Name
Abstract | ||
|---|---|---|
Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TNNLS.2013.2253563 | Neural Networks and Learning Systems, IEEE Transactions |
Keywords | Field | DocType |
computational complexity,field programmable gate arrays,object recognition,xc6vlx240t fpga,xilinx virtex 6 ml605 evaluation board,binary visual object recognition tasks,biologically inspired hmax model,computational vision approach,fast neuromimetic object recognition,hierarchical model and x model,multiclass visual object recognition tasks,recognition accuracy,still images,visual object recognition,hmax | Computer science,Virtex,Artificial intelligence,Hierarchical database model,Computer vision,Computational problem,3D single-object recognition,Pattern recognition,Field-programmable gate array,Pixel,Machine learning,Computational complexity theory,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
abs/1511.00100 | 8 | 2162-2388 |
Citations | PageRank | References |
9 | 0.60 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Orchard, G. | 1 | 24 | 3.77 |
| Jacob G. Martin | 2 | 26 | 3.79 |
| R. Jacob Vogelstein | 3 | 18 | 1.84 |
| Ralph Etienne-Cummings | 4 | 9 | 0.60 |