67 research outputs found
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the
relationship between template matching and the recent popular correlation
filter based trackers (CFTs). Compared to the correlation operation in CFTs, a
sophisticated similarity metric termed "mutual buddies similarity" (MBS) is
proposed to exploit the relationship of multiple reciprocal nearest neighbors
for target matching. By doing so, our tracker obtains powerful discriminative
ability on distinguishing target and background as demonstrated by both
empirical and theoretical analyses. Besides, instead of utilizing single
template with the improper updating scheme in CFTs, we design a novel online
template updating strategy named "memory filtering" (MF), which aims to select
a certain amount of representative and reliable tracking results in history to
construct the current stable and expressive template set. This scheme is
beneficial for the proposed tracker to comprehensively "understand" the target
appearance variations, "recall" some stable results. Both qualitative and
quantitative evaluations on two benchmarks suggest that the proposed tracking
method performs favorably against some recently developed CFTs and other
competitive trackers.Comment: has been published on IEEE TI
ASIC: Aligning Sparse in-the-wild Image Collections
We present a method for joint alignment of sparse in-the-wild image
collections of an object category. Most prior works assume either ground-truth
keypoint annotations or a large dataset of images of a single object category.
However, neither of the above assumptions hold true for the long-tail of the
objects present in the world. We present a self-supervised technique that
directly optimizes on a sparse collection of images of a particular
object/object category to obtain consistent dense correspondences across the
collection. We use pairwise nearest neighbors obtained from deep features of a
pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches
and make them dense and accurate matches by optimizing a neural network that
jointly maps the image collection into a learned canonical grid. Experiments on
CUB and SPair-71k benchmarks demonstrate that our method can produce globally
consistent and higher quality correspondences across the image collection when
compared to existing self-supervised methods. Code and other material will be
made available at \url{https://kampta.github.io/asic}.Comment: Web: https://kampta.github.io/asi
The Method of Automatic Knuckle Image Acquisition for Continuous Verification Systems
The paper proposes a method of automatic knuckle image acquisition for continuous
verification systems. The developed acquisition method is dedicated for verification systems in
which the person being verified uses a computer keyboard. This manner of acquisition enables
registration of the knuckle image without interrupting the user’s work for the time of acquisition.
This is an important advantage, unprecedented in the currently known methods. The process of
the automatic location of the finger knuckle can be considered as a pattern recognition approach
and is based on the analysis of symmetry and similarity between the reference knuckle patterns
and live camera image. The effectiveness of the aforesaid approach has been tested experimentally.
The test results confirmed its high effectiveness. The effectiveness of the proposed method was also
determined in a case where it is a part of a multi-biometric method
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