Novel nonlinear adaptive composite filters for illumination-invariant pattern recognition are presented. Pattern recognition is carried out with space-variant nonlinear correlation. The information about objects to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a nonlinear adaptive correlation filter with a given discrimination capability. The designed filter during recognition process adapts its parameters to local statistics of the input image. Computer simulation results obtained with the proposed filters in nonuniformly illuminated test scenes are discussed and compared with those of linear composite correlation filters with respect to discrimination capability, robustness to input additive and impulsive noise, and tolerance to small geometric image distortions.

Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS.

Estimation of distance from objects in real‐world scenes is an important topic in several applications such as navigation of autonomous robots, simultaneous localization and mapping (SLAM), and augmented reality (AR). Even though there is a technology for this purpose, in some cases, this technology has some disadvantages. For example, GPS systems are susceptible to interference, especially in places surrounded by buildings, under bridges or indoors; alternatively, RGBD sensors can be used, but they are expensive, and their operational range is limited. Monocular vision is a low‐cost suitable alternative that can be used indoor and outdoor. However, monocular odometry is challenging because the object location can be known up a scale factor. Moreover, when objects are moving, it is necessary to estimate the location from consecutive images accumulating error. This paper introduces a new method to compute the distance from a single image of the desired object, with known dimensions, captured with a monocular calibrated vision system. This method is less restrictive than other proposals in the state‐of‐the‐art literature. For the detection of interest points, a Region‐based Convolutional Neural Network combined with a corner detector were used. The proposed method was tested on a standard dataset and images acquired by a low‐cost and low‐resolution webcam, under noncontrolled conditions. The system was tested and compared with a calibrated stereo vision system. Results showed the similar performance of both systems, but the monocular system accomplished the task in less time.

Nowadays, the deformation measurement in metal sheets is important for industries such as the automotive and aerospace industries during its mechanical stamping processes. In this sense, Digital Image Correlation (DIC) has become the most relevant measurement technique in the field of experimental mechanics. This is mainly due to its versatility and low-cost compared with other techniques. However, traditionally, DIC global image registration implemented in software, such as MATLAB 2018, did not find the complete perspective transformation needed successfully and with high precision, because those algorithms use an image registration of the type “afine” or “similarity”, based on a 2D information. Therefore, in this paper, a DIC initialization method is presented to estimate the surface deformation of metal sheets used in the bodywork automotive industry. The method starts with the 3D points reconstruction from a stereoscopic digital camera system. Due to the problem complexity, it is first proposed that the user indicates four points, belonging to reference marks of a “Circle grid”. Following this, an automatic search is performed among the nearby marks, as far as one desires to reconstruct it. After this, the local DIC is used to verify that those are the correct marks. The results show reliability by reason of the high coincidence of marks in experimental cases. We also consider that the quality of mark stamping, lighting, and the initial conditions also contribute to trustworthy effects.

Las cámaras de seguridad y los sistemas de videovigilancia se han convertido en infraestructuras sumamente importantes para garantizar la seguridad de los ciudadanos. Sin embargo, el problema de inseguridad sigue en crecimiento debido en gran parte al fácil acceso a armas de fuego tipo pistola, la detección temprana de este tipo de armas es de suma importancia para ayudar a prevenir accidentes. El objetivo de este trabajo es implementar un sistema de visión estereoscópica que sea capaz de detectar en tiempo real este tipo de objetos con alta confianza ademas de lograr definir la distancia a la que se encuentra dicho objeto. Para dicha detección se implementó una arquitectura de redes neuronales convolucionales (CNN) con un algoritmo tipo YOLO, empleando aprendizaje transferido, junto con un algoritmo para la estimación estereoscópica de la distancia. El sistema presentado funciona con un valor de Intersección sobre unión (IoU) de 0.6 en el cual el se logró una precisión de 92.2 % además, el error promedio detectado en la estimación de distancia hasta un máximo de 3 metros es de 9.3 centimetros.

La conservación de ecosistemas en zonas costeras debe contemplarse dentro de un marco sustentable con las actividades antropogénicas. Es relevante cuantificar el impacto que generan agentes externos, por lo que el objetivo de este trabajo es implementar un método para el monitoreo de la degradación en superficie de playa y vegetación litoral adyacente. Fueron recabadas capturas de imágenes aéreas de zonas costeras protegidas, obtenidas periódicamente mediante un vehículo drone. Se integró un dataset que contempla todas las fases estacionales y distingue 5 clases para su monitoreo: Mangle, vegetación rastrera, arena, mar y cerro-planicie. Para la segmentación semántica se implementaron y compararon distintas arquitecturas de redes neuronales convolucionales (CNN) empleando aprendizaje transferido. Los resultados han sido robustos en la clasificación, alcanzando una precisión global del 93.9% y entre 89.9-95.8% en las clases individuales. En la métrica Intersección sobre Unión, IoU, el rango fue entre 86.6-92.7%. En la detección de cambio son utilizadas series temporales para el monitoreo de clases. Este método ha sido aplicado al caso de estudio de la playa Ensenada Grande en el Parque Nacional Archipiélago Espíritu Santo.