Visual tracking has long been studied in computer vision, and it has many practical applications such as surveillance and security, traffic control, human-computer interaction, and activity or behavior recognition to mention but a few. Head tracking attempts to follow a person's head within a video sequence. This paper presents a methodology that automatically designs an artificial dorsal stream for the problem of head tracking. Multiple visual operators are synthesized to obtain several visual and conspicuity maps that are fused into a saliency map, which is converted to a binary image, thus defining the proto-object. The automatic design of visual attention programs for the problem of head tracking is achieved through an optimization process following the Darwinian paradigm of artificial evolution. Artificial brains are synthesized using multiple visual operators embedded within a complex hierarchical procedure consisting of several key processes such as center-surround mechanisms, normalization, and pyramid-scale processes. The proposed strategy robustly handles many difficulties including occlusion, distraction, and illumination, and the resulting programs are real-time systems that are able to track a person's head with enough accuracy to automatically control the camera. Extensive experimentation shows that the proposed method outperforms several state-of-the-art methods in the challenging problem of head tracking.

Digital fringe projection profilometry (DFPP) is a widely used technique for full-field, non-contact 3D surface measurement, offering precision from the sub-micrometer-to-millimeter scale depending on system geometry and fringe design. This review provides a consolidated synthesis of advances reported between 2022 and 2025, covering projection and imaging architectures, phase formation and unwrapping strategies, calibration approaches, high-speed implementations, and learning-based reconstruction methods. A central contribution of this review is the integration of these developments within a metrological perspective, explicitly relating phase–height transformation, fringe parameters, system geometry, and calibration to dominant uncertainty sources and error propagation. Recent progress highlights trade-offs between sensitivity, robustness, computational complexity, and applicability to non-ideal surfaces, while learning-based and hybrid optical–computational approaches demonstrate substantial improvements in reconstruction reliability under challenging conditions. Remaining challenges include measurements on reflective or transparent surfaces, dynamic scenes, environmental instability, and real-time operation. The review outlines emerging research directions such as physics-informed learning, digital twins, programmable optics, and autonomous calibration, providing guidance for the development of next-generation DFPP systems for precision metrology.