Generation of Hierarchical Object Models for Image Recognition.

Attention in hierarchical models of object recognition.

Object recognition and visual attention are tightly linked processes in human perception. Over the last three decades, many models have been suggested to explain these two processes and their interactions, and in some cases these models appear to contradict each other. We suggest a unifying framework for object recognition and attention and review the existing modeling literature in this contex...

Object Recognition with Hierarchical Stel Models

We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment multiple images into a consistent set of image regions. The segmentations are provided at several levels of granularity and links among them are automatically provided. Model training and inference in it is faster than most loc...

Learning Optimized Features for Hierarchical Models of Invariant Object Recognition

There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares components like weight sharing, pooli...

Hierarchical feed forward models for robust object recognition

Automatic Hierarchical Object Decomposition for Object Recognition

Industrial applications of 2D object recognition such as quality control often demand robustness, highest accuracy, and real-time computation from the object recognition approach. Simultaneously fulfilling all of these demands is a hard problem and has recently drawn considerable attention within the research community of close-range photogrammetry and computer vision. The problem is complicate...