17,790 research outputs found
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
In this paper, we employ Probabilistic Neural Network (PNN) with image and
data processing techniques to implement a general purpose automated leaf
recognition algorithm. 12 leaf features are extracted and orthogonalized into 5
principal variables which consist the input vector of the PNN. The PNN is
trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater
than 90%. Compared with other approaches, our algorithm is an accurate
artificial intelligence approach which is fast in execution and easy in
implementation.Comment: 6 pages, 3 figures, 2 table
Automatic Leaf Extraction from Outdoor Images
Automatic plant recognition and disease analysis may be streamlined by an
image of a complete, isolated leaf as an initial input. Segmenting leaves from
natural images is a hard problem. Cluttered and complex backgrounds: often
composed of other leaves are commonplace. Furthermore, their appearance is
highly dependent upon illumination and viewing perspective. In order to address
these issues we propose a methodology which exploits the leaves venous systems
in tandem with other low level features. Background and leaf markers are
created using colour, intensity and texture. Two approaches are investigated:
watershed and graph-cut and results compared. Primary-secondary vein detection
and a protrusion-notch removal are applied to refine the extracted leaf. The
efficacy of our approach is demonstrated against existing work.Comment: 13 pages, India-UK Advanced Technology Centre of Excellence in Next
Generation Networks, Systems and Services (IU-ATC), 201
Dean Diepeveen survey of computer-based vision systems for automatic identification of plant species
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D
shapes for recognition: should 3D shapes be represented with descriptors
operating on their native 3D formats, such as voxel grid or polygon mesh, or
can they be effectively represented with view-based descriptors? We address
this question in the context of learning to recognize 3D shapes from a
collection of their rendered views on 2D images. We first present a standard
CNN architecture trained to recognize the shapes' rendered views independently
of each other, and show that a 3D shape can be recognized even from a single
view at an accuracy far higher than using state-of-the-art 3D shape
descriptors. Recognition rates further increase when multiple views of the
shapes are provided. In addition, we present a novel CNN architecture that
combines information from multiple views of a 3D shape into a single and
compact shape descriptor offering even better recognition performance. The same
architecture can be applied to accurately recognize human hand-drawn sketches
of shapes. We conclude that a collection of 2D views can be highly informative
for 3D shape recognition and is amenable to emerging CNN architectures and
their derivatives.Comment: v1: Initial version. v2: An updated ModelNet40 training/test split is
used; results with low-rank Mahalanobis metric learning are added. v3 (ICCV
2015): A second camera setup without the upright orientation assumption is
added; some accuracy and mAP numbers are changed slightly because a small
issue in mesh rendering related to specularities is fixe
Classification of Plants Using Images of their Leaves
Plant recognition is a matter of interest for scientists as well as laymen. Computer aided technologies can make the process of plant recognition much easier; botanists use morphological features of plants to recognize them. These features can also be used as a basis for an automated classification tool. For example, images of leaves of different plants can be studied to determine effective algorithms that could be used in classifying different plants. In this thesis, those salient features of plant leaves are studied that may be used as a basis for plant classification and recognition. These features are independent of leaf maturity and image translation, rotation and scaling and are studied to develop an approach that produces the best classification algorithm. First, the developed algorithms are used to classify a training set of images; then, a testing set of images is used for verifying the classification algorithms
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