scholarly journals Psychophysical evidence and perceptual observations show that object recognition is not hierarchical but is a parallel, simultaneous, egalitarian, non-computational system.

2021 ◽  
Author(s):  
Moshe Gur
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Human Performance ◽  
Spatial Information ◽  
Fundamental Property ◽  
Object Perception ◽  
Alternative View ◽  
Wide Range ◽  
Object Appearance ◽  
Perceptual Performance

Object recognition models have at their core similar essential characteristics: feature extraction and hierarchical convergence leading to a code that is unique to each object and immune to variations in the object appearance. To compare computational, biologically-feasible models to human performance, subjects viewed objects displayed at a wide range of orientations and sizes, and were able to recognize them almost perfectly. These empirical results, together with consideration of thought experiments and analysis of everyday perceptual performance, lead to a conclusion that biologically-plausible object perception models do not even come close to matching our perceptual abilities. We can categorize many thousands of objects, discriminate between enormous numbers of different exemplars within each category, and recognize an object as unique although it may appear in countless variations—most of which have never been seen. This seemingly technical, quantitative failure stems from a fundamental property of our perception: the ability to perceive spatial information instantaneously and in parallel, retain details including their relative properties, and yet be able to integrate details into a meaningful percept such as an object. I present an alternative view of object perception whereby objects are represented by responses in primary visual cortex (V1) which is the only cortical area responding to small spatial elements. The rest of the visual cortex is dedicated to scene understanding and interpretation such as constructing 3D percepts from 2D inputs, coding motion, categorization and memories. Since our perception abilities cannot be explained by convergence to 'object cells' or by interactions implemented by axonal transmissions, a parallel-to-parallel field-like process is suggested. In this view, spatial information is not modified by multiple neural interactions but is retained by affecting changes in a 'neural field' which preserves the identity of individual elements while enabling a new holistic percept when these elements change.

scholarly journals Unique spatial integration in mouse primary visual cortex and higher visual areas

2019 ◽  
Author(s):  
Kevin A. Murgas ◽  
Ashley M. Wilson ◽  
Valerie Michael ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Spatial Information ◽  
Spatial Scales ◽  
Spatial Integration ◽  
Global Features ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas

AbstractNeurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the area’s unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

scholarly journals Texture-like representation of objects in human visual cortex

2022 ◽  
Author(s):  
Akshay Vivek Jagadeesh ◽  
Justin Gardner
Keyword(s):  
Visual Cortex ◽  
Human Performance ◽  
Object Perception ◽  
Image Synthesis ◽  
Spatial Arrangement ◽  
Basis Set ◽  
Visual Features ◽  
Deep Convolutional Neural Networks ◽  
Natural Feature ◽  
Complex Features

The human visual ability to recognize objects and scenes is widely thought to rely on representations in category-selective regions of visual cortex. These representations could support object vision by specifically representing objects, or, more simply, by representing complex visual features regardless of the particular spatial arrangement needed to constitute real world objects. That is, by representing visual textures. To discriminate between these hypotheses, we leveraged an image synthesis approach that, unlike previous methods, provides independent control over the complexity and spatial arrangement of visual features. We found that human observers could easily detect a natural object among synthetic images with similar complex features that were spatially scrambled. However, observer models built from BOLD responses from category-selective regions, as well as a model of macaque inferotemporal cortex and Imagenet-trained deep convolutional neural networks, were all unable to identify the real object. This inability was not due to a lack of signal-to-noise, as all of these observer models could predict human performance in image categorization tasks. How then might these texture-like representations in category-selective regions support object perception? An image-specific readout from category-selective cortex yielded a representation that was more selective for natural feature arrangement, showing that the information necessary for object discrimination is available. Thus, our results suggest that the role of human category-selective visual cortex is not to explicitly encode objects but rather to provide a basis set of texture-like features that can be infinitely reconfigured to flexibly learn and identify new object categories.

scholarly journals Overview of Popular Techniques of Raman Spectroscopy and Their Potential in the Study of Plant Tissues

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1537
Author(s):  
Aneta Saletnik ◽  
Bogdan Saletnik ◽  
Czesław Puchalski
Keyword(s):  
Raman Spectroscopy ◽  
Cell Walls ◽  
Structural Changes ◽  
Spatial Information ◽  
Technological Development ◽  
Analytical Techniques ◽  
High Sensitivity ◽  
Plant Tissues ◽  
Wide Range ◽  
Identification Technique

Raman spectroscopy is one of the main analytical techniques used in optical metrology. It is a vibration, marker-free technique that provides insight into the structure and composition of tissues and cells at the molecular level. Raman spectroscopy is an outstanding material identification technique. It provides spatial information of vibrations from complex biological samples which renders it a very accurate tool for the analysis of highly complex plant tissues. Raman spectra can be used as a fingerprint tool for a very wide range of compounds. Raman spectroscopy enables all the polymers that build the cell walls of plants to be tracked simultaneously; it facilitates the analysis of both the molecular composition and the molecular structure of cell walls. Due to its high sensitivity to even minute structural changes, this method is used for comparative tests. The introduction of new and improved Raman techniques by scientists as well as the constant technological development of the apparatus has resulted in an increased importance of Raman spectroscopy in the discovery and defining of tissues and the processes taking place in them.

An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot

2017 ◽  
Vol 17 (3) ◽  
pp. 247-257 ◽  
Author(s):  
Evan B. Clark ◽  
Nathan E. Bramall ◽  
Brent Christner ◽  
Chris Flesher ◽  
John Harman ◽  
...  
Keyword(s):  
Human Performance ◽  
Autonomous Underwater Vehicles ◽  
Field Performance ◽  
Planetary Science ◽  
Operating Environment ◽  
Intelligent Algorithm ◽  
Future State ◽  
Science Operations ◽  
Wide Range ◽  
Matanuska Glacier

AbstractThe development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa.

Level of Automation Forms a Key Aspect of Autonomy Design

2017 ◽  
Vol 12 (1) ◽  
pp. 29-34 ◽  
Author(s):  
Mica R. Endsley
Keyword(s):  
Situation Awareness ◽  
Interface Design ◽  
Human Performance ◽  
Human Cognition ◽  
Level Of Automation ◽  
Out Of The Loop ◽  
Wide Range ◽  
Levels Of Automation ◽  
And Performance ◽  
The Impact

The concept of different levels of automation (LOAs) has been pervasive in the automation literature since its introduction by Sheridan and Verplanck. LOA taxonomies have been very useful in guiding understanding of how automation affects human cognition and performance, with several practical and theoretical benefits. Over the past several decades a wide body of research has been conducted on the impact of various LOAs on human performance, workload, and situation awareness (SA). LOA has a significant effect on operator SA and level of engagement that helps to ameliorate out-of-the-loop performance problems. Together with other aspects of system design, including adaptive automation, granularity of control, and automation interface design, LOA is a fundamental design characteristic that determines the ability of operators to provide effective oversight and interaction with system autonomy. LOA research provides a solid foundation for guiding the creation of effective human–automation interaction, which is critical for the wide range of autonomous and semiautonomous systems currently being developed across many industries.

scholarly journals Universal void density profiles from simulation and SDSS

2014 ◽  
Vol 11 (S308) ◽  
pp. 542-545 ◽  
Author(s):  
S. Nadathur ◽  
S. Hotchkiss ◽  
J. M. Diego ◽  
I. T. Iliev ◽  
S. Gottlöber ◽  
...  
Keyword(s):  
Fundamental Property ◽  
Watershed Algorithm ◽  
Void Size ◽  
Self Similarity ◽  
Density Profiles ◽  
Watershed Transform ◽  
Future Studies ◽  
Main Galaxy ◽  
Wide Range ◽  
Self Similar

AbstractWe discuss the universality and self-similarity of void density profiles, for voids in realistic mock luminous red galaxy (LRG) catalogues from the Jubilee simulation, as well as in void catalogues constructed from the SDSS LRG and Main Galaxy samples. Voids are identified using a modified version of the ZOBOV watershed transform algorithm, with additional selection cuts. We find that voids in simulation areself-similar, meaning that their average rescaled profile does not depend on the void size, or – within the range of the simulated catalogue – on the redshift. Comparison of the profiles obtained from simulated and real voids shows an excellent match. The profiles of real voids also show auniversalbehaviour over a wide range of galaxy luminosities, number densities and redshifts. This points to a fundamental property of the voids found by the watershed algorithm, which can be exploited in future studies of voids.

Adult Cortical Dynamics

1998 ◽  
Vol 78 (2) ◽  
pp. 467-485 ◽  
Author(s):  
CHARLES D. GILBERT
Keyword(s):  
Visual Cortex ◽  
Functional Properties ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Visual Space ◽  
Visual Pathway ◽  
Local Features ◽  
Spatial Integration ◽  
Cortical Dynamics ◽  
Wide Range

Gilbert, Charles D. Adult Cortical Dynamics. Physiol. Rev. 78: 467–485, 1998. — There are many influences on our perception of local features. What we see is not strictly a reflection of the physical characteristics of a scene but instead is highly dependent on the processes by which our brain attempts to interpret the scene. As a result, our percepts are shaped by the context within which local features are presented, by our previous visual experiences, operating over a wide range of time scales, and by our expectation of what is before us. The substrate for these influences is likely to be found in the lateral interactions operating within individual areas of the cerebral cortex and in the feedback from higher to lower order cortical areas. Even at early stages in the visual pathway, cells are far more flexible in their functional properties than previously thought. It had long been assumed that cells in primary visual cortex had fixed properties, passing along the product of a stereotyped operation to the next stage in the visual pathway. Any plasticity dependent on visual experience was thought to be restricted to a period early in the life of the animal, the critical period. Furthermore, the assembly of contours and surfaces into unified percepts was assumed to take place at high levels in the visual pathway, whereas the receptive fields of cells in primary visual cortex represented very small windows on the visual scene. These concepts of spatial integration and plasticity have been radically modified in the past few years. The emerging view is that even at the earliest stages in the cortical processing of visual information, cells are highly mutable in their functional properties and are capable of integrating information over a much larger part of visual space than originally believed.

scholarly journals From Virtual Field Trip to Geologically-Reasoned Decisions in Yosemite Valley

2021 ◽  
Author(s):  
Nicolas C. Barth ◽  
Greg M. Stock ◽  
Kinnari Atit
Keyword(s):  
Spatial Information ◽  
Opportunity To Learn ◽  
Field Trip ◽  
Google Earth ◽  
Learning Goals ◽  
Spatial Skills ◽  
Yosemite Valley ◽  
Wide Range ◽  
Virtual Field ◽  
Virtual Field Trip

Abstract. This study highlights a Geology of Yosemite Valley virtual field trip (VFT) and companion exercises produced as a four-part module to substitute for physical field experiences. The VFT is created as an Earth project in Google Earth Web, a versatile format that allows access through a web browser or Google Earth app with the sharing of an internet address. Many dynamic resources can be used for VFT stops through use of the Google Earth Engine (global satellite imagery draped on topography, 360° street-level imagery, user-submitted 360° photospheres). Images, figures, videos, and narration can be embedded into VFT stops. Hyperlinks allow for a wide range of external resources to be incorporated; optional background resources help reduce the knowledge gap between general public and upper-division students, ensuring VFTs can be broadly accessible. Like many in-person field trips, there is a script with learning goals for each stop, but also an opportunity to learn through exploration as the viewer can dynamically change their vantage at each stop (i.e. guided discovery learning). This interactive VFT format scaffolds students’ spatial skills and encourages attention to be focused on a stop’s critical spatial information. The progression from VFT to mapping exercise to geologically-reasoned decision-making results in high quality student work; students find it engaging, enjoyable, and educational.

scholarly journals Mapping of the distribution and development of phytopathogens in wheat and barley by the NextGIS application

2020 ◽  
pp. 61-66
Author(s):  
О. Yu. Kremneva ◽  
I. A. Kostenko ◽  
A. A. Pachkin ◽  
R. Yu. Danilov ◽  
A. V. Ponomarev ◽  
...  
Keyword(s):  
Leaf Spot ◽  
Spatial Information ◽  
Plant Protection ◽  
Brown Spot ◽  
Field Surveys ◽  
Grain Crops ◽  
Damage Degree ◽  
Rust Diseases ◽  
Wide Range ◽  
Causative Agents

There have been carried out the route surveys to assess the development and distribution of the main pathogens of wheat and barley in 57 districts ofKrasnodar,StavropolTerritoriesand Rostov Region. The collection, organization and analysis of information on the damage degree of the production and breeding sowings of these grain crops by the main pathogens was conducted using a single spatial information environment designed by the Russian company “NextGIS”. During the field surveys, there have been included the data on the resistance to causative agents of yellow leaf spot of wheat, wheat Septoria leaf spot, powdery mildew on wheat and barley, rust diseases on wheat and barley, net spot of barley, and brown spot of barley into the program. In addition, while filling in the survey card, there were taken into account the date and time of the assessment, the phase of development of the grain crop, the variety; there was carried out a photographic fixation of the examined plants. In the field, there was used the freely distributed NextGIS Mobile software, installed on mobile digital devices (smartphones) running by the Android operating system. According to the results of field surveys using the NextGIS QGIS full-featured desktop geographic information system there were prepared thematic cartographic materials of the main wheat and barley pathogens in the southern region ofRussia. The collected long-term data of phytosanitary surveys will allow conducting a temporary analysis of the spatial distribution of pathogens in the studied area. This information can be useful for specialists dealing with plant protection, for employees of breeding institutions who develop varieties resistant to economically significant diseases of grain crops. The data can be used also by a wide range of specialists engaged in plant breeding who conduct field surveys and account any parameters of plant conditions, harmful facilities and the environment, as well as by the consumers of such information.

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