scholarly journals Crystal or Jelly? Effect of Color on the Perception of Translucent Materials with Photographs of Real-world Objects

2021 ◽  
Author(s):  
Chenxi Liao ◽  
Masataka Sawayama ◽  
Bei Xiao
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Material Recognition ◽  
Property Estimation ◽  
Material Perception ◽  
Semantic Attribute ◽  
Classification Of Images ◽  
High Level ◽  
Material Property Estimation

Translucent materials are ubiquitous in nature (e.g. teeth, food, wax), but our understanding of translucency perception is limited. Previous work in translucency perception has mainly used monochromatic rendered images as stimuli, which are restricted by their diversity and realism. Here, we measure translucency perception with photographs of real-world objects. Specifically, we use three behavior tasks: binary classification of 'translucent' versus 'opaque', semantic attribute rating of perceptual qualities (see-throughness, glossiness, softness, glow and density), and material categorization. Two different groups of observers finish the three tasks with color or grayscale images. We find that observers' agreements depend on the physical material properties of the objects such that translucent materials generate more inter-observer disagreements. Further, there are more disagreements among observers in the grayscale condition in comparison to that in color condition. We also discover that converting images to grayscale substantially affects the distributions of attribute ratings for some images. Furthermore, ratings of see-throughness, glossiness, and glow could predict individual observers' binary classification of images in both grayscale and color conditions. Lastly, converting images to grayscale alters the perceived material categories for some images such that observers tend to misjudge images of food as non-food and vice versa. Our result demonstrates color is informative about material property estimation and recognition. Meanwhile, our analysis shows mid-level semantic estimation of material attributes might be closely related to high-level material recognition. We also discuss individual differences in our results and highlight the importance of such consideration in material perception.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals Powered knee and ankle prosthesis with indirect volitional swing control enables level-ground walking and crossing over obstacles

2020 ◽  
Vol 5 (44) ◽  
pp. eaba6635 ◽  
Author(s):  
Joel Mendez ◽  
Sarah Hood ◽  
Andy Gunnel ◽  
Tommaso Lenzi
Keyword(s):  
Real World ◽  
Gait Pattern ◽  
Crossing Over ◽  
Volitional Control ◽  
Foot Placement ◽  
Control Approach ◽  
Minimum Jerk ◽  
Foot Clearance ◽  
High Level

Powered prostheses aim to mimic the missing biological limb with controllers that are finely tuned to replicate the nominal gait pattern of non-amputee individuals. Unfortunately, this control approach poses a problem with real-world ambulation, which includes tasks such as crossing over obstacles, where the prosthesis trajectory must be modified to provide adequate foot clearance and ensure timely foot placement. Here, we show an indirect volitional control approach that enables prosthesis users to walk at different speeds while smoothly and continuously crossing over obstacles of different sizes without explicit classification of the environment. At the high level, the proposed controller relies on a heuristic algorithm to continuously change the maximum knee flexion angle and the swing duration in harmony with the user’s residual limb. At the low level, minimum-jerk planning is used to continuously adapt the swing trajectory while maximizing smoothness. Experiments with three individuals with above-knee amputation show that the proposed control approach allows for volitional control of foot clearance, which is necessary to negotiate environmental barriers. Our study suggests that a powered prosthesis controller with intrinsic, volitional adaptability may provide prosthesis users with functionality that is not currently available, facilitating real-world ambulation.

scholarly journals Co-evolution model of Colletotrichum lindemuthianum (melanconiaceae, melanconiales) races that occur in some Brazilian regions

1999 ◽  
Vol 22 (1) ◽  
pp. 115-118 ◽  
Author(s):  
Ana Lilia Alzate-Marin ◽  
Everaldo Gonçalves de Barros ◽  
Maurílio Alves Moreira
Keyword(s):  
Binary Classification ◽  
Random Amplified Polymorphic Dna ◽  
Evolution Model ◽  
Colletotrichum Lindemuthianum ◽  
Phaseolus Vulgaris L ◽  
Differential Cultivars ◽  
The Common ◽  
High Level ◽  
Virulence Diversity

Colletotrichum lindemuthianum, the causal agent of anthracnose in the common bean (Phaseolus vulgaris L.), displays a high level of virulence diversity, which explains the large number of existing pathotypes. Several lines of evidence indicate that such diversity is, at least in part, due to plant and pathogen co-evolution. A co-evolution model based on the binary classification of 25 races identified in Brazil by inoculation of differential cultivars and random amplified polymorphic DNA (RAPD) data is proposed. In this model, races 8 and 64 that infected bean cultivar Cornell 49-242 (Are gene) and Mexico 222 (Mexico I gene) are considered to be sources of two important evolutionary routes. Inferences about undescribed races from Brazil could be made.

scholarly journals High-Level Descriptors for Fall Event Detection Supported by a Multi-Stream Network

2021 ◽  
Vol 12 (1) ◽  
pp. 11-21
Author(s):  
Sarah Almeida Carneiro ◽  
Silvio Jamil Ferzoli Guimarães ◽  
Hélio Pedrini
Keyword(s):  
Event Detection ◽  
Binary Classification ◽  
Quick Response ◽  
Video Classification ◽  
Stream Network ◽  
Stream Architecture ◽  
High Level

The need for assertive video classification has been increasingly in demand. Especially for detecting endangering situations, it is crucial to have a quick response to avoid triggering more serious problems. During this work, we target video classification concerning falls. Our study focuses on the use of high-level descriptors able to correctly characterize the event. These descriptor results will serve as inputs to a multi-stream architecture of VGG-16 networks. Therefore, our proposal is based on the analysis of the best combination of high-level extracted features for the binary classification of videos. This approach was tested on three known datasets, and has proven to yield similar results as other more consuming methods found in the literature.

scholarly journals AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

2021 ◽  
Vol 13 (18) ◽  
pp. 3713
Author(s):  
Jie Liu ◽  
Xin Cao ◽  
Pingchuan Zhang ◽  
Xueli Xu ◽  
Yangyang Liu ◽  
...  
Keyword(s):  
Real World ◽  
Data Sets ◽  
Semantic Features ◽  
Real World Data ◽  
Global Features ◽  
Data Set ◽  
Multi Scale ◽  
Public Data ◽  
High Level

As an essential step in the restoration of Terracotta Warriors, the results of fragments classification will directly affect the performance of fragments matching and splicing. However, most of the existing methods are based on traditional technology and have low accuracy in classification. A practical and effective classification method for fragments is an urgent need. In this case, an attention-based multi-scale neural network named AMS-Net is proposed to extract significant geometric and semantic features. AMS-Net is a hierarchical structure consisting of a multi-scale set abstraction block (MS-BLOCK) and a fully connected (FC) layer. MS-BLOCK consists of a local-global layer (LGLayer) and an improved multi-layer perceptron (IMLP). With a multi-scale strategy, LGLayer can parallel extract the local and global features from different scales. IMLP can concatenate the high-level and low-level features for classification tasks. Extensive experiments on the public data set (ModelNet40/10) and the real-world Terracotta Warrior fragments data set are conducted. The accuracy results with normal can achieve 93.52% and 96.22%, respectively. For real-world data sets, the accuracy is best among the existing methods. The robustness and effectiveness of the performance on the task of 3D point cloud classification are also investigated. It proves that the proposed end-to-end learning network is more effective and suitable for the classification of the Terracotta Warrior fragments.

Binary Classification of Images for Applications in Intelligent 3D Scanning

2017 ◽  
pp. 199-209
Author(s):  
Branislav Vezilić ◽  
Dušan B. Gajić ◽  
Dinu Dragan ◽  
Veljko Petrović ◽  
Srđan Mihić ◽  
...  
Keyword(s):  
Binary Classification ◽  
3D Scanning ◽  
Classification Of Images

scholarly journals Recognition of Real-World Texture Images Under Challenging Conditions With Deep Learning

2018 ◽  
pp. 122-126
Author(s):  
Ozal Yildirim ◽  
Aysegul Ucar ◽  
Ulas Baran Baloglu
Keyword(s):  
Real World ◽  
Motion Tracking ◽  
Recognition Performance ◽  
Research Area ◽  
Performance Evaluations ◽  
System Model ◽  
Proposed Model ◽  
Important Research Area ◽  
Classification Of Images

Images obtained from the real world environments usually have various distortions in image quality. For example, when an object in motion is filmed, or when an environment is being filmed on the move, motion tracking effects occur on the image. Increasing the recognition performance of expert systems, which perform image recognition on data obtained under such conditions, is an important research area. In this study, we propose a Convolutional Neural Network (CNN) based Deep System Model (CNN-DSM) for accurate classification of images under challenging conditions. In the proposed model, a new layer is designed in addition to the classical CNN layers. This layer works as an enhancement layer. For the performance evaluations, various real world surface images were selected from the Curet database. Finally, results are presented and discussed.

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