Search

The existing histogram-based methods for underwater image enhancement are prone to over-enhancement, which will affect the analysis of enhanced images. However, an idea that achieves contrast balance by enhancing and weakening the contrast of an image can address the problem. Therefore, an underwater image enhancement method based on extreme enhancement and ultimate weakening (EEUW) is proposed in this paper. This approach comprises two main steps. Firstly, an image with extreme contrast can be achieved by applying grey prediction evolution algorithm (GPE), which is the first time that GPE is introduced into dual-histogram thresholding method to find the optimal segmentation threshold for accurate segmentation. Secondly, a pure gray image can be obtained through a fusion strategy based on the grayscale world assumption to achieve the ultimate weakening. Experiments conducted on three standard underwater image benchmark datasets validate that EEUW outperforms the 10 state-of-the-art methods in improving the contrast of underwater images. [ABSTRACT FROM AUTHOR]

Machine vision is a non-destructive grading technology and cost-effective method with high accuracy that can be used to predict length, width, and mass, as well as defects of both interior and exterior of a sample by employing different cameras, such as color, multispectral, or hyperspectral cameras. To obtain certain data, which relates to sample quality in the 3D space (thickness, volume, and surface gradient distribution) and mass prediction, a novel method was developed and the obtained appearance quality was graded utilizing a new image processing algorithm for depth images. In this study, we recorded the depth images of 110 potatoes using a depth camera, including samples with uniform shapes or with deformations (e.g., bumps, bent shape, and divots). Length, width, thickness, and volume were calculated respectively, and used as key factors for detecting potato deformity, such as bent shape, bumps, and hollow. Experimental results indicate that mass prediction based on a volume model for both normal and deformed potato samples showed high accuracy, thus 90% of the samples were graded for the correct size group using the volume model. In addition, the appearance quality grading reached 88% of a correct percentage for bent shape, bump, and hollow defect detection by combining the surface data in 2D and 3D space. In addition, a potato virtual reality model rebuilding algorithm was developed for sample quality tracing and rechecking based on 3D shape and color images. This model redisplays the potato color and 3D shape data in multi-views and supports 360-degree rotation in both horizontal and vertical directions to simulate the in-hand examination experience. This depth image processing is an effective potential method for future non-destructive post-harvesting grading, especially for products where size, shape, and surface condition are important factors. [ABSTRACT FROM AUTHOR]

Machine vision based on color, multispectral, and hyperspectral cameras to develop potato quality grading can be used to predict length, width, and mass, as well as defects on the interior and exterior of a sample. However, the images obtained by these cameras are limited by two-dimensional shape information, including width, length, and boundary. Other vital elements of appearance data related to potato mass and quality, including thickness, volume, and surface gradient changes are difficult to detect due to slight surface color differences and device limitations. In this study, we recorded the depth images of 110 potatoes using a depth camera, including samples with uniform shapes or with deformations (e.g., bumps and divots). A novel method was developed for estimating potato mass and shape information and three-dimensional models were built utilizing a new image processing algorithm for depth images. Other features, including length, width, thickness, and volume were also calculated as mass prediction related factors. Experimental results indicate that the proposed models accurately predict potato length, width, and thickness; the mean absolute errors for these predictions were 2.3 mm, 2.1 mm, and 2.4 mm, respectively, while the mean percentage errors were 2.5%, 3.5%, and 4.4%. Mass prediction based on a 3D volume model for both normal and deformed potato samples proved to be more accurate compared to models based on area calculation. Thus 93% of samples were graded by the correct size group using the volume density model while only 73% were graded correctly using the area density. This depth image processing is an effective potential method for future non-destructive post-harvesting grading, especially for products where size, shape, and surface condition are important factors. [ABSTRACT FROM AUTHOR]

Abstract: The image is the important data in the lunar exploration. The main objective of this work is to detection lunar craters with the multi-information fuzzy logic method, and to quantify the image's terrain and the abundance of lunar surface minerals based on crater distribution law and soil characterization consortium data set with lunar surface reflectance. We implement image processing to recognize lunar crater and analysis lunar terrain; use lunar reflectance model to solve mineral reflectance question; joint commonly used look-up lunar reflectance tables and least squares to solve lunar surface reflectance questions; and calculate the minerals reflectance, region reflectance, and then estimate abundance of lunar surface soil minerals. An actual lunar image (Apollo 15 landsite, Clementine mission) of Mare region as an example, this method analysis results of the terrain and mineral's abundance are basically same with published literature. In the future, this method can be simple rapid in-time implemented in real-time lunar exploration. [Copyright &y& Elsevier]