Object detection in multi-epoch data

نویسندگان

  • G. Jogesh Babu
  • S. G. Djorgovski
  • Jogesh Babu
چکیده

In astronomy multiple images are frequently obtained at the same position of the sky for followup co-addition as it helps one go deeper and look for fainter objects. With large scale panchromatic synoptic surveys becoming more common, image co-addition has become even more necessary as new observations start to get compared with co-added fiducial sky in real time. The standard co-addition techniques have included straight averages, variance weighted averages, medians etc. A more sophisticated nonlinear response chi-square method is also used when it is known that the data are background noise limited and the point spread function is homogenized in all channels. A more robust object detection technique capable of detecting faint sources, even those not seen at all epochs which will normally be smoothed out in traditional methods, is described. The analysis at each pixel level is based on a formula similar to Mahalanobis distance. The method does not depend on the point spread function.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Fisher Discriminant Analysis (FDA), a supervised feature reduction method in seismic object detection

Automatic processes on seismic data using pattern recognition is one of the interesting fields in geophysical data interpretation. One part is the seismic object detection using different supervised classification methods that finally has an output as a probability cube. Object detection process starts with generating a pickset of two classes labeled as object and non-object and then selecting ...

متن کامل

ar X iv : a str o - ph / 0 61 27 07 v 1 2 2 D ec 2 00 6 Object detection in multi - epoch data

In astronomy multiple images are frequently obtained at the same position of the sky for followup co-addition as it helps one go deeper and look for fainter objects. With large scale panchromatic synoptic surveys becoming more common, image co-addition has become even more necessary as new observations start to get compared with co-added fiducial sky in real time. The standard co-addition techn...

متن کامل

Urban Vegetation Recognition Based on the Decision Level Fusion of Hyperspectral and Lidar Data

Introduction: Information about vegetation cover and their health has always been interesting to ecologists due to its importance in terms of habitat, energy production and other important characteristics of plants on the earth planet. Nowadays, developments in remote sensing technologies caused more remotely sensed data accessible to researchers. The combination of these data improves the obje...

متن کامل

Automatic Road Detection and Extraction From MultiSpectral Images Using a New Hierarchical Object-based Method

Road detection and Extraction is one of the most important issues in photogrammetry, remote sensing and machine vision. A great deal of research has been done in this area based on multispectral images, which are mostly relatively good results. In this paper, a novel automated and hierarchical object-based method for detecting and extracting of roads is proposed. This research is based on the M...

متن کامل

Detection and Recognition of Multi-language Traffic Sign Context by Intelligent Driver Assistance Systems

Design of a new intelligent driver assistance system based on traffic sign detection with Persian context is concerned in this paper. The primary aim of this system is to increase the precision of drivers in choosing their path with regard to traffic signs. To achieve this goal, a new framework that implements fuzzy logic was used to detect traffic signs in videos captured along a highway f...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006