Costs-Sensitive Classification in Multistage Classifier with Fuzzy Observations of Object Features

2011 ◽  
pp. 245-252 ◽  
Author(s):  
Robert Burduk
Keyword(s):  
Object Features ◽  
Multistage Classifier

Evaluation of the expediency of using thermal i magery data for decryption exogenous processes and vegetation

2018 ◽  
Vol 933 (3) ◽  
pp. 52-62
Author(s):  
V.S. Tikunov ◽  
I.A. Rylskiy ◽  
S.B. Lukatzkiy
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Data Extraction ◽  
Spectral Band ◽  
Information Provision ◽  
Lidar Data ◽  
Thermal Imagery ◽  
Scanning Lidar ◽  
Field Works ◽  
Object Features

Innovative methods of aerial surveys changed approaches to information provision of projecting dramatically in last years. Nowadays there are several methods pretending to be the most efficient for collecting geospatial data intended for projecting – airborne laser scanning (LIDAR) data, RGB aerial imagery (forming 3D pointclouds) and orthoimages. Thermal imagery is one of the additional methods that can be used for projecting. LIDAR data is precise, it allows us to measure relief even under the vegetation, or to collect laser re-flections from wires, metal constructions and poles. Precision and completeness of the DEM, produced from LIDAR data, allows to define relief microforms. Airborne imagery (visual spectrum) is very widespread and can be easily depicted. Thermal images are more strange and less widespread, they use different way of image forming, and spectral features of ob-jects can vary in specific ways. Either way, the additional spectral band can be useful for achieving additional spatial data and different object features, it can minimize field works. Here different aspects of thermal imagery are described in comparison with RGB (visual) images, LIDAR data and GIS layers. The attempt to estimate the feasibility of thermal imag-es for new data extraction is made.

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

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.

OBEliSK: Novel Knowledgebase of Object Features and Exchange Strategies

2015 ◽  
pp. 448-454
Author(s):  
David Cabañeros Blanco ◽  
Ana Belén Rodríguez Arias ◽  
Víctor Fernández-Carbajales Cañete ◽  
Joaquín Canseco Suárez
Keyword(s):  
Object Features

Tracking the object features in video based on OpenCV

2016 ◽  
Author(s):  
M. Michalko ◽  
J. Onuska ◽  
A. Lavrin ◽  
D. Cymbalak ◽  
O. Kainz
Keyword(s):  
Object Features

scholarly journals Sensory computations in the cuneate nucleus of macaques

2021 ◽  
Vol 118 (49) ◽  
pp. e2115772118
Author(s):  
Aneesha K. Suresh ◽  
Charles M. Greenspon ◽  
Qinpu He ◽  
Joshua M. Rosenow ◽  
Lee E. Miller ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Receptive Fields ◽  
Nerve Fibers ◽  
Cuneate Nucleus ◽  
Local Skin ◽  
Response Properties ◽  
Conventional View ◽  
Random Dot Patterns ◽  
Object Features ◽  
Spatiotemporal Response

Tactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures—the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in the CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, the CN plays a key role in processing tactile information.

scholarly journals Colors and Handles: How Action Primes Perception

2021 ◽  
Vol 15 ◽  
Author(s):  
Marcello Costantini ◽  
Davide Quarona ◽  
Corrado Sinigaglia
Keyword(s):  
Priming Effect ◽  
Perceptual Judgment ◽  
Motor Training ◽  
Perceptual Judgments ◽  
Object Features

How deeply does action influence perception? Does action performance affect the perception of object features directly related to action only? Or does it concern also object features such as colors, which are not held to directly afford action? The present study aimed at answering these questions. We asked participants to repeatedly grasp a handled mug hidden from their view before judging whether a visually presented mug was blue rather than cyan. The motor training impacted on their perceptual judgments, by speeding participants’ responses, when the handle of the presented mug was spatially aligned with the trained hand. The priming effect did not occur when participants were trained to merely touch the mug with their hand closed in a fist. This indicates that action performance may shape the perceptual judgment on object features, even when these features are colors and do not afford any action. How we act on surrounding objects is therefore not without consequence for how we experience them.

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