This paper describes two distortion estimation techniques for object recognition that solve EZ-Gimpy and Gimpy-r, two of the visual CAPTCHAs ("completely automated public turing test to tell computers and humans ...
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This paper describes two distortion estimation techniques for object recognition that solve EZ-Gimpy and Gimpy-r, two of the visual CAPTCHAs ("completely automated public turing test to tell computers and humans apart") with high degrees of success. A CAPTCHA is a program that generates and grades tests that most humans can pass but current computer programs cannot pass. We have developed a correlation algorithm that correctly identifies the word in an EZ-Gimpy challenge image 99% of the time and a direct distortion estimation algorithm that correctly identifies the four letters in a Gimpy-r challenge image 78% of the time.
Shape-from-shading (SfS) is a fundamental problem in computervision. At its basis lies the image irradiance equation. Recently, the authors proposed to base the image irradiance equation on the assumption of perspect...
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Shape-from-shading (SfS) is a fundamental problem in computervision. At its basis lies the image irradiance equation. Recently, the authors proposed to base the image irradiance equation on the assumption of perspective projection rather than the common orthographic one. The current paper presents a greatly-improved reconstruction method based on the perspective formulation. The proposed model is solved efficiently via a modification of the fast marching method of Kimmel and Sethian. We compare the two versions of the fast marching method (orthographic vs. perspective) on medical images. The perspective algorithm outperformed the orthographic one. This shows that the more realistic hypothesis of perspective projection improves reconstruction significantly. The comparison also demonstrates the usability of perspective SfS for real-life applications such as medical endoscopy.
The goal of this work is to detect a human figure image and localize his joints and limbs along with their associated pixel masks. In this work we attempt to tackle this problem in a general setting. The dataset we us...
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The goal of this work is to detect a human figure image and localize his joints and limbs along with their associated pixel masks. In this work we attempt to tackle this problem in a general setting. The dataset we use is a collection of sports news photographs of baseball players, varying dramatically in pose and clothing. The approach that we take is to use segmentation to guide our recognition algorithm to salient bits of the image. We use this segmentation approach to build limb and torso detectors, the outputs of which are assembled into human figures. We present quantitative results on torso localization, in addition to shortlisted full body configurations.
A key problem in learning multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which part is irrelevant clutter which is n...
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A key problem in learning multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which part is irrelevant clutter which is not associated to the objects. We investigate empirically to what extent pure bottom-up attention can extract useful information about the location, size and shape of objects from images and demonstrate how this information can be utilized to enable unsupervised learning of objects from unlabeled images. Our experiments demonstrate that the proposed approach to using bottom-up attention is indeed useful for a variety of applications.
We explore a novel application of facial asymmetry: expression classification. Using 2D facial expression images, we show the effectiveness of automatically selected local facial asymmetry for expression recognition. ...
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We explore a novel application of facial asymmetry: expression classification. Using 2D facial expression images, we show the effectiveness of automatically selected local facial asymmetry for expression recognition. Quantitative evaluations of expression classification using local asymmetry demonstrate statistically significant improvements over expression classification results on the same data set without explicit representation of facial asymmetry. A comparison of discriminative local facial asymmetry features for expression classification versus human identification is given.
In this paper we describe a method for skeletonization of gray-scale images without segmentation. Our method is based on anisotropic vector diffusion. The skeleton strength map, calculated from the diffused vector fie...
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In this paper we describe a method for skeletonization of gray-scale images without segmentation. Our method is based on anisotropic vector diffusion. The skeleton strength map, calculated from the diffused vector field, provides us a measure of how possible each pixel could be on the skeletons. The final skeletons are traced from the skeleton strength map, which mimics the behavior of edge detection from the edge strength map of the original image. A couple of real or synthesized images will be shown to demonstrate the performance of our algorithm.
This paper presents a spatio-temporal query language useful for video interpretation and event recognition. The language is suited to describe configurations of objects moving on a plane. To demonstrate its applicabil...
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This paper presents a spatio-temporal query language useful for video interpretation and event recognition. The language is suited to describe configurations of objects moving on a plane. To demonstrate its applicability it has been tested on the output of a tracker working on a car traffic scene. The results of two example sets of queries are shown in two videos generated from the trackers data output. The first selects a ghost from the tracking data and the second shows how to find queues of cars in the road traffic scene without prior knowledge of lanes.
We present a general framework for characterizing the object identity in a single image or a group of images with each image containing a transformed version of the object, with applications to face recognition. In te...
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We present a general framework for characterizing the object identity in a single image or a group of images with each image containing a transformed version of the object, with applications to face recognition. In terms of the transformation, the group is made of either many still images or frames of a video sequence. The object identity is either discrete- or continuous-valued. This probabilistic framework integrates all the evidence of the set and handles the localization problem, illumination and pose variations through subspace identity encoding. Issues and challenges arising in this framework are addressed and efficient computational schemes are presented. Good face recognition results using the PIE database are reported.
We present a generalized random field model in a random environment to classify hyperspectral textures. The model generalizes traditional random field models by allowing the spatial interaction parameters of the field...
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We present a generalized random field model in a random environment to classify hyperspectral textures. The model generalizes traditional random field models by allowing the spatial interaction parameters of the field to be random variables. Principal component analysis is used to reduce the dimensionality of the data set to a small number of spectral bands that capture almost all of the energy in the original hyperspectral textures. Using the model we obtain a compact feature vector that efficiently computes within and between band information. Using a set of hyperspectral samples, we evaluate the performance of this model for classifying textures and compare the results with other approaches.
We present a novel structure-enhancing adaptive filter guided by features derived from the gradient structure tensor. We employ this filter to reduce noise in seismic data and to assist in generating seed points for i...
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We present a novel structure-enhancing adaptive filter guided by features derived from the gradient structure tensor. We employ this filter to reduce noise in seismic data and to assist in generating seed points for initializing an automatic horizon picking algorithm. In addition, our algorithm takes seismic attributes into consideration to reduce the possibilities of false horizon generation and fault crossing. Comparative experimental results are presented to highlight the potential of our approach.
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