Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images. The presented literature review focuses on classifying images into treated and untreated areas. The high computation burden to process a given image frame makes it unsuitable for real-time system applications. In addition, the considered current methods do not provide a quantitative assessment of the properties of the treated surfaces. The markup, processed, and untreated surfaces are explored based on the entropy criterion of information showing the randomness disorder of an already treated surface. However, the absence of an explicit indication of the magnitude of the irregularities carries a dependence on the lighting conditions, not allowing to explicitly specify such characteristics in the system. Moreover, due to the requirement of the mandatory use of specific area data, regarding the size of the cavities, the work is challenging in evaluating the average frequency of these cavities. Therefore, an algorithm is developed for finding the period of determining the quality of metal surface treatment, taking into account the porous matrix, and the complexities of calculating the surface tensor. Experimentally, the results of this work make it possible to effectively evaluate the quality of the treated surface, according to the criterion of the size of the resulting irregularities, with a frame processing time of 20 ms, closely meeting the real-time requirements.

To enhance public safety, crowd detection and prevention systems have essentially become a natural means to manage diverse crowded areas, such as urban settings, transportation hubs, and event venues. Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. Additionally, image processing tools play a key role in real-time monitoring by analyzing video feeds to detect crowd density, flow direction, and identify potential risks like overcrowding or emergencies. However, most existing solutions focus on the detection phase and often overlook integrated error handling and robust decision-making frameworks to ensure accurate and actionable crowd prevention. Aiming to solve these issues, we take advantage of the prediction capabilities of machine learning models and the analysis and clustering strengths of Formal Concept Analysis (FCA) chosen for its strong mathematical foundation and superior clustering capabilities compared to traditional methods, as highlighted in recent works such as K-means or hierarchical clustering. We used the first technique to extract useful knowledge from areas’ produced images while mitigating potential error accumulation through modular error-checking mechanisms. A neural network is used to mark human bodies, determine the position of walking individuals, and predict crowd levels. Such information is, thereafter, inputted to the FCA-based decision system to ensure an explicit representation and modelling of crowd data, thanks to lattice structures. These latter’s hierarchical view helped us identify the crowded areas and manage them as clustered zones, based on their common crowd information. We also define bottom-up parsing algorithms to recommend the suitable crowd prevention plan w.r.t. the crowd level. Experiments have successfully proved the ability of FCA to exclude low-crowd zones, locate crowded areas, and provide actionable crowd management insights, which may complement crowd counting techniques.

We propose a novel application adaptive (multi-mode) headway-based broadcasting protocol that is most appropriate for public-safety applications in VANETs. Being headway-based, the proposed protocol possesses unique robustness at different speeds and traffic volumes. Moreover, it addresses application differences with a new multi-mode feature. The logic behind the new concepts/approaches adopted in the protocol and their merits are highlighted, supported with analysis and simulation.

The great advance in image processing demands a guarantee for assuring integrity of images. In this paper, a semi-fragile image authentication technique is proposed, which can detect and localize the malicious alteration distinguishing the content-preserving manipulations, such as JPEG compression, from the content alterations. The original image is divided into large blocks (sub-images) that are also divided into 8 × 8 blocks. Secure image hashing is utilized to generate the sub-image hash (signature) that may slightly change when the content-preserving manipulations are applied. Then, two sub-image hash copies are embedded into relatively-distant sub-images using a doubly linked chain, which prevents the vector quantization (VQ) attack. The hash bits are robustly embedded in chosen discrete cosine transform (DCT) coefficients exploiting a property of DCT coefficients that is invariant before and after JPEG compression. The experimental results show that the proposed technique successfully detects and localizes the content alterations only. Furthermore, It can effectively resist many attacks such as vector quantization attacks.