Conventional tracking approaches assume proximity in space, time and appearance of objects in successive observations. However, observations of objects are often widely separated in time and space when viewed from multiple non-overlapping cameras. To address this problem, we present a novel approach for establishing object correspondence across non-overlapping cameras. Our multi-camera tracking...

Local binary pattern (LBP) is an effective texture descriptor which has successful applications in texture classification and face recognition. Many extensions are made for conventional LBP descriptors. One of the extensions is dominant local binary patterns which aim at extracting the dominant local structures in texture images. The second extension is representing LBP descriptors in Gabor tra...

The design of low-level image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT [16] and HOG [3], are the most successful and popular features for visual object and scene recognition. We highlight the kernel view of orientation histograms, and show that they are equivalent to a certain type of match kernels over image patches. This novel view all...

Buildings’ segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning-based methods literature can be applied to fixed or narrow-range spatial resolution imagery. In practical scenarios, users deal with broad spectrum image resolutions. Thus, given often needs re-sampled match dataset used train learning model, which results deg...

Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features includi...

We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of tensors. They consume multigrid inputs and produce multigrid outputs; convolutional filters themselves have both within-scale and cross-scale extent. This aspect is distinct from simple ...

Abstract In crowded scenes, one of the most important issues is that heavily overlapped objects are hardly distinguished from each other since their pixels shared and visible occluded objects, which used to represent features, limited. this paper, a spatial pyramid convolutional shuffle (SPCS) module proposed extract refined information limited generate distinguishable representations for objec...

The spatial organization of ecological units that constitute river floodplains was modeled using a recent image synthesis method. The model combined a purely spatial process, the stochastic pyramid, with analytical descriptions of ecological successions observed on each landform of the fluvial landscape. Because the stochastic pyramid is a generator of non-homogeneous, anisotropic, multicolored...