Sistem Temu Kembali Citra Daun Menggunakan Metode Reduced Multi Scale Arch Height (R-march) Pada Smartphone

Metode yang digunakan dalam sistem temu kembali citra daun harus efesien agar dapat berjalan baik pada smartphone yang memiliki sumber daya terbatas. Salah satu metode sistem temu kembali citra daun yang cukup efesien dan dapat diterapkan pada smartphone adalah Multiscale Arch Height (MARCH). MARCH menggunakan karakteristik tinggi lengkungan pada kontur daun sebagai fitur untuk proses temu kembali citra daun. Pada metode MARCH sampel titik pada kontur yang digunakan cukup banyak sehingga masih dimungkinkan mengurangi komputasi metode MARCH dengan cara mereduksi titik acuan Arch Height yang digunakan. Pada penelitian ini diusulkan metode sistem temu kembali citra daun pada smartphone menggunakan metode reduced multi scale arch height (R-MARCH) yang lebih efesien dibanding metode MARCH. Dari percobaan yang sudah dilakukan, didapatkan waktu komputasi yang dibutuhkan metode MARCH adalah 864 milidetik sedangkan R-MARCH 632 milidetik

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

SISTEM TEMU KEMBALI CITRA DAUN MENGGUNAKAN METODE REDUCED MULTI SCALE ARCH HEIGHT (R-MARCH) PADA SMARTPHONE

Metode yang digunakan dalam sistem temu kembali citra daun harus efesien agar dapat berjalan baik pada smartphone yang memiliki sumber daya terbatas. Salah satu metode sistem temu kembali citra daun yang cukup efesien dan dapat diterapkan pada smartphone adalah Multiscale Arch Height (MARCH). MARCH menggunakan karakteristik tinggi lengkungan pada kontur daun sebagai fitur untuk proses temu kembali citra daun. Pada metode MARCH sampel titik pada kontur yang digunakan cukup banyak sehingga masih dimungkinkan mengurangi komputasi metode MARCH dengan cara mereduksi titik acuan Arch Height yang digunakan. Pada penelitian ini diusulkan metode sistem temu kembali citra daun pada smartphone menggunakan metode reduced multi scale arch height (R-MARCH) yang lebih efesien dibanding metode MARCH. Dari percobaan yang sudah dilakukan, didapatkan waktu komputasi yang dibutuhkan metode MARCH adalah 864 milidetik sedangkan R-MARCH 632 milidetik

Advanced shape context for plant species identification using leaf image retrieval

International audienceThis paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach

Fractal analysis of leaf-texture properties as a tool for taxonomic and identification purposes: a case study with species from Neotropical Melastomataceae (Miconieae tribe)

Melastomataceae is a common and dominant family in Neotropical vegetation, with high species diversity which leads to a large variation in some morphological structures. Despite this, some species of Melastomataceae are very similar in their external leaf morphology, leading to difficulties in their identification without the presence of reproductive organs. Here we have proposed and tested a computer-aided texture-based approach used to correctly identify and distinguish leaves of some species of Melastomataceae that occur in a region of Neotropical savanna in Southeastern Brazil, also comparing it with other previously proposed approaches. The results demonstrated that our approach may clearly separate the studied species, analyzing the patterns of leaf texture (both adaxial and abaxial surfaces), and achieving better accuracy (100%) than other methods. Our work has suggested that leaf texture properties can be used as a new characteristic for identification, and as an additional source of information in taxonomic and systematic studies. As the method may be supervised by experts, it is also suitable for discrimination of species with high morphological plasticity, improving the automated discrimination task. This approach can be very useful for identification of species in the absence of reproductive material, and is a rapid and powerful tool for plant identification.CNPq (306628/2007-4; 484474/2007-3)FAPESP (08/57313-2

Modifikasi Metode Multiscale Arch Height (M-MARCH) Untuk Temu Kembali Citra Daun Berdasarlam Bentuk Tepi dan Ruas Tulang Daun

Sistem temu kembali citra daun memiliki peran penting dalam lingkup botani. Saat ini kurang lebih terdapat 400.000 spesies tumbuhan yang telah dikenali di mana 270.000 diantaranya sudah diidentifikasi dan diberi nama. Dengan besarnya jumlah spesies yang ada maka pengenalan spesies tumbuhan secara manual akan sulit dilakukan. Sistem temu kembali citra digital dapat membantu proses pengenalan spesies tumbuhan dengan mengukur kedekatan antara citra kueri dengan citra yang berada pada database. Smartphone merupakan alat komputasi yang ideal untuk penerapan sistem temu kembali citra daun mengingat habitat tumbuhan terdapat pada alam bebas. Salah satu metode yang unggul diterapkan pada smartphone adalah metode Multiscale Arch Height (MARCH), metode ini membutuhkan komputasi yang rendah dan invariant terhadap penskalaan, translasi dan rotasi. MARCH mengekstraksi bentuk daun menjadi vektor fitur yang merupakan kumpulan tinggi lengkungan pada tiap titik pada kontur. Metode MARCH tidak merepresentasikan karakteristik region/intrisik dalam daun, padahal ada beberapa spesies tumbuhan yang mempunyai pola kontur yang hampir sama. Pada penelitian ini dikembangkan metode ekstraksi fitur tepi dan ruas tulang daun yang lebih efektif dengan melakukan modifikasi terhadap metode MARCH. Vektor fitur didapatkan dari tinggi lengkungan suatu titik pada kontur daun, di mana titik tersebut merupakan titik convex hull dari kontur daun serta titik convex hull dari kontur tulang daun. Metode Modifikasi MARCH menghasilkan akurasi yang lebih tinggi dibandingkan metode MARCH karena karakteristik region/intrisik dalam daun juga terakomodasi dengan digunakannya fitur ruas tulang daun. Pengujian metode yang diusulkan dilakukan dengan membandingkan nilai Mean Average Precession (MAP) dan waktu komputasi antara MARCH dengan metode yang diusulkan. Dataset yang digunakan adalah Flavia leaf dataset. Dari penelitian yang sudah dilakukan didapatkan nilai rata – rata MAP untuk metode Modified MARCH lebih tinggi dibanding metode MARCH sebesar 0.74% : 0.70%. sedangkan perbandingan waktu komputasi Modified MARCH sedikit lebih lama dibanding metode MARCH yaitu 1060 ms dibanding 864 ms dengan selisih 196 ms. =============================================================================================== leaves Image retrieval has an important role in the sphere of botany. there are approximately 400,000 plant species where 270,000 are already identified. With the large amount of existing species, manual identification would be difficult to do. Digital image retrieval system can assist the identification by measuring the similarities between query image and the image that is on database. Smartphone is a ideal computational tool for leaves image retrieval system, considering the plants habitat is in the wild. One of the preeminent method applied to the smartphone is MARCH, this method requires a low computation, scale, translation and rotation invariant. MARCH extract shape leaves into a feature vector that consist collection of high curvature at each point of the countur of the leaf. MARCH does not represent region characteristics of the leaves while there are several species of plants have almost the same contour patterns. This research develop a more effective method to extract leaves contour and vein from modification of MARCH methode.The modification is on positioning stages of the arch will be extracted and the addition MARCH application in bone leaves. The feature vector obtained from the high curvature of a point on the contour of the leaves, where a point is a point of the convex hull of the contour of the leaves as well as the convex hull of the point of the bone contour leaves. MARCH modification method is expected to produce a higher accuracy than the method because of the characteristics of the region MARCH / intrinsic in the leaves also accommodated with use features vertebrae leaves. The evaluation is done by comparing the Mean Average value of Precession (MAP) and the computing time between MARCH with the proposed method. This research using Flavia leaf as Datasets. from the evaluation, obtained MAP value for Modified MARCH method is higher than MARCH which comparison is 0.74%: 0.70%. Whereas the Modified MARCH computing time is slightly longer than MARCH (1060 ms compared to 864 ms) with 196 ms difference