A Label Geometry Image Filter for Multiple Object Measurement

The Insight Journal ◽

10.54294/saa3nn ◽

2013 ◽

Author(s):

Dirk Padfield ◽

James Miller

Keyword(s):

Geometric Features ◽

Image Filter ◽

Mathematical Background ◽

Multiple Object ◽

Object Features ◽

Geometry Image

The itkLabelGeometryImageFilter is a new ITK filter that enables the measurement of geometric features of labeled objects. It calculates features similar to the “regionprops” command of Matlab. It is related to the itkLabelStatisticsImageFilter in that both filters measure features of labeled masks. It differs, however, in that it measures geometric features of the objects themselves rather than statistics of image intensities under the masks defined by the objects. This document describes the mathematical background of the geometric features measured by this filter and describes the framework of the code, which is structured to enable easy expandability as new object features are desired.

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Independent features form integrated objects: Using a novel shape-color “conjunction task” to reconstruct memory resolution for multiple object features simultaneously

10.31234/osf.io/szjr3 ◽

2021 ◽

Author(s):

Aedan Yue Li ◽

Keisuke Fukuda ◽

Morgan Barense

Keyword(s):

High Resolution ◽

Object Representation ◽

Empirical Support ◽

Work Memory ◽

Spatial Location ◽

Feature Integration ◽

Multiple Object ◽

Individual Features ◽

Independent Features ◽

Object Features

Though much progress has been made to understand feature integration, debate remains regarding how objects are represented in mind based on their constituent features. Here, we advance this debate by introducing a novel shape-color “conjunction task” to reconstruct memory resolution for multiple object features simultaneously. In a first experiment, we replicated and extended a classic change detection paradigm using our task. Replicating previous work, memory resolution for individual features was reduced when the number of objects increased, regardless of the number of to-be-remembered features. Extending previous work, we found that high resolution memory near perfect in resemblance to the target was selectively impacted by the number of to-be-remembered features. Applying a statistical model of stochastic dependence, we found evidence primarily for integration of low-resolution feature memories, but less evidence for integration of high-resolution feature memories. These results suggest a resolution trade-off, such that memory resolution for individual features can be higher when those features are represented independently compared to when those features are integrated. In a second experiment which manipulated the nature of distracting information, we examined whether object features were directly bound to each other or by virtue of shared spatial location. Feature integration was disrupted by distractors sharing visual features of target objects but not disrupted when distractors shared spatial location – suggesting that feature integration was driven by direct binding between shape and color features. Our results constrain theoretical models of object representation, providing empirical support for hierarchical representations of both integrated and independent features.

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The Effect of Object Features on Multiple Object Tracking and Identification

Engineering Psychology and Cognitive Ergonomics - Lecture Notes in Computer Science ◽

10.1007/978-3-642-02728-4_22 ◽

2009 ◽

pp. 206-212 ◽

Cited By ~ 3

Author(s):

Tianwei Liu ◽

Wenfeng Chen ◽

Yuming Xuan ◽

Xiaolan Fu

Keyword(s):

Object Tracking ◽

Multiple Object Tracking ◽

Multiple Object ◽

Object Features

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Analysis of semantic features in free-form objects reconstruction

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060415000153 ◽

2015 ◽

Vol 30 (1) ◽

pp. 44-63

Author(s):

Milan Trifunovic ◽

Milos Stojkovic ◽

Miroslav Trajanovic ◽

Miodrag Manic ◽

Dragan Misic ◽

...

Keyword(s):

Semantic Interpretation ◽

Free Form ◽

Geometric Features ◽

Semantic Features ◽

Semantic Model ◽

Bone Implant ◽

Digital Reconstruction ◽

The Right ◽

Object Features ◽

Geometric Elements

AbstractOne of the biggest challenges associated with design and digital reconstruction of free forms comes from uniqueness and unrepeatability of these shapes. During digital reconstruction of these forms, the designer has to choose the right set of geometric features and then compose them in a way that will enable the most accurate reconstruction of the geometry. While doing this, the designer primarily relies on personal experience gained through work with free-form objects of similar geometry. In our opinion, the analysis of free-form objects geometry should rely upon semantic interpretation of their geometric and other features, and the greatest challenge of automation of digital reconstruction and free-form object design in general is closely related to automation of semantic interpretation of geometric and other free-form object features. In this paper, a case of chest bone implant digital reconstruction is presented, where a new semantic model called the active semantic model was used for modeling the meaning of geometric elements, that is, the semantic features of a free-form object. The active semantic model and its analogy-based reasoning algorithms have shown themselves as applicable for the automation of semantic interpretation of the unique, unrepeatable, and unpredictable forms of chest bone. Moreover, this semantic model showed the potential to help automate selecting and composing of geometric features for efficient digital reconstruction of the geometry of free forms.

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Multiple-Object Tracking

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000085 ◽

2011 ◽

Vol 58 (3) ◽

pp. 196-200 ◽

Cited By ~ 15

Author(s):

K. Botterill ◽

R. Allen ◽

P. McGeorge

Keyword(s):

Object Tracking ◽

Cognitive Processes ◽

Spatial Location ◽

Multiple Object Tracking ◽

Tracking Performance ◽

Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

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