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Related Experiment Video

Updated: Apr 25, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models.

George Azzopardi¹, Nicolai Petkov¹

¹Intelligent Systems, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen Groningen, Netherlands.

Frontiers in Computational Neuroscience

|August 16, 2014

Summary

This summary is machine-generated.

We introduce S-COSFIRE, a trainable hierarchical model for object recognition and localization inspired by primate vision. This method effectively detects shapes in complex scenes without prior segmentation, advancing computer vision.

Keywords:

handwriting analysis hierarchical representation object recognition robotics shape ventral stream vision and scene understanding

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Area of Science:

Computational neuroscience
Computer vision
Machine learning

Background:

Primate visual system processing inspires computational models.
Object recognition in complex scenes is crucial for computer vision.
Existing methods often require pre-segmentation, creating a dependency loop.

Purpose of the Study:

To propose a trainable hierarchical object recognition model, S-COSFIRE (Shape Combination Of Shifted FIlter REsponses).
To enable recognition and localization of objects in complex scenes without prior segmentation.
To mimic visual processing in the primate ventral stream (V1/V2 → V4 → TEO).

Main Methods:

Developed S-COSFIRE filters, automatically configured for contour-based feature arrangements.
Utilized weighted geometric mean of blurred and shifted vertex detector responses.
Inspired by inferotemporal cortex neuron properties.

Main Results:

Demonstrated effectiveness in handwritten manuscript analysis (letter/keyword spotting).
Showcased utility in domestic robot computer vision for object spotting.
Achieved effective recognition and localization of deformable objects without pre-segmentation.

Conclusions:

S-COSFIRE filters are versatile, trainable shape detectors.
The model is conceptually simple and easy to implement.
Hierarchical shape representation enhances brain understanding and computer vision robustness.