Background subtraction based on Local Shape

We present a novel approach to background subtraction that is based on the local shape of small image regions. In our approach, an image region centered on a pixel is mod-eled using the local self-similarity descriptor. We aim at obtaining a reliable change detection based on local shape change in an image when foreground objects are moving. The method first builds a background model and compares the local self-similarities between the background model and the subsequent frames to distinguish background and foreground objects. Post-processing is then used to refine the boundaries of moving objects. Results show that this approach is promising as the foregrounds obtained are com-plete, although they often include shadows.Comment: 4 pages, 5 figures, 3 tabl

Facial Component Detection in Thermal Imagery

This paper studies the problem of detecting facial components in thermal imagery (specifically eyes, nostrils and mouth). One of the immediate goals is to enable the automatic registration of facial thermal images. The detection of eyes and nostrils is performed using Haar features and the GentleBoost algorithm, which are shown to provide superior detection rates. The detection of the mouth is based on the detections of the eyes and the nostrils and is performed using measures of entropy and self similarity. The results show that reliable facial component detection is feasible using this methodology, getting a correct detection rate for both eyes and nostrils of 0.8. A correct eyes and nostrils detection enables a correct detection of the mouth in 65% of closed-mouth test images and in 73% of open-mouth test images

Attribute-Graph: A Graph based approach to Image Ranking

We propose a novel image representation, termed Attribute-Graph, to rank images by their semantic similarity to a given query image. An Attribute-Graph is an undirected fully connected graph, incorporating both local and global image characteristics. The graph nodes characterise objects as well as the overall scene context using mid-level semantic attributes, while the edges capture the object topology. We demonstrate the effectiveness of Attribute-Graphs by applying them to the problem of image ranking. We benchmark the performance of our algorithm on the 'rPascal' and 'rImageNet' datasets, which we have created in order to evaluate the ranking performance on complex queries containing multiple objects. Our experimental evaluation shows that modelling images as Attribute-Graphs results in improved ranking performance over existing techniques.Comment: In IEEE International Conference on Computer Vision (ICCV) 201

WxBS: Wide Baseline Stereo Generalizations

We have presented a new problem -- the wide multiple baseline stereo (WxBS) -- which considers matching of images that simultaneously differ in more than one image acquisition factor such as viewpoint, illumination, sensor type or where object appearance changes significantly, e.g. over time. A new dataset with the ground truth for evaluation of matching algorithms has been introduced and will be made public. We have extensively tested a large set of popular and recent detectors and descriptors and show than the combination of RootSIFT and HalfRootSIFT as descriptors with MSER and Hessian-Affine detectors works best for many different nuisance factors. We show that simple adaptive thresholding improves Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them on infrared and low contrast images. A novel matching algorithm for addressing the WxBS problem has been introduced. We have shown experimentally that the WxBS-M matcher dominantes the state-of-the-art methods both on both the new and existing datasets.Comment: Descriptor and detector evaluation expande