Reliable detection and tracking of eyes is an important requirement for attentive user interfaces. In this paper we present a methodology for detecting eyes robustly in indoor environments in real-time. We exploit the...
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ISBN:
(纸本)0769506623
Reliable detection and tracking of eyes is an important requirement for attentive user interfaces. In this paper we present a methodology for detecting eyes robustly in indoor environments in real-time. We exploit the physiological properties and appearance of eyes as well as head/eye motion dynamics. Infrared lighting is used to capture the physiological properties of eyes, Kalman trackers are used to model eye/head dynamics, and a probabilistic based appearance model is used to represent eye appearance. By combining three separate modalities, with specific enhancements within each modality, our approach allows eyes to be treated as robust features that can be used for other higher-level processing.
With the recent advances of Convolutional Neural Networks (CNN) in computervision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In thi...
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ISBN:
(数字)9781538661000
ISBN:
(纸本)9781538661000
With the recent advances of Convolutional Neural Networks (CNN) in computervision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked U-Nets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.
We evaluated six algorithms for computing egomotion from image velocities. We established benchmarks for quantifying bias and sensitivity to noise, and for quantifying the convergence properties of those algorithms th...
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ISBN:
(纸本)0818672587
We evaluated six algorithms for computing egomotion from image velocities. We established benchmarks for quantifying bias and sensitivity to noise, and for quantifying the convergence properties of those algorithms that require numerical search. Our simulation results reveal some interesting and surprising results. First, it is often written in the literature that the egomotion problem is difficult because translation (e.g., along the X-axis) and rotation (e.g., about the Y-axis) produce similar image velocities. We found, to the contrary, that the bias and sensitivity of our six algorithms are totally invariant with respect to the axis of rotation. Second, it is also believed by some that fixating helps to make the egomotion problem easier. We found, to the contrary, that fixating does not help when the noise is independent of the image velocities. Fixation does help if the noise is proportional to speed, but this is only for the trivial reason that the speeds are slower under fixation. Third, it is widely believed that increasing the field of view will yield better performance. We found, to the contrary, that this is not necessarily true.
We present a class of statistical models for part-based object recognition that are explicitly parameterized according to the degree of spatial structure they can represent. These models provide a way of relating diff...
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ISBN:
(纸本)0769523722
We present a class of statistical models for part-based object recognition that are explicitly parameterized according to the degree of spatial structure they can represent. These models provide a way of relating different spatial priors that have been used for recognizing generic classes of objects, including joint Gaussian models and tree-structured models. By providing explicit control over the degree of spatial structure, our models make it possible to study the extent to which additional spatial constraints among parts are actually helpful in detection and localization, and to consider the tradeoff in representational power and computational cost. We consider these questions for object classes that have substantial geometric structure, such as airplanes, faces and motorbikes, using datasets employed by other researchers to facilitate evaluation. We find that for these classes of objects, a relatively small amount of spatial structure in the model can provide statistically indistinguishable recognition performance from more powerful models, and at a substantially lower computational cost.
Deep learning and patternrecognition in smart farming has seen rapid growth as a building bridge between crop science and computervision. One of the important application is anomaly segmentation in agriculture like ...
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ISBN:
(纸本)9781665448994
Deep learning and patternrecognition in smart farming has seen rapid growth as a building bridge between crop science and computervision. One of the important application is anomaly segmentation in agriculture like weed, standing water, cloud shadow, etc. Our research work focuses on aerial farmland image dataset known as Agriculture vision. We propose to have data fusion of R, G, B, and NIR modalities that enhances the feature extraction and also propose Efficient Fused Pyramid Network (Fuse-PN) for anomaly pattern segmentation. The proposed encoder module is a bottom-up pathway having a compound scaled network and decoder module is a top-down pyramid network enhancing features at different scales having rich semantic features with lateral connections of low-level features. This proposed approach achieved a mean dice similarity score of 0.8271 for six agricultural anomaly patterns of Agriculture vision dataset and outperforms various approaches in literature.
The detection of smooth curves in images and their completion over gaps are two important problems in perceptual grouping. lit this paper we examine the nation of completion energy and introduce a fast method to compu...
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ISBN:
(纸本)0780342364
The detection of smooth curves in images and their completion over gaps are two important problems in perceptual grouping. lit this paper we examine the nation of completion energy and introduce a fast method to compute the most likely completions in images. Specifically, we develop two novel analytic approximations to the curve of least energy. In addition, we introduce a fast numerical method to compute the curve of least energy and show that our approximations are obtained at early stages of this numerical computation. We then use our newly developed energies to find the most likely completions in images through a generalized summation of induction fields. Since in practice edge elements are obtained by applying filters of certain widths and lengths to the image, we adjust our computation to take these parameters into account. Finally, we show that, due to the smoothness of the kernel of summation the process of summing induction fields can be run in time that is linear in the number of different edge elements in the image, or in O(N log N) where N is the number of pixels in the image, using multigrid methods.
We have been researching three dimensional (3D) ground-truth systems for performance evaluation of vision and perception systems in the fields of smart manufacturing and robot safety. In this paper we first present an...
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ISBN:
(纸本)9780769549903
We have been researching three dimensional (3D) ground-truth systems for performance evaluation of vision and perception systems in the fields of smart manufacturing and robot safety. In this paper we first present an overview of different systems that have been used to provide ground-truth (GT) measurements and then we discuss the advantages of physically-sensed ground-truth systems for our applications. Then we discuss in detail the three ground- truth systems that we have used in our experiments: ultra wide-band, indoor GPS, and a camera-based motion capture system. Finally, we discuss three different perception-evaluation experiments where we have used these GT systems
作者：
Rigoutsos, IIBM Corp
Thomas J Watson Res Ctr Computat Biol Ctr Bioinformat & Pattern Discovery Yorktown Heights NY 10598 USA
We derive and discuss a set of parametric equations which, when given a convex 3D feature domain, K, will generate affine invariants with the property that the invariants' values are uniformly distributed in the r...
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ISBN:
(纸本)0818684976
We derive and discuss a set of parametric equations which, when given a convex 3D feature domain, K, will generate affine invariants with the property that the invariants' values are uniformly distributed in the region [0,1]x[0,1]x[0,1]. Once the shape of the feature domain K is determined and fixed it is straightforward to compute the values of the parameters and thus the proposed scheme can be tuned to a specific feature domain. The features of all recognizable objects (models) are assumed to be three-dimensional points and uniformly distributed over K. The scheme leads to improved discrimination power, improved computational-load and storage-load balancing and can also be used to determine and identify biases in the database of recognizable models (over-represented constructs of object points). Obvious enhancements produce rigid-transformation and similarity-transformation invariants with the same good distribution properties, making this approach generally applicable.
In this paper, we describe an algorithm called Fast Marching Watersheds that segments a triangle mesh into visual parts. This computervision algorithm leverages a human vision theory known as the minima rule. Our imp...
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In this paper, we describe an algorithm called Fast Marching Watersheds that segments a triangle mesh into visual parts. This computervision algorithm leverages a human vision theory known as the minima rule. Our implementation computes the principal curvatures and principal directions at each vertex of a mesh, and then our hill-climbing watershed algorithm identifies regions bounded by contours of negative curvature minima. These regions fit the definition of visual parts according to the minima rule. We present evaluation analysis and experimental results for the proposed algorithm.
In this paper we present an extensive evaluation of instance segmentation in the context of images containing clothes. We propose a multi level evaluation that completes the classical overlapping criteria given by IoU...
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ISBN:
(纸本)9781665448994
In this paper we present an extensive evaluation of instance segmentation in the context of images containing clothes. We propose a multi level evaluation that completes the classical overlapping criteria given by IoU. In particular, we quantify both the contour and color content accuracy of the the predicted segmentation masks. We demonstrate that the proposed evaluation framework is relevant to obtain meaningful insights on models performance through experiments conducted on five state of the art instance segmentation methods.
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