Combining Top-down and Bottom-up Visual Saliency for Firearms Localization

Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless information and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the people\u2019s face position. This model has been created by analyzing information from of a public available database of movie frames representing actors holding firearms

Image and Information Fusion Experiments with a Software-Defined Multi-Spectral Imaging System for Aviation and Marine Sensor Networks

The availability of Internet, line-of-sight and satellite identification and surveillance information as well as low-power, low-cost embedded systems-on-a-chip and a wide range of visible to long-wave infrared cameras prompted Embry Riddle Aeronautical University to collaborate with the University of Alaska Arctic Domain Awareness Center (ADAC) in summer 2016 to prototype a camera system we call the SDMSI (Software-Defined Multi-spectral Imager). The concept for the camera system from the start has been to build a sensor node that is drop-in-place for simple roof, marine, pole-mount, or buoy-mounts. After several years of component testing, the integrated SDMSI is now being tested, first on a roof-mount at Embry Riddle Prescott. The roof-mount testing demonstrates simple installation for the high spatial, temporal and spectral resolution SDMSI. The goal is to define and develop software and systems technology to complement satellite remote sensing and human monitoring of key resources such as drones, aircraft and marine vessels in and around airports, roadways, marine ports and other critical infrastructure. The SDMSI was installed at Embry Riddle Prescott in fall 2016 and continuous recording of long-wave infrared and visible images have been assessed manually and compared to salient object detection to automatically record only frames containing objects of interest (e.g. aircraft and drones). It is imagined that ultimately users of the SDMSI can pair with it via wireless to browse salient images. Further, both ADS-B (Automatic Dependent Surveillance-Broadcast) and S-AIS (Satellite Automatic Identification System) data are envisioned to be used by the SDMSI to form expectations for observing in future tests. This paper presents the preliminary results of several experiments and compares human review with smart image processing in terms of the receiver-operator characteristic. The system design and software are open architecture, such that other researchers are encouraged to construct and participate in sharing results and networking identical or improved versions of the SDMSI for safety, security and drop-in-place scientific image sensor networking

Exploiting Photographic Style for Category-Level Image Classification by Generalizing the Spatial Pyramid

International audienceThis paper investigates the use of photographic style for category-level image classi cation. Speci cally, we exploit the assumption that images within a category share a similar style de ned by attributes such as colorfulness, lighting, depth of eld, viewpoint and saliency. For these style attributes we create correspondences across images by a generalized spatial pyramid matching scheme. Where the spatial pyramid groups features spatially, we allow more general feature grouping and in this paper we focus on grouping images on photographic style. We evaluate our approach in an object classi cation task and investigate style differences between professional and amateur photographs. We show that a generalized pyramid with style-based attributes improves performance on the professional Corel and amateur Pascal VOC 2009 image datasets

Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering

In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multiscale information fusion based on the original image, the image at the final scale at which the diffusion process converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for image classification. The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, with high classification rates