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Article
The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization
We present a visual recognition system for fine-grained visual categorization. The system is composed of a human and a machine working together and combines the complementary strengths of computer vision algor...
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Chapter and Conference Paper
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This ...
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Chapter and Conference Paper
Camera Distance from Face Images
We present a method for estimating the distance between a camera and a human head in 2D images from a calibrated camera. Leading head pose estimation algorithms focus mainly on head orientation (yaw, pitch, an...
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Chapter and Conference Paper
Face Box Shape and Verification
Successful face verification and recognition require matching corresponding points in a pair of images, and it is commonly acknowledged that alignment is a critical step prior to matching. Once aligned, a port...
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Chapter and Conference Paper
JBoost Optimization of Color Detectors for Autonomous Underwater Vehicle Navigation
In the world of autonomous underwater vehicles (AUV) the prominent form of sensing is sonar due to cloudy water conditions and dispersion of light. Although underwater conditions are highly suitable for sonar,...
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Article
Open AccessGlobally Optimal Algorithms for Stratified Autocalibration
We present practical algorithms for stratified autocalibration with theoretical guarantees of global optimality. Given a projective reconstruction, we first upgrade it to affine by estimating the position of t...
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Chapter and Conference Paper
Word Spotting in the Wild
We present a method for spotting words in the wild, i.e., in real images taken in unconstrained environments. Text found in the wild has a surprising range of difficulty. At one end of the spectrum, Optical Chara...
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Chapter and Conference Paper
Visual Recognition with Humans in the Loop
We present an interactive, hybrid human-computer method for object classification. The method applies to classes of objects that are recognizable by people with appropriate expertise (e.g., animal species or airp...
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Article
Practical Global Optimization for Multiview Geometry
This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and homography esti...
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Chapter and Conference Paper
Multiple Component Learning for Object Detection
Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, achieving very low false positives rates...
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Chapter and Conference Paper
Weakly Supervised Object Localization with Stable Segmentations
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from we...
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Article
A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion
We present a novel framework for motion segmentation that combines the concepts of layer-based methods and feature-based motion estimation. We estimate the initial correspondences by comparing vectors of filte...
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Chapter and Conference Paper
Structure from Periodic Motion
We show how to exploit temporal periodicity of moving objects to perform 3D reconstruction. The collection of period-separated frames serve as a surrogate for multiple rigid views of a particular pose of the m...
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Chapter and Conference Paper
Practical Global Optimization for Multiview Geometry
This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and homography estim...
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Chapter
Matching with Shape Contexts
We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solving for correspondences between ...
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Chapter and Conference Paper
On Refractive Optical Flow
This paper presents a novel generalization of the optical flow equation to the case of refraction, and it describes a method for recovering the refractive structure of an object from a video sequence acquired ...
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Chapter and Conference Paper
A Feature-Based Approach for Determining Dense Long Range Correspondences
Planar motion models can provide gross motion estimation and good segmentation for image pairs with large inter-frame disparity. However, as the disparity becomes larger, the resulting dense correspondences wi...
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Chapter
Extracting Global Structure from Gene Expression Profiles
We have developed a program, GENECUT, for analyzing datasets from gene expression profiling. GENECUT is based on a pairwise clustering method known as Normalized Cut [Shi and Malik, 1997]. GENECUT extracts global...
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Chapter and Conference Paper
Spectral Partitioning with Indefinite Kernels Using the Nyström Extension
Fowlkes et al. [7] recently introduced an approximation to the Normalized Cut (NCut) grou** algorithm [18] based on random subsampling and the Nyström extension. As presented, their method is restricted to the ...
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Chapter and Conference Paper
Approximate Thin Plate Spline Map**s
The thin plate spline (TPS) is an effective tool for modeling coordinate transformations that has been applied successfully in several computer vision applications. Unfortunately the solution requires the inve...
