Object recognition and segmentation by a fragment-based hierarchy.

By Shimon Ullman. 2007

256 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

The cortical dynamics of speaking: present shortcomings and future avenues (2016, unknown): “…2009) and even anticipate specific objects for recognition (e.g. Chelazzi, Miller, Duncan, & Desimone, 1993; O’Craven & Kanwisher, 2000; Peelen et al., 2009; Ullman, 2007) (for reviews see: e.g. Corbetta & Shulman, 2002; Gilbert & Li, 2013; Kanwisher & Wojciulik, 2000; Kastner & Ungerleider, 2000).”
Reading normal and degraded words: Contribution of the dorsal and ventral visual pathways (2008, background): “Neurophysiological observations in the macaque ventral visual regions have led to construe this system as a hierarchy of converging neural detectors with progressively larger receptor fields, tuned to increasingly complex objects (Booth and Rolls, 1998; Riesenhuber and Poggio, 1999; Rolls, 2000; Ull”
Perceived Shape Similarity among Unfamiliar Objects and the Organization of the Human Object Vision Pathway (2008, background): “A more comprehensive computational model of shape recognition might include an adaptive feature dictionary (Serre et al., 2007) so that the tuning becomes more informative for object categorization (Biederman, 1987; Edelman and Intrator, 2000; Ullman et al., 2002; Kiani et al., 2007; Ullman, 2007).”
Learning illumination- and orientation-invariant representations of objects through temporal association. (2009, background): “An alternative model proposes that recognition is based upon image matching (Bülthoff & Edelman, 1992) and more recently, abstract feature matching (Torralba, Murphy, & Freeman, 2007; Ullman, 2006; Wallis & Bülthoff, 1999).”
How the brain might work: a hierarchical and temporal model for learning and recognition (2008, methodology): “Neocognitron [32], convolutional neural networks [58], the HMAX model [84], and the fragments hierarchy from Ullman [100] are examples of models that have network architectures and pooling operations similar to the HTM model described here.”
Mechanisms of face perception. (2008, methodology): “Some machine vision models of recognition use common “meta-algorithms” to learn the primitives for both detection and recognition of faces (Riesenhuber & Poggio 2000, Ullman 2007) thus the two processes may share core computational principles.”
Learned Non-Rigid Object Motion is a View-Invariant Cue to Recognizing Novel Objects (2012, unknown): “This robust viewpoint effect across many stimuli and recognition tasks has motivated many computational models to adopt a view-dependent approach to understanding visual object recognition (e.g., Serre et al., 2007; Ullman, 2007; for a view-invariant approach, see Hummel and Biederman, 1992).”
Hierarchical Part Matching for Fine-Grained Visual Categorization (2013, background): “Fortunately, as evidences accumulate in Neuroscience, researchers realize that human beings recognize objects using a combinational representation of local features [16].”
Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey (2008, background): “This suggests that an IT model should acquire category-discriminating features by supervised learning (Ullman, 2007).”