MOPEX Online Manual

APEX Modules: Detect

PURPOSE

Module detect performs image segmentation and computes the centroids for the detected pixel clusters. It is another instance of the same module used for outlier detection in the mosaic pipeline.

PARAMETER BLOCK

&DETECT
 Detection_Max_Area = 5,
 Detection_Min_Area = 2,
 Detection_Threshold = 10,
 Input_Type = 'image_input',
 Threshold_Type = 'combo',
 Extended_Object_Area = 500,
 Minimum_Coverage = 1.8,
&END

if Input_Type = "snr_input" then you must also include the following parameter block. N_Outliers_Per_Window must be set to 0:

&PSP_GAUSSNOISE
 Window_X = 45,
 Window_Y = 45,
 N_Outliers_Per_Window = 0,
&END

INPUTS

Detection_Max_Area: (int) The maximum area (pixels) of a detected pixel cluster before iterative thresholding.

Detection_Min_Area: (int) The minimum area (pixel) for a detected pixel cluster to be retained; smaller clusters are discarded.

Detection_Threshold: (float) The number of sigma above the mean to be used as the initial threshold used for cluster detection.

Input_Type: This keyword determines the type of image to be used as the input for detect. The choice is between an "snr" image (snr_input) or an image based on the original data (image_input). There are several different versions of each of these input types. The version used depends upon the keyword use_psp_to_detect, which is set in the Global Parameters section of the APEX namelist. For more information about the available image types, see the Discussion section below.

Threshold_Type: This parameter determines the way the image segmentation threshold is recalculated during iterative thresholding. The threshold type does not depend on the type of input image.

Extended_Object_Area: (int) The minimum area (pixels) to be considered an extended object. If the number of pixels in a cluter is larger than this area, it is not split by Threshold_Type="peak".

Minimum_Coverage: (float) The minimum value in the coverage map needed for a pixel to be considered for segmentation.

Window_X: (int) the X size in pixels of the window used to compute the median of the background. The default value is 45

Window_Y: (int) the Y size in pixels of the window used to compute the median of the background. The default value is 45

N_Outliers_Per_Window: (int) the number of outlier pixels (N) being rejected from the X*Y window when computing the noise. In PSP_GAUSSNOISE this must be set to 0.

OUTPUTS

Table (mosaic_detect.tbl): In the single frame mode there is one detection list produced in this step. In the multi frame mode the detection is performed for each co-added tile. The detection lists for each tile are combined into one detection list.

For each source, the output table of detected sources lists:

• the source ID
• the position (X, Y in pixels)
• a rough "flux" estimate (the peak pixel value, in the same units as the input image),
• BlendId: keeps track of the sequential number of a detection blend in the table.
• BlendSize: gives the number of detections in the blend.

Detection Image: The image used for detection (input or SNR, for example) can be displayed.

DISCUSSION

Input Image Type: The input to this module depends on namelist parameter Input_Type and the switch use_psp_to_detect in the APEX namelist. The table below gives the type of image that will be used for segmentation. The "Filtered" image mentioned in the table is defined as the product of the PSF image times the background subtracted image (see APEX Modules: Point Source Probability for more information).

use PSP to detect	Input Type	Input Images
2	"SNR input"	SNR image based on the background subtracted input image
2	"Image input"	Background subtracted input image
1 (default)	"SNR input"	SNR image based on the PSP image
1 (default)	"Image input"	PSP image
0	"SNR input"	SNR image based on the Filtered image
0	"Image input"	Filtered image
-1 (Single Frame only)	"SNR input"	SNR image based on the input image without background subtraction
-1 (Single Frame only)	"Image input"	the input image without background subtraction

Output Format: Below is an example of the output table from apex_1frame.pl:

\char comment = Output from DETECT, version 1.00 
srcid|     x|       y|       flux| BlendId| BlendSize| 
| i  |     r|       r|       r   | i      | i        | 
    0   2.50   229.50   1.328e+03        0          0 
    1   3.30    77.50   1.369e+03        0          0 
    2   3.40   190.55   1.338e+03        1          2 
    3   4.82   190.24   1.435e+03        1          2 
    4   4.50   111.50   1.332e+03        0          0 
    5   8.40   205.00   1.275e+04        2          3 
    6   5.50   205.00   1.330e+03        2          3 
    7   6.38   206.70   1.853e+03        2          3 
    8   5.50   127.50   1.328e+03        0          0