A new automatic approach to seed image analysis: From acquisition to segmentation

Image Analysis offers a new tool for classifying vascular plant species based on the morphological and colorimetric features of the seeds, and has made significant contributions in systematic studies. However, in order to extract the morphological and colorimetric features, it is necessary to segment the image containing the samples to be analysed. This stage represents one of the most challenging steps in image processing, as it is difficult to separate uniform and homogeneous objects from the background. In this paper, we present a new, open source plugin for the automatic segmentation of an image of a seed sample. This plugin was written in Java to allow it to work with ImageJ open source software. The new plugin was tested on a total of 3,386 seed samples from 120 species belonging to the Fabaceae family. Digital images were acquired using a flatbed scanner. In order to test the efficacy of this approach in terms of identifying the edges of objects and separating them from the background, each sample was scanned using four different hues of blue for the background, and a total of 480 digital images were elaborated. The performance of the new plugin was compared with a method based on double image acquisition (with a black and white background) using the same seed samples, in which images were manually segmented using the Core ImageJ plugin. The results showed that the new plugin was able to segment all of the digital images without generating any object detection errors. In addition, the new plugin was able to segment images within an average of 0.02 s, while the average time for execution with the manual method was 63 s. This new open source plugin is proven to be able to work on a single image, and to be highly efficient in terms of time and segmentation when working with large numbers of images and a wide diversity of shapes.

Fast and accurate computation of orthogonal moments for texture analysis

In this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community's disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six well-known databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification.