Person reidentification is the process of matching individuals from images taken of them at different times and often with different cameras. To perform matching, most methods extract features from the entire image; however, this gives no consideration to the spatial context of the information present in the image. We propose using a convolutional neural network approach based on ResNet-50 to predict the foreground of an image: the parts with the head, torso, and limbs of a person. With this information, we use the LOMO and salient color name feature descriptors to extract features primarily from the foreground areas. In addition, we use a distance metric learning technique (XQDA), to calculate optimally weighted distances between the relevant features. We evaluate on the VIPeR, QMUL GRID, and CUHK03 data sets and compare our results against a linear foreground estimation method, and show competitive or better overall matching performance.
KEYWORDS: Image segmentation, Data modeling, Blood vessels, Monte Carlo methods, Distance measurement, Medical imaging, Photography, Statistical modeling, Image processing, Binary data
We present a quantitative method for the comparison of vascular topology and geometry measured from retinal fundus photographs. The measure compares the difference between distributions taken from a graph representation of the vasculature, which is derived by image segmentation. The measure uses the Kullback-Leibler distance between statistical measures on the reference and test segmentations which can be geometrical, like the distribution of vessel widths, or topological, like local connectivity or a combination of the two.
The user is free to build any meaningful description and here we illustrate two local topology measures graphically. Using this assessment method, we also show that our model based segmentation method has better geometrical accuracy than a technique based on matched filtering. We have tested out the measures on a set of 20 images from the STARE project data.
KEYWORDS: Image processing, Signal to noise ratio, Error analysis, Biological research, Digital filtering, Visual communications, Image segmentation, Image filtering, Smoothing, Image processing algorithms and systems
The problem of estimating feature orientation from noisy image data is addressed using a multiresolution technique. It is shown that by an appropriate choice of representation of orientation, it is possible to employ simple linear smoothing methods to reduce estimation noise. The smoothing is done using a combination of scale-space recursive filtering and iterative estimation, giving significant improvements in estimated orientations at low computational cost. Applications to enhancement and segmentation are presented.
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