EVALUATION OF KANSEI RETRIEVAL METHOD FOR QUANTITATIVE FEATURE EXTRACTION

2004 ◽  
Vol 05 (03) ◽  
pp. 313-327 ◽  
Author(s):  
Akihiro Miyakawa ◽  
Kaoru Sugita ◽  
Tomoyuki Ishida ◽  
Yoshitaka Shibata
Keyword(s):  
Feature Extraction ◽  
Image Coding ◽  
Design Pattern ◽  
Visual Features ◽  
Multimedia Database ◽  
Visual Feature ◽  
Retrieval Method ◽  
Quantitative Feature ◽  
Deviation Rate ◽  
Feature Values

In this paper, we propose a Kansei retrieval method based on the design pattern of traditional Japanese crafting object to provide a user with the desired presentation space in digital traditional Japanese crafting system. The visual quantitative feature values are extracted by using Visual Pattern Image Coding (VPIC). These values include the total number, the frequency, the dispersion rate and the deviation rate for different edges. The quantitative feature values for traditional Japanese crafting objects are registered in the multimedia database and the relation between Kansei words and the visual feature of traditional Japanese crafting objects are analyzed by using the questionnaire. Then, the visual features are compared with the quantitative feature values. Through the above process, we can find the relation between the design pattern components and edge types using VPIC. By finding this relation, the Kansei retrieval method can be realized.

Research on Mars Surface Image Visual Feature Extraction Algorithm

2013 ◽  
Vol 683 ◽  
pp. 801-804 ◽  
Author(s):  
Ying Hou ◽  
Gui Cai Wang
Keyword(s):  
Feature Extraction ◽  
Visual Features ◽  
Visual Feature ◽  
Illumination Change ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Surface Image ◽  
Rotation Transformation ◽  
Visual Feature Extraction ◽  
Surface Environment

Visual feature extraction was the basic of mars surface topography reconstruction. The deep research was done to extract mars surface image visual feature in the unstructured mars surface environment. On this basis, the paper gave the mars surface image visual feature extraction algorithm. The experimental results show that the algorithm has good adaptability to illumination change and rotation transformation of mars surface image. Meanwhile, the paper could extract the abundant visual features of mars surface image.

Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

2019 ◽  
Author(s):  
Sushrut Thorat
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Large Degree ◽  
Visual Features ◽  
Visual Feature ◽  
Visual Stream ◽  
Feature Information ◽  
Ventral Visual Stream ◽  
Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

The decoupling visual feature extraction of dynamic three-dimensional V-type seam for gantry welding robot

2015 ◽  
Vol 80 (9-12) ◽  
pp. 1741-1749 ◽  
Author(s):  
Haiyong Chen ◽  
Weipeng Liu ◽  
Liying Huang ◽  
Guansheng Xing ◽  
Meng Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Three Dimensional ◽  
Visual Feature ◽  
Welding Robot ◽  
Visual Feature Extraction

Tactile image computation using a feature extraction algorithm

Robotica ◽  
1992 ◽  
Vol 10 (3) ◽  
pp. 241-254
Author(s):  
M. Mehdian
Keyword(s):  
Feature Extraction ◽  
Local Property ◽  
Recognition System ◽  
Image Feature ◽  
Image Size ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Tactile Image ◽  
Feature Values ◽  
Image Computation

SUMMARYA binary tactile image feature extraction algorithm using image primitive notation and perceptrons is presented. The basic image segments are defined as geometric factors by which the image structure is described so that effective feature values such as image shape, image size, perimeter and texture may be extracted on the basis of local image computation. The local property of the tactile image computation is evaluated by the concept called order of the perceptrons and based on this feature extraction algorithm, an efficient tactile image recognition system is realised.

Comparison of Visual Feature Extraction Methods of Sperms in Semen Microscopic Videos

2020 ◽  
Author(s):  
Xialin Li ◽  
Chen Li ◽  
Wenwei Zhao ◽  
Yuzhuo Gu ◽  
Jindong Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Visual Feature ◽  
Visual Feature Extraction
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