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There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Corners, highly important local features of images and corner finding, play a crucial role in computer vision and image processing, such as object tracking and vehicle detection. Proposing effective and efficient corner detectors is the aim of corner detection. In this study, the authors first present a new measure of corner sharpness termed as the point‐to‐centroid distance (PCD) and then examine its behaviours, which display beneficial characteristics that help distinguish corners from non‐corners. Based on PCD behaviours, the authors propose a novel corner detector. Extensive experimental results demonstrate that the PCD technique is effective and simultaneously efficient for corner detection compared with six other contour‐based corner detectors in terms of two commonly used evaluation metrics – average repeatability and localisation error. [ABSTRACT FROM AUTHOR]

Human face contains abundant shape features. This fact motivates a lot of shape feature‐based face detection and three‐dimensional (3D) face recognition approaches. However, as far as we know, there is no prior low‐level face representation which is purely based on shape feature proposed for conventional 2D (image‐based) face recognition. In this study, the authors present a novel low‐level shape‐based face representation named 'shape primitives histogram' (SPH) for face recognition. In this approach, the face images are separated into a number of tiny shape fragments and they reduce these shape fragments to several uniform atomic shape patterns called 'shape primitives'. Then the face representation is obtained by implementing a histogram statistic of shape primitives in a local image region. To take scale information into consideration, they also produce multi‐scale SPHs (MSPHs) by concatenating the SPHs extracted from different scales. Moreover, they experimentally study the influences of each stage of SPH computation on performance, concluding that a small cell with 1/2 overlap and a fine size block with 1/2 overlap are important for good results. Four popular face databases, namely ORL, AR, YaleB and LFW‐a, are employed to evaluate SPH and MSPH. Surprisingly, such seemingly naive shape‐based face representations outperform the state‐of‐the‐art low‐level face representations. [ABSTRACT FROM AUTHOR]