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Immunolabeling and Counting Ribbon Synapses in Young Adult and Aged Gerbil Cochleae

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Handling memory overflow in connected component labeling applications.

I Dinstein¹, D W Yen, M D Flickner

¹Department of Electrical and Computer Engineering, Ben-Gurion University, Beersheva, Israel; IBM Research Laboratory, San Jose, CA 95193.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study presents a novel component labeling method that reuses memory, preventing overflow for large image processing. This hardware-efficient approach ensures realizable storage requirements for complex feature extraction tasks.

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Area of Science:

Computer Vision
Image Processing
Hardware Architecture

Background:

Component labeling and feature extraction in large images pose significant, often unknown, storage challenges.
High storage demands can impede the practical hardware implementation of image processing algorithms.

Purpose of the Study:

To develop a memory-efficient component labeling procedure for large-scale image analysis.
To address and overcome the storage limitations in hardware implementations of feature extraction.

Main Methods:

A novel labeling procedure designed to eliminate memory overflow by enabling memory location reuse.
Analysis of storage requirements under worst-case conditions to demonstrate feasibility.
Implementation of the procedure in two distinct modes: interrupted and parallel.

Main Results:

The proposed labeling method effectively reuses memory, mitigating overflow issues.
Worst-case storage analysis confirms the realizability of the approach for hardware implementation.
Successful demonstration of the procedure in both interrupted and parallel modes.

Conclusions:

The developed component labeling technique offers a practical solution for managing storage in large image processing.
The method's efficiency and hardware feasibility make it suitable for complex feature extraction applications.
The presented hardware design provides a tangible implementation of the memory-saving labeling procedure.