Omnidirectional Vision Based Topological Navigation

Goedemé T., Van Gool L., ''Omnidirectional vision based topological navigation'', Mobile robots navigation, pp. 172-196, Barrera Alejandra, ed., March 2010, InTech.status: publishe

Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning

Computer vision has almost solved the issue of in the wild face detection, using complex techniques like convolutional neural networks. On the contrary many open source computer vision frameworks like OpenCV have not yet made the switch to these complex techniques and tend to depend on well established algorithms for face detection, like the cascade classification pipeline suggested by Viola and Jones. The accuracy of these basic face detectors on public datasets like FDDB stays rather low, mainly due to the high number of false positive detections. We propose several adaptations to the current existing face detection model training pipeline of OpenCV. We improve the training sample generation and annotation procedure, and apply an active learning strategy. These boost the accuracy of in the wild face detection on the FDDB dataset drastically, closing the gap towards the accuracy gained by CNN-based face detectors. The proposed changes allow us to provide an improved face detection model to OpenCV, achieving a remarkably high precision at an acceptable recall, two critical requirements for further processing pipelines like person identification, etc

Special issue on Advanced Machine Vision Preface by the guest editors

This preface acts as an introduction to the special issue on Advanced Machine Vision. It highlights the goal of this special issue on Advanced Machine Vision as well as discusses the reviewing process. On top of that, it highlights the selected submissions and describes them briefly.status: publishe

A 28-nm Coarse Grain 2D-Reconfigurable Array with Data Forwarding

To answer the ever-increasing demand for computational power, coarse-grain reconfigurable architectures (CGRAs) experience a revival. As they provide efficient, dedicated datapaths to match the dataflow graphs of a wide range of applications, CGRAs successfully manage high performance tasks. Yet, modern CGRAs access data through classical memory hierarchies or global scratchpad memories, failing to take advantage of data locality. This reveals memory bandwidth as a key bottleneck for these reconfigurable architectures. This letter introduces a heterogeneous 2-D CGRA which distributes data buffers across its computational grid. Together with a single-cycle data forwarding path, this allows the CGRA to better take advantage of data locality, in order to minimize data transfers. A 28-nm CMOS instantiation of this concept realizes the mapping and execution of a variety of compute intensive kernels, such as a real-time 512-point FFT or $5\times 5$ convolutional filter at high power efficiency. The CGRA demonstrates a peak energy efficiency of 584.9 GOPS/W during a 103-tap FIR filter, marking a $2.9\times$ improvement over state-of-the-art 2-D CGRAs