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

Color Bands Detection on a Gel Electrophoresis Image in one Dimension Applying a Location Algorithm Based on Maximums and Minimums

En este artículo se escribe una metodología desarrollado en MATLAB para el procesamiento de imágenes de electroforesis en gel de una dimensión las cuales contienen información de antígenos de insectos (Triatoma Dimidiata o Pito) y de parásitos (Fasciola). La metodología propuesta permite complementar la interpretación visual de las imágenes basándose en técnicas de contraste, realce de cada una de las bandas y la implementación de filtros para eliminar discontinuidades; adicionalmente se propone un algoritmo de máximos y mínimos para la identificación automática de cada una de las bandas. Como resultado final se obtuvieron 5 imágenes a color, ubicando e identificando con una eficiencia del 100% las bandas características de la electroforesis en gel en comparación con sus patrones originales, complementado con un análisis cuasi-cuantitativo de la cantidad de muestra de cada una de ellas en relación con la banda con mayor concentración.This article describes a developed methodology on MATLAB for the images processing of the gel electrophoresis image in one dimension which contain information of insect antigens (Triatoma Dimidiata or Pito) and parasites (Fasciola). The proposed methodology permits to complement the visual interpretation of the images basing on contrast technics, heighten each of the bands and the implementation of filters to eliminate discontinuities; additionally, it’s proposed an algorithm of maximums and minimums for the automatic identification of each band. As a final result it’s obtained 5 color images, locating with a 100% efficiency the characteristic bands of the gel electrophoresis in comparison with their original patterns, complementing with a quasi-quantitative analysis of the sample amount of each of them in relation with the highest concentration band

An Efficient and Self-Adapted Approach to the Sharpening of Color Images

An efficient approach to the sharpening of color images is proposed in this paper. For this, the image to be sharpened is first transformed to the HSV color model, and then only the channel of Value will be used for the process of sharpening while the other channels are left unchanged. We then apply a proposed edge detector and low-pass filter to the channel of Value to pick out pixels around boundaries. After that, those pixels detected as around edges or boundaries are adjusted so that the boundary can be sharpened, and those nonedge pixels are kept unaltered. The increment or decrement magnitude that is to be added to those edge pixels is determined in an adaptive manner based on global statistics of the image and local statistics of the pixel to be sharpened. With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained. Finally, the adjusted channel of Value and that of Hue and Saturation will be integrated to get the sharpened color image. Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches