'Feature maps' presentation slideshows

Kernel Methods

1.21k views • 100 slides

Competitive Networks

Competitive Networks. Outline. Hamming Network Competitive Layer Competitive Layers in Biology Self-Organizing Feature Maps Learning Vector Quantization. Hamming Network. Layer 1 (Correlation). Layer 2 (Competition). Competitive Layer. Competitive Learning. Graphical Representation.

1k views • 71 slides

Understanding V1 --- Saliency map and pre-attentive segmentation

Understanding V1 --- Saliency map and pre-attentive segmentation. Li Zhaoping University College London, Psychology www.gatsby.ucl.ac.uk/~zhaoping. Lecture 2, at EU Advanced Course in Computational Neuroscience, Arcachon, France, Aug. 2006

703 views • 56 slides

Test Recap

Learning Locomotion: Extreme Learning For Extreme Terrain CMU: Chris Atkeson Drew Bagnell, James Kuffner, Martin Stolle, Hanns Tappeiner, Nathan Ratliff, Joel Chestnutt, Michael Dille, Andrew Maas CMU Robotics Institute Test Recap Test 1: Everything went well.

811 views • 53 slides

Psy280: Perception

431 views • 43 slides

Features and Objects in Visual Processing

Features and Objects in Visual Processing. The Visual World is an Arrangement of Features. Color Motion Form Depth Orientation. Pre-attentive vs. Attentive Processing. Pre-attentive processing Does the visual system register some basic features automatically (without attention)

793 views • 37 slides

A bottom up visual saliency map in the primary visual cortex

A bottom up visual saliency map in the primary visual cortex --- theory and its experimental tests. Li Zhaoping. University College London. Invited presentation at COSYNE (computational and systems Neuroscience) conference Salt Lake City, Utah, Feb. 2007. Test 1:.

530 views • 33 slides

Nicolas Pinto Massachusetts Institute of Technology David Cox

Beyond Simple Features: A Large-Scale Feature Search Approach to Unconstrained Face Recognition. International Conference on Automatic Face and Gesture Recognition (FG), 2011. Nicolas Pinto Massachusetts Institute of Technology David Cox

444 views • 30 slides

Radiometric Normalization

Radiometric Normalization. Spring 2009. Ben-Gurion University of the Negev. Instructor. Dr. H. B Mitchell email: harveymitchell@walla.co.il. Sensor Fusion Spring 2009. Radiometric Normalization.

428 views • 28 slides

Using FLUXNET data to evaluate land surface models

315 views • 22 slides

Cognitive Effects on Visual Target Acquisition

310 views • 22 slides

Implementation of a Visual Attention Model

448 views • 22 slides

Dual-force convolutional neural networks for accurate brain tumor segmentation

Dual-force convolutional neural networks for accurate brain tumor segmentation. Shengcong Chen, Changxing Ding, Minfeng Liu 2018. Introduction. Median Survival Time of High-Grade Gliomas patients is less than 2 years Median Survival Time of glioblastoma patients is 4.9 months

353 views • 19 slides

What are the mechanisms by which targets for saccades are selected?

What are the mechanisms by which targets for saccades are selected?. Neurophysiological studies of LIP suggest it serves to identify region in the image that may the target of the next saccade. (Goldberg, Colby etc)

247 views • 15 slides

The R ole of the Literature R eview

The R ole of the Literature R eview. Tine Wirenfeldt Jensen CENTER FOR TEACHING DEVELOPMENT & DIGITAL MEDIA AARHUS UNIVERSITY. A rubric for the quality of a literature review.

179 views • 9 slides

Visual Attention

302 views • 8 slides

Leticia M. Seijas and Enrique C. Segura {lseijas, esegura}@dc.uba.ar

The reliability table expresses how reliable the answer of each SOM is. Using the definition of conditional probability and the multiplication rule, we define the probability of pattern p belonging to class C given that feature map f has responded C for input p as:. (1).

139 views • 1 slides