Matching locations is not just matching sensory representations

Fast Least Square Matching

Least square matching (LSM) is one of the most accurate image matching methods in photogrammetry and remote sensing. The main disadvantage of the LSM is its high computational complexity due to large size of observation equations. To address this problem, in this paper a novel method, called fast least square matching (FLSM) is being presented. The main idea of the proposed FLSM is decreasing t...

Robust Steganography based on Matching Pixel Locations

Steganography consist of concealing secret information in a cover object to be sent over a public communication channel. It allows two parties to share hidden information in a way that no intruder can detect the presence of hidden information. This paper presents a novel steganography approach based on pixel location matching of the same cover image. Here the information is not directly embedde...

Adaptive Approximate Record Matching

Typographical data entry errors and incomplete documents, produce imperfect records in real world databases. These errors generate distinct records which belong to the same entity. The aim of Approximate Record Matching is to find multiple records which belong to an entity. In this paper, an algorithm for Approximate Record Matching is proposed that can be adapted automatically with input error...

Sensory Integration: It's Not Just for Children

Active Sensing: Matching Motor and Sensory Space

A recent study has shown that, unusually, both the sensory and motor capabilities of an electric fish are omnidirectional. This matching of motor and sensory spaces helps the fish to hunt prey efficiently - particularly important given their energetically costly active sensory system.