Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, limitations on the size of objects being acquired, speed, and portability. This paper presents a novel robotic arm-based system design, which we call LightBot, as well as its applications in reflectance transformation imaging (RTI) in particular. The proposed model alleviates some of the limitations observed in the case of free-form or dome-based systems. It allows the automation and reproducibility of one or a series of acquisitions adapting to a given surface in two-dimensional space.

Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.

This work investigates the use of Reflectance Transformation Imaging (RTI) rendering for visual inspection. This imaging technique is being used more and more often for the inspection of the visual quality of manufactured surfaces. It allows reconstructing a dynamic virtual rendering of a surface from the acquisition of a sequence of images where only the illumination direction varies. We investigate, through psychometric experimentation, the influence of different essential parameters in the RTI approach, including modeling methods, the number of lighting positions and the measurement scale. In addition, to include the dynamic aspect of perception mechanisms in the methodology, the psychometric experiments are based on a design of experiments approach and conducted on reconstructed visual rendering videos. The proposed methodology is applied to different industrial surfaces. The results show that the RTI approach can be a relevant tool for computer-aided visual inspection. The proposed methodology makes it possible to objectively quantify the influence of RTI acquisition and processing factors on the perception of visual properties, and the results obtained show that their impact in terms of visual perception can be significant.

In this article, we define a generic gradient for color and spectral images, considering a proposed taxonomy of the state of the art. A full-vector gradient, taking into account the sensor’s characteristics, is in compliance with the metrological properties of genericity, robustness, and reproducibility. Here, we construct a protocol to compare gradients from different sensors. The comparison is developed by simulating sensors using their spectral characteristics. We develop three experiments using this protocol. The first experiment shows the consistency of results for similar sensors; the second demonstrates the genericity of the approach, adapted to any kind of imaging sensors; and the third focuses on the channel inter-correlation considering sensors such as in the color vision deficiency case.

Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and use chemical products in a confined environment. Those constraints tends to lower control performance and increase stress and fatigue. As a solution, we propose an AI-based assistive machine (called “PARADES”) inside the hazardous environment, remotely manipulated by a human operator, outside of the confined area, in cleaner and safer conditions. This paper focuses on the development of a complete industrial-grade AI machine, both in terms of hardware and software. The result is a standalone assistive AI-based vision system, plug-and-play and controller-friendly, which only needs the usual power supply 230 V plug that detects defects and measures defect length. In conclusion, the PARADES machines address for the first time the problem of occupational health in MPI with an industrial standalone machine which can work on several parts and be integrated into current production lines. Providing cleaner and healthier working conditions for operators will invariably lead to increased quality of detection. These results suggest that it would be beneficial to spread this kind of AI-based assistive technology in NDT, in particular MPI, but also in Fluorescent Penetrant Testing (FPT) or in visual inspection.

Gradient extraction is important for a lot of metrological applications such as Control Quality by Vision. In this work, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.