A Comparative Experiment of Several Shape Methods in Recognizing Plants

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.Comment: 8 pages; International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 3, June 201

Experiments of Distance Measurements in a Foliage Plant Retrieval System

One of important components in an image retrieval system is selecting a distance measure to compute rank between two objects. In this paper, several distance measures were researched to implement a foliage plant retrieval system. Sixty kinds of foliage plants with various leaf color and shape were used to test the performance of 7 different kinds of distance measures: city block distance, Euclidean distance, Canberra distance, Bray-Curtis distance, x2 statistics, Jensen Shannon divergence and Kullback Leibler divergence. The results show that city block and Euclidean distance measures gave the best performance among the others.Comment: 14 pages, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 5, No. 2, June, 201

Fruit detection with binary partition trees (İkili bölüntü ağaçları ile meyve tespiti)

In this study, binary partition trees are applied to the problem of fruit detection. The fact that binary partition trees are inherently unbiased and independent of flatzones is the main reason for this application. Using only circularity from the shape priors, this system is put to test with 39 images of three classes of fruits and the test results show an average of 0.669 precision and 0.851 recall

Classify the plant species based on lobes, sinuses and margin

This paper proposed a novel approach to cluster the species of plants based on their lobes, sinuses and margin. Firstly, all the boundary points in the clockwise or anticlockwise direction were selected. Then, an estimated centre point for leaf boundary points was used to compute the distance between the leaf boundary points and centre point. Next, the peaks and valleys from the distance found stated above were located where peaks represent lobes and valleys represent the sinuses. The number of peak and valleys is calculated to cluster the plant according to the rule-based method. From the results obtained, the accuracy for the plant clustering is up to 100 percent