16 Result(s)
within
Serge Belongie
Computer Imaging, Vision, Pattern Recognition and Graphics
-
Chapter and Conference Paper
SITTA: Single Image Texture Translation for Data Augmentation
Recent advances in data augmentation enable one to translate images by learning the map** between a source domain and a target domain. Existing methods tend to learn the distributions by training a model on ...
-
Article
Open AccessOccluded Video Instance Segmentation: A Benchmark
Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset called OVIS for occluded video instance segmentation, th...
-
Article
Convolutional Networks with Adaptive Inference Graphs
Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained d...
-
Article
Editorial: Special Issue on Active and Interactive Methods in Computer Vision
-
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...
-
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,...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
Article
Contour and Texture Analysis for Image Segmentation
This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour ...
-
Chapter
Contour and Texture Analysis for Image Segmentation
This paper provides an algorithm for partitioning gray-scale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour...
