Stereo matching obtains a depth map called a disparity map that indicates or shows the positions of the objects in a scene. To estimate a disparity map, the most popular trend consists of comparing two images (left‐right) from two different points from the same scene. Unfortunately, small window sizes are suitable to preserve the edges, while large window sizes are required in homogeneous areas. To solve this problem, in this article, a novel real‐time stereo matching algorithm embedded in an FPGA is proposed. The approach consists of estimating disparity maps with different window sizes by using the sum of absolute differences (SAD) as a local correlation metric. Once the disparity maps are obtained, the left‐right consistency for each window size is computed. At the end of this stage, the centre pixel deviation is estimated through a 5 × 5 window and the Sobel gradient is extracted from the left image. Finally, both parameters are processed by a Fuzzy Inference System (FIS), which combines the calculated disparities and generates a final disparity map. An architecture embedded in FPGA is established and hardware acceleration strategies are discussed. Experimental results demonstrated that this algorithmic formulation provides promising results compared with the current state of the art.

Due to the increasing need for continuous use of face masks caused by COVID-19, it is essential to evaluate the filtration quality that each face mask provides. In this research, an estimation method based on thermal image processing was developed; the main objective was to evaluate the effectiveness of different face masks while being used during breathing. For the acquisition of heat distribution images, a thermographic imaging system was built; moreover, a deep learning model detected the leakage percentage of each face mask with a mAP of 0.9345, recall of 0.842 and F1-score of 0.82. The results obtained from this research revealed that the filtration effectiveness depended on heat loss through the manufacturing material; the proposed estimation method is simple, fast, and can be replicated and operated by people who are not experts in the computer field.

Object tracking is the process of estimating in time N the location of one or more moving element through an agent (camera, sensor, or other perceptive device). An important application in object tracking is the analysis of animal behavior to estimate their health. Traditionally, experts in the field have performed this task. However, this approach requires a high level of knowledge in the area and sufficient employees to ensure monitoring quality. Another alternative is the application of sensors (inertial and thermal), which provides precise information to the user, such as location and temperature, among other data. Nevertheless, this type of analysis results in high infrastructure costs and constant maintenance. Another option to overcome these problems is to analyze an RGB image to obtain information from animal tracking. This alternative eliminates the reliance on experts and different sensors, yet it adds the challenge of interpreting image ambiguity correctly. Taking into consideration the aforementioned, this article proposes a methodology to analyze lamb behavior from an approach based on a predictive model and deep learning, using a single RGB camera. This method consists of two stages. First, an architecture for lamb tracking was designed and implemented using CNN. Second, a predictive model was designed for the recognition of animal behavior. The results obtained in this research indicate that the proposed methodology is feasible and promising. In this sense, according to the experimental results on the used dataset, the accuracy was 99.85% for detecting lamb activities with YOLOV4, and for the proposed predictive model, a mean accuracy was 83.52% for detecting abnormal states. These results suggest that the proposed methodology can be useful in precision agriculture in order to take preventive actions and to diagnose possible diseases or health problems.