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Related Experiment Video

Updated: Aug 19, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Representation based regression for object distance estimation.

Mete Ahishali¹, Mehmet Yamac¹, Serkan Kiranyaz²

¹Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, 33720, Finland.

Neural Networks : the Official Journal of the International Neural Network Society

|November 27, 2022

Summary

This summary is machine-generated.

This study introduces Representation-based Regression (RbR), a novel method for object distance prediction using modified Convolutional Support Estimator Networks (CSENs). RbR significantly improves distance estimation accuracy, especially with limited data.

Keywords:

Convolutional support estimator network Object distance estimation Representation-based regression Sparse support estimation

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Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Object detection and distance estimation are crucial in computer vision.
Existing methods often struggle with performance on scarce data.
Representation-based methods are typically used for classification, not regression.

Purpose of the Study:

To propose a novel approach for predicting object distances in observed scenes.
To adapt Convolutional Support Estimator Networks (CSENs) for regression tasks.
To introduce Representation-based Regression (RbR) as a new method for distance estimation.

Main Methods:

Modification of Convolutional Support Estimator Networks (CSENs).
Development of Compressive Learning CSEN (CL-CSEN) for joint optimization.
Application of representation-based methods (sparse/collaborative representation) to regression problems.
Utilizing the KITTI 3D Object Detection dataset for evaluation.

Main Results:

The proposed Representation-based Regression (RbR) method demonstrates superior performance.
Compressive Learning CSEN (CL-CSEN) effectively optimizes the proxy mapping stage.
Significantly improved distance estimation accuracy compared to competing methods was achieved.
The method shows effectiveness even with scarce data.

Conclusions:

Representation-based methods can be successfully applied to well-designed regression problems.
The modified CSENs, specifically CL-CSEN, offer a powerful tool for distance estimation.
The novel RbR approach advances the field of 3D object detection and distance prediction.