Hasil untuk "cs.CV"

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arXiv Open Access 2021
Early warning of pedestrians and cyclists

Joerg Christian Wolf

State-of-the-art motor vehicles are able to break for pedestrians in an emergency. We investigate what it would take to issue an early warning to the driver so he/she has time to react. We have identified that predicting the intention of a pedestrian reliably by position is a particularly hard challenge. This paper describes an early pedestrian warning demonstration system.

en cs.CV
arXiv Open Access 2020
Covapixels

Jeffrey Uhlmann

We propose and discuss the summarization of superpixel-type image tiles/patches using mean and covariance information. We refer to the resulting objects as covapixels.

en cs.CV
arXiv Open Access 2020
Extreme compression of grayscale images

Franklin Mendivil, Örjan Stenflo

Given an grayscale digital image, and a positive integer $n$, how well can we store the image at a compression ratio of $n:1$? In this paper we address the above question in extreme cases when $n>>50$ using "$\mathbf{V}$-variable image compression".

arXiv Open Access 2017
Challenge of Multi-Camera Tracking

Yong Wang, Ke Lu

Multi-camera tracking is quite different from single camera tracking, and it faces new technology and system architecture challenges. By analyzing the corresponding characteristics and disadvantages of the existing algorithms, problems in multi-camera tracking are summarized and some new directions for future work are also generalized.

en cs.CV
arXiv Open Access 2013
Stroke-Based Cursive Character Recognition

K. C. Santosh, E. Iwata

Human eye can see and read what is written or displayed either in natural handwriting or in printed format. The same work in case the machine does is called handwriting recognition. Handwriting recognition can be broken down into two categories: off-line and on-line. ...

en cs.CV
arXiv Open Access 2011
Kernel diff-hash

Michael M Bronstein

This paper presents a kernel formulation of the recently introduced diff-hash algorithm for the construction of similarity-sensitive hash functions. Our kernel diff-hash algorithm that shows superior performance on the problem of image feature descriptor matching.

en cs.CV, cs.AI
arXiv Open Access 2011
Kunchenko's Polynomials for Template Matching

Oleg Chertov, Taras Slipets

This paper reviews Kunchenko's polynomials using as template matching method to recognize template in one-dimensional input signal. Kunchenko's polynomials method is compared with classical methods - cross-correlation and sum of squared differences according to numerical statistical example.

en cs.CV

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