Analysis of the Leaf Fractal Dimension

The present study deals with the analysis of leaf shapes in terms of fractal geometry with medicinal plant like Hibiscus Leaf, by using the techniques of Image Processing. Fractal analysis has been applied to describe various aspects connected with the complexity of plant morphology. In this work we determined the fractal dimension of leaves for four methods like Prewitt , Sobel , Roberts , Canny . We summarize the different methods that have been developed for estimating the fractal dimension of medicinal leaves. The results are very informative

Branching Boogaloo: Botanical Adventures in Multi-Mediated Morphologies

FormaLeaf is a software interface for exploring leaf morphology using parallel string rewriting grammars called L-systems. Scanned images of dicotyledonous angiosperm leaves removed from plants around Bard’s campus are displayed on the left and analyzed using the computer vision library OpenCV. Morphometrical information and terminological labels are reported in a side-panel. “Slider mode” allows the user to control the structural template and growth parameters of the generated L-system leaf displayed on the right. “Vision mode” shows the input and generated leaves as the computer ‘sees’ them. “Search mode” attempts to automatically produce a formally defined graphical representation of the input by evaluating the visual similarity of a generated pool of candidate leaves. The system seeks to derive a possible internal structural configuration for venation based purely off a visual analysis of external shape. The iterations of the generated L-system leaves when viewed in succession appear as a hypothetical development sequence. FormaLeaf was written in Processing

High Resolution Maps of the Vasculature of An Entire Organ

The structure of vascular networks represents a great, unsolved problem in anatomy. Network geometry and topology differ dramatically from left to right and person to person as evidenced by the superficial venation of the hands and the vasculature of the retinae. Mathematically, we may state that there is no conserved topology in vascular networks. Efficiency demands that these networks be regular on a statistical level and perhaps optimal. We have taken the first steps towards elucidating the principles underlying vascular organization, creating the rst map of the hierarchical vasculature (above the capillaries) of an entire organ. Using serial blockface microscopy and fluorescence imaging, we are able to identify vasculature at 5 μm resolution. We have designed image analysis software to segment, align, and skeletonize the resulting data, yielding a map of the individual vessels. We transformed these data into a mathematical graph, allowing computationally efficient storage and the calculation of geometric and topological statistics for the network. Our data revealed a complexity of structure unexpected by theory. We observe loops at all scales that complicate the assignment of hierarchy within the network and the existence of set length scales, implying a distinctly non-fractal structure of components within

Plant identification using deep convolutional networks based on principal component analysis

Plants have substantial effects in human vitality through their different uses in agriculture, food industry, pharmacology, and climate control. The large number of herbs and plant species and shortage of skilled botanists have increased the need for automated plant identification systems in recent years. As one of the challenging problems in object recognition, automatic plant identification aims to assign the plant in an image to a known taxon or species using machine learning and computer vision algorithms. However, this problem is challenging due to the inter-class similarities within a plant family and large intra-class variations in background, occlusion, pose, color, and illumination. In this thesis, we propose an automatic plant identification system based on deep convolutional networks. This system uses a simple baseline and applies principal component analysis (PCA) to patches of images to learn the network weights in an unsupervised learning approach. After multi-stage PCA filter banks are learned, a simple binary hashing is applied to output maps and the obtained maps are subsampled through max-pooling. Finally, the spatial pyramid pooling is applied to the downsampled data to extract features from block histograms. A multi-class linear support vector machine is then trained to classify the different species. The system performance is evaluated on the plant identification datasets of LifeCLEF 2014 in terms of classification accuracy, inverse rank score, and robustness against pose (translation, scaling, and rotation) and illumination variations. A comparison of our results with those of the top systems submitted to LifeCLEF 2014 campaign reveals that our proposed system would have achieved the second place in the categories of Entire, Branch, Fruit, Leaf, Scanned Leaf, and Stem, and the third place in the Flower category while having a simpler architecture and lower computational complexity than the winner system(s). We achieved the best accuracy in scanned leaves where we obtained an inverse rank score of 0.6157 and a classification accuracy of 68.25%

Creating Knowledge, volume 4, 2011

The College of Liberal Arts and Sciences, through the deliberations and efforts of its task force on “Students Creating Knowledge”, chaired by Professor Ralph Erber, associate dean for research in the College of Liberal Arts and Sciences, committed itself to a number of new strategic initiatives that would enhance and enrich the academic quality of the student experience within the college. Chief among these initiatives was one that would encourage students to become actively engaged in creating scholarship and research and give them a venue for the publication of their essays. The first volume of “Creating Knowledge: The LA&S Student Research Journal” was published in 2008. I am now extremely pleased to be able to introduce the fourth volume of Creating Knowledge. This year’s publication, like the ones that preceded it, gives considerable testimony to the creativity, hard work and sophistication of our undergraduate scholars. It is through the continuing, annual publication of this undergraduate student journal that we aim to encourage students across the college and the university to understand that leadership within their disciplines requires them to not only be familiar with the knowledge base of the discipline, but to have the experience of being actively engaged in understanding how creative work and/or scientific discoveries are created through research, scholarship and the dissemination and sharing of knowledge. I want to congratulate, first and foremost, the many student scholars whose work is featured in this fourth volume of the journal. I also want to thank the students and faculty who served to make this publication possible—those who served on the editorial board that shaped this journal and who reviewed the many submissions of student work. In accomplishing this task all of you have participated in what is the heart of scholarship—the contributions to enabling and sustaining an intellectual community—one which we hope will lead you to make similar contributions beyond the college and DePaul University. To one and all, my most sincere congratulations and gratitude. Chuck Suchar Deanhttps://via.library.depaul.edu/ckgallery/1003/thumbnail.jp

Computer graphics simulation of organic and inorganic optical and morphological appearance changes.

Organic bodies are subject to internal biological, chemical and physical processes as well as environmental interactions after death, which cause significant structural and optical changes. Simulating corpse decomposition and the environmental effects on its surface can help improve the realism of computer generated scenes and provide the impression of a living, dynamic environment. The aim of this doctorate thesis is to simulate post mortem processes of the human body and their visual effects on its appearance. The proposed method is divided into three processes; surface weathering due to environmental activities, livor mortis and natural mummification by desiccation. The decomposing body is modelled by a layered model consisting of a tetrahedral mesh representing the volume and a high resolution triangle surface mesh representing the skin. A particle-based surface weathering approach is employed to add environmental effects. The particles transport substances that are deposited on the object’s surface. A novel, biologically-inspired blood pooling simulation is used to recreate the physical processes of livor mortis and its visual effects on the corpse’s appearance. For the mummification, a physically-based approach is used to simulate the moisture diffusion process inside the object and the resulting de- formations of the volume and skin. In order to simulate the colouration changes associated with livor mortis and mummification, a chemically-based layered skin shader that considers time and spatially varying haemoglobin, oxygen and moisture contents is proposed. The suggested approach is able to model changes in the internal structure and the surface appearance of the body that resemble the post mortem processes livor mortis, natural mummification by desiccation and surface weathering. The surface weathering approach is able to add blemishes, such as rust and moss, to an object’s surface while avoiding inconsistencies in deposit sizes and dis- continuities on texture seams. The livor mortis approach is able to model the pink colouration changes caused by blood pooling, pressure induced blanching effects, fixation of hypostasis and the purple discolouration due to oxygen loss in blood. The mummification method is able to reproduce volume shrinkage effects caused by moisture loss, skin wrinkling and skin darkening that are comparable to real mummies

The development of virtual leaf surface models for interactive agrichemical spray applications

This project constructed virtual plant leaf surfaces from digitised data sets for use in droplet spray models. Digitisation techniques for obtaining data sets for cotton, chenopodium and wheat leaves are discussed and novel algorithms for the reconstruction of the leaves from these three plant species are developed. The reconstructed leaf surfaces are included into agricultural droplet spray models to investigate the effect of the nozzle and spray formulation combination on the proportion of spray retained by the plant. A numerical study of the post-impaction motion of large droplets that have formed on the leaf surface is also considered