2 research outputs found
Lightweight real-time hand segmentation leveraging MediaPipe landmark detection
Producci贸n Cient铆ficaReal-time hand segmentation is a key process in applications that require human鈥揷omputer interaction, such as gesture rec-
ognition or augmented reality systems. However, the infinite shapes and orientations that hands can adopt, their variability
in skin pigmentation and the self-occlusions that continuously appear in images make hand segmentation a truly complex
problem, especially with uncontrolled lighting conditions and backgrounds. The development of robust, real-time hand
segmentation algorithms is essential to achieve immersive augmented reality and mixed reality experiences by correctly
interpreting collisions and occlusions. In this paper, we present a simple but powerful algorithm based on the MediaPipe
Hands solution, a highly optimized neural network. The algorithm processes the landmarks provided by MediaPipe using
morphological and logical operators to obtain the masks that allow dynamic updating of the skin color model. Different
experiments were carried out comparing the influence of the color space on skin segmentation, with the CIELab color space
chosen as the best option. An average intersection over union of 0.869 was achieved on the demanding Ego2Hands dataset
running at 90 frames per second on a conventional computer without any hardware acceleration. Finally, the proposed seg-
mentation procedure was implemented in an augmented reality application to add hand occlusion for improved user immer-
sion. An open-source implementation of the algorithm is publicly available at https://github.com/itap-robotica-medica/light
weight-hand-segmentation.Ministerio de Ciencia e Innovaci贸n (under Grant Agreement No. RTC2019-007350-1)Publicaci贸n en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y Le贸n (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LE脫N, Actuaci贸n:20007-CL - Apoyo Consorcio BUCL
KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering
Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity