Name
Abstract | ||
|---|---|---|
In computer vision research, the first most important step is to represent the captured object into some mathematical transformed feature vector describing the proper shape, texture and/or color information for the classification. To understand the nature's biodiversity, together with computer vision (CV), the emerging ubiquitous mobile technologies are now used. Therefore, in this paper, a novel low computational, efficient, and accurate rotation-scale-translation invariant shape profile transform called Angle View Projection (AVP) is proposed. The leaf images captured via mobile devices are transformed to an AVP shape profile curve (a set of four shapelets) and then compacted using Discrete Cosine Transform (DCT) to improve the performance of the system. It also reduces the energy consumption of the device. The algorithm is tested on five different types of leaf datasets: Flavia dataset, 100 plant species leaves dataset, Swedish database, Intelligent Computing Laboratory leaf dataset and Diseased leaf dataset. An `Agent' on mobile device decides whether the module needs to offload to the Server or to compute on the device itself. The experiments carried out clearly indicates that the proposed system outperforms the state-of-the-art with a fast response time even in a low vision environment. AVP also outperforms other methods when tested over incomplete leaves caused due to the physiological or pathological phenomenon. This AVP shape profile based mobile plant biometric system is developed for general applications in our society to better understand the nature and helps in botanical studies and researches. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-016-3309-2 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Mobile plant biometric system, Low vision, Shape profile curve, Discrete Cosine Transform (DCT), k-NN, Angle View Projection (AVP) | Mobile technology,Computer vision,Feature vector,Pattern recognition,Computer science,Discrete cosine transform,Response time,Mobile device,Invariant (mathematics),Artificial intelligence,Low vision,Energy consumption | Journal |
Volume | Issue | ISSN |
76 | 5 | 1573-7721 |
Citations | PageRank | References |
6 | 0.55 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shitala Prasad | 1 | 55 | 7.28 |
| Sateesh Kumar Peddoju | 2 | 28 | 1.61 |
| Debashis Ghosh | 3 | 496 | 49.16 |