Publication

A Color and Gradient Local Descriptor Fusion Scheme For Object Recognition

2003
Reports

Abstract

In this paper we study the behavior of local descriptor object recognition methods with respect to 3D geometric transformations and image resolution variations. As expected performance decreases with accentuated perspective and decrease in resolution. To improve performance and robustness, we propose a scheme to fuse color and gradient local descriptors. This approach is motivated by the discriminative power of color in man-made object recognition. The problem of color feature extraction is addressed as well as the considerations on the fusion process and steps to train such fusion. We used SOIL-47A database for experiments and shown a 7% to 10% relative improvement when compared with state-of-the-art gradient based descriptors.

Official source

https://infoscience.epfl.ch/entities/publication/d23296cc-0af7-4c66-ae65-62ae998c0018

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2003
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/d23296cc-0af7-4c66-ae65-62ae998c0018

About this result

Related concepts (29)

Feature (computer vision)

In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.

Scale-invariant feature transform

The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, , 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database.

Object detection

Object detection is a computer technology related to computer vision and that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including and video surveillance. It is widely used in computer vision tasks such as , vehicle counting, activity recognition, face detection, face recognition, video object co-segmentation.

Related publications (28)

A Color and Gradient Local Descriptor Fusion Scheme For Object Recognition

Chat with Graph Search

Object recognition from sparse simulated phosphenes and curved segments

Wide visual angle enhances social interaction in a realistic virtual reality environment under simulated artificial vision

HBB2OBB: Horizontal to Oriented Bounding Box Conversion and Evaluation Tool