Videos tagged with machine vision

Bay Area Vision Meeting (more info below) Unsupervised Feature Learning and Deep Learning Presented by Andrew Ng March 7, 2011 ABSTRACT Despite machine learning's numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other p...

Bay Area Vision Meeting (more info below) Position-Dependent Face Processing: insights from the Human Brain Presented by Kalanit Grill-Spector March 7, 2011 ABSTRACT Prevailing models of face recognition in the human brain suggest a hierarchical processing stream beginning in primary visual cortex (V1) and ending in the temporal lobe in an area specialized for face recognition (fusiform face ar...

Bay Area Vision Meeting (more info below) Perception for Robotics Caroline Pantofaru and Radu Rusu March 7, 2011 ABSTRACT Perception for robotics is growing as a field. What was once a specialized area utilizing expensive sensors and using situational information unavailable to more general computer vision problems, is merging into the mainstream. Cheap sensors such as the Kinect provide 3D dat...

Bay Area Vision Meeting (more info below) Learning Representations for Real-world Recognition Trevor Darrell March 7, 2011 ABSTRACT Methods for visual recognition had made dramatic strides in recent years on various online benchmarks, but performance in the real world still often falters. Classic bag-of-local-feature models make overly simplistic assumptions regarding image appearance statistic...

Bay Area Vision Meeting (more info below) Visual Recognition via Feature Learning Kai Yu March 7, 2011 ABSTRACT In this talk I will share some of our experiences at NEC Labs about large-scale image recognition by using feature learning. We worked on extending sparse coding to a broader family of nonlinear coding methods that explore the geometrical structure of sensory image data. The coding of...

Google Tech Talk (more info below) May 4, 2011 Presented by Zdenek Kalal. ABSTRACT I will be talking about an algorithm that I have developed during my PhD thesis and which recently become popular on the internet: http://goo.gl/rC5Xj. The algorithm is called Predator and it is a visual tracker that has the property to improve its own performance during run-time. This is achieved by designing a ...

Google Tech Talk (more info below) May 5, 2011 Presented by Professor Fei-Fei Li, Stanford University ABSTRACT A key challenge in visual recognition is to recognize and label a large number of visual concepts, such as object and scene classes. In this talk, I'll discuss two recent projects in the Stanford Vision Lab on this topic. ImageNet is a large-scale image ontology that is built on the ba...

Bay Area Vision Meeting (more info below)Unsupervised Feature Learning and Deep LearningPresented by Andrew NgMarch 7, 2011ABSTRACTDespite machine learning's numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other proble...

Bay Area Vision Meeting (more info below)Perception for RoboticsCaroline Pantofaru and Radu RusuMarch 7, 2011ABSTRACTPerception for robotics is growing as a field. What was once a specialized area utilizing expensive sensors and using situational information unavailable to more general computer vision problems, is merging into the mainstream. Cheap sensors such as the Kinect provide 3D data to ...

Bay Area Vision Meeting (more info below)Position-Dependent Face Processing: insights from the Human BrainPresented by Kalanit Grill-SpectorMarch 7, 2011ABSTRACTPrevailing models of face recognition in the human brain suggest a hierarchical processing stream beginning in primary visual cortex (V1) and ending in the temporal lobe in an area specialized for face recognition (fusiform face areas, ...