Topic
Orientation (computer vision)
About: Orientation (computer vision) is a research topic. Over the lifetime, 17196 publications have been published within this topic receiving 358181 citations.
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TL;DR: This paper addresses the problem of estimating head pose over a wide range of angles from low-resolution images usingGrey-level normalized face imagettes serve as input for linear auto-associative memory, and achieves similar results in estimating orientation in tilt (head nodding) angle, and higher precision for estimating Orientation in the pan (side-to-side) angle.
Abstract: This paper addresses the problem of estimating head pose over a wide range of angles from low-resolution images. Faces are detected using chrominance-based features. Grey-level normalized face imagettes serve as input for linear auto-associative memory. One memory is computed for each pose using a Widrow-Hoff learning rule. Head pose is classified with a winner-takes-all process. We compare results from our method with abilities of human subjects to estimate head pose from the same data set. Our method achieves similar results in estimating orientation in tilt (head nodding) angle, and higher precision for estimating orientation in the pan (side-to-side) angle.
127 citations
01 Jan 2000
TL;DR: A new approach for geometric distortion correction based on image normalization is presented, in which the watermark is embedded and detected in the normalized image regardless of its size, orientation and flipping direction.
Abstract: A new approach for geometric distortion correction based on image normalization is presented in this paper. By normalization we mean geometrically transforming the image into a standard form. The parameters by which the image is normalized are estimated from the geometric moments of the image. This paper presents a system in which the watermark is embedded and detected in the normalized image. The watermark can then be embedded and detected in the normalized image regardless of its size, orientation and flipping direction.
127 citations
24 Oct 2004
TL;DR: This paper discusses methods in evaluating fingerprint image quality on a local level using feature vectors extracted from fingerprint image subblocks and three different classifiers employed to compare each of its different effectiveness.
Abstract: Fingerprint image quality analysis is crucial in eliminating poor fingerprint images, which will affect the performance of the automatic fingerprint identification system. In this article, two types of new quality measures will be introduced: ridge and valley clarity and global orientation flow to calculate the overall image quality score that can be used to quantitatively determine the quality of the fingerprint image. In order to evaluate the performance of the proposed algorithm, the quality measure is used to rank the performance of a fingerprint recognition system and the ranking is compared with the quality measure rated manually. The result shows that the proposed scheme will return a score that ensures its reliability to indicate the quality of a given fingerprint image.
127 citations
18 Sep 2012
TL;DR: This paper detects the head region since this is the most visible part of the body in a crowded scene and proposes a novel interest point detector based on gradient orientation feature to locate regions similar to the top of head region from gray level images.
Abstract: Crowd counting and density estimation is still one of the important task in video surveillance. Usually a regression based method is used to estimate the number of people from a sequence of images. In this paper we investigate to estimate the count of people in a crowded scene. We detect the head region since this is the most visible part of the body in a crowded scene. The head detector is based on state-of-art cascade of boosted integral features. To prune the search region we propose a novel interest point detector based on gradient orientation feature to locate regions similar to the top of head region from gray level images. Two different background subtraction methods are evaluated to further reduce the search region. We evaluate our approach on PETS 2012 and Turin metro station databases. Experiments on these databases show good performance of our method for crowd counting.
127 citations
TL;DR: This work presents a technique based on compressed sensing that can resolve crossing fibers using diffusion MRI data that can be rapidly and routinely acquired in the clinic (30 directions) and assumes that the observed data can be well fit using a sparse linear combination of tensors taken from a fixed collection of possible tensors each having a different orientation.
126 citations