Gradient local auto-correlation features for depth human action recognition

Gradient Local Auto-Correlations and Extreme Learning Machine for Depth-Based Activity Recognition

This paper presents a new method for human activity recognition using depth sequences. Each depth sequence is represented by three depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues. A feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is introduced to extract features from DMMs. The gradient lo...

Evaluating spatiotemporal interest point features for depth-based action recognition

Classifying Seabed Sediments Using the Local Auto-correlation Features

Understanding the distribution of seafloor sediment using a side-scan sonar is very important to grasp the distribution of seabed resources. This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the mi...

Motion recognition using local auto-correlation of space-time gradients

0167-8655/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2012.01.007 ⇑ Corresponding author. Tel.: +81 29 861 5491; fax E-mail addresses: [email protected] (T. (N. Otsu). In this paper, we propose a motion recognition scheme based on a novel method of motion feature extraction. The feature extraction method utilizes auto-correlations of space–time gradients...

Holistic Farsi handwritten word recognition using gradient features

In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...