the PSNRs of the Harris method and SUFR method, respectively. It has been confirmed that this scheme can successfully solve the edge blurring and distortion problems of multi-frame movies and image images in the field of art form change.

Multi-wing chaotic system based on meminductor and its application in image encryption

Meminductor is a novel type of nonlinear device following the memristor, characterized by its memory properties. Currently, research on meminductors is still in its infancy, with their physical devices yet to be formally realized. Therefore, conducting fundamental research on their nonlinear circuit properties and applications is of great significance. In this paper, a new multi-wing chaotic system is proposed based on the mathematical model of a magnetically controlled meminductor. By varying the values of its parameters, the system can generate two-wing, three-wing, and four-wing chaotic attractors. Various analytical methods are employed to study the dynamical behaviours of the proposed chaotic system. The results demonstrate that the system is highly sensitive to its initial conditions and control parameters, which makes it suitable for image encryption. Based on the new system, we propose a new algorithm for image encryption that combines the newly established four-dimensional multi-wing chaotic system with bit plane decomposition technique, firstly, the high four-bit planes containing 94% image information are disordered by S-type permutation, then the disordered bit planes perform operation of XOR with the random matrix generated by chaotic sequences, and finally, the encrypted image is obtained by merging the bit planes.

Multiple-image authentication method based on phase-only holograms and a logistic map

An interesting security method for a multiple-image authentication scheme is proposed based on computer-generated holograms and a logistic map. First, each original image is encoded as the complex-valued hologram under the point light source model. The resulting hologram is then converted to a phase-only hologram using the Floyd-Steinberg dithering algorithm. Second, each phase-only hologram is randomly sampled with the aid of a binary mask. Through the catenation of all selected pixels, a phase-only pixel sequence is formed. Finally, a non-periodic and non-converging sequence generated with the logistic map is used to scramble this sequence. After only preserving the phase data of the scrambled sequence, the real-valued ciphertext carrying the information of all original images is obtained. In the process of authentication, although no valid information can be discerned from noisy reconstructed images at a small sampling rate, the verification of original images can be efficiently accomplished using the nonlinear correlation maps. Besides binary masks, the parameters of the logistic map are served as secret keys. Due to their high sensitivity, the security of the proposed method is greatly enhanced. The proposed authentication mechanism has been demonstrated to be effective and robust through experiments. To our knowledge, it is the first time to implement multiple-image authentication using phase-only holograms, which can provide a new perspective for optical information security.

Application of Cryptography and Steganography Techniques to Improving the Security of Text Messages with RC4 Algorithm and MSB Method

This study discusses the application of cryptography and steganography techniques to improve the security of text messages using the Rivest Code 4 (RC4) algorithm and the Most Significant Bit (MSB) method. In the ever-growing digital era, data security is a top priority due to the increasing threat of cybercrime that can harm many parties. RC4 is a cryptography algorithm known for its encryption and decryption speed, while the MSB method is an effective steganography technique for hiding information in digital images. This study aims to develop an application that is able to encrypt text messages with the RC4 algorithm and hide them in digital images using the MSB method. With this combination, data is not only encrypted but also hidden, thus providing two layers of security to protect information from unauthorized access. The results of the study show that the combination of RC4 cryptography and MSB steganography techniques successfully improves data security well. The developed application is able to protect sensitive information from the risk of data theft and cyber attacks. In addition, this technique is also easy to implement and can be applied in various sectors, such as banking, health, and business communications, to protect sensitive data from unauthorized access. Keywords: Cryptography, Steganography, Most Significant Bit (MSB)

Digital Image Security Analysis using Hill Cipher and AES Algorithm

In today's digital era, digital image exchange has become very common in various industries, but this also increases security risks such as counterfeiting, image manipulation, and information theft. To protect the confidentiality of information in digital images, encryption is a fairly effective method. Hill Cipher, as a classic cryptographic method, offers matrix-based encryption, while Advanced Encryption Standard (AES) is known for its high level of security and efficiency. By combining Hill Cipher and AES, encryption systems can leverage the strengths of classic and modern cryptography together, providing an additional layer of protection that strengthens the security of digital data and reduces vulnerabilities that may exist in each method separately. This approach provides a more comprehensive solution for maintaining the confidentiality of digital images in the context of evolving security threats.

Effects of Providing Moving Image Information During Driving on Drivers’ Visual and Driving Behaviors

Effects of Providing Moving Image Information During Driving on Drivers’ Visual and Driving Behaviors

The Analysis of Smarter Future

With the continuous development of technology, smart home control systems, as one of the applications of artificial intelligence in daily life, are receiving increasing attention. However, smart home control systems face challenges in practical applications, such as complex scenarios and multimodal information. In this context, this paper introduces Visual Question Answering (VQA) technology to enhance the intelligence and user experience of smart home control systems. Despite the potential advantages of VQA technology in smart home control systems, current models still have certain shortcomings in handling local image information and integrating visual-language multimodal features. To address this, this paper proposes an innovative Transformer-based Multimodal Fusion Network (TMFNet) model. TMFNet aims to overcome the limitations of existing models in dealing with complex smart home scenarios by introducing a global-local feature attention mechanism, deep encoding-decoding modules, and multimodal representation modules.

Infrared image super-resolution reconstruction based on visible light image guidance and recursive fusion

Due to the limitations of hardware equipment, infrared images generally have problems such as low resolution, blurred details and poor visual quality during acquisition. Using visible light images to guide the super-resolution reconstruction of infrared images is an effective way to improve the resolution of infrared images. However, the imaging principles of visible light images and infrared images are different, resulting in differences in detail information between the two images, so problems such as blur and ghosting may occur during reconstruction. This paper proposes an infrared image super-resolution network based on visible light image guidance and recursive fusion. In this network, a flow Fourier residual module is designed, and modules of different depths are used to extract information of different frequencies in visible light images and infrared images, so that each module focuses on the appropriate frequency information. At the same time, a hybrid attention module is used to obtain detail information in multimodal images from channel and spatial perspectives, and fuse them in a complementary way, which helps to eliminate the generation of artifacts. On this basis, a global recursive fusion branch is designed to consider the correlation between multi-layer features, adaptively fuse multi-layer features, and generate clearer high-resolution infrared images. Experimental results show that compared with the comparative methods, this method performs better in objective evaluation indicators; in terms of subjective visual comparison, the images reconstructed by this method have clearer textures and fewer artifacts, and better object distinction in complex environments.

A method for extracting Martian terrain features based on unsupervised contrastive learning

Intelligent recognition of Martian surface terrain is of great significance to autonomous exploration of Mars rovers. Feature extraction methods for Martian terrain images are currently mainly divided into two categories: traditional shallow visual feature extraction and deep feature extraction based on supervised learning. Loss of image information and the need for a large amount of labeled data are key issues to be addressed. A method for Martian terrain feature recognition based on unsupervised contrastive learning is proposed. By establishing an image dictionary dataset, two sets of neural networks, "query" and "encoding", are used to compare a single image with other images in the "dictionary" dataset, and the network is trained using the similarity functional as the loss function, thereby realizing feature recognition of Martian terrain images. The proposed method also has the ability to recognize new types of terrain images outside the training dataset, and has outstanding superiority in subsequent recognition and classification. Computer simulation verification shows that the recognition accuracy of the proposed method is and the 85.4%recognition accuracy of new types of terrain images is 84.5%.