Probabilistic and Biologically Inspired Feature Representations

Name: Probabilistic and Biologically Inspired Feature Representations
Brand: Springer Nature
SKU: 9783031006944
Availability: InStock

Best Price:
Buy Probabilistic and Biologically Inspired Feature Representations for $44.99 at @ Link.springer.com
No coupon is required — this is the standard retail price.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $44.99 - $44.99

BEST PRICE

Single Product Purchase

$44.99

@ Link.springer.com BUY Now

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$44.99	$44.99			$44.99		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9783031006944

Condition

New

Product Description

Under the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knifethey can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.

Available Colors

N/A

Available Sizes

N/A