The Art of Shaping Materials

2019 ◽  
Vol 8 (3-4) ◽  
pp. 407-433 ◽  
Author(s):  
Filipp Schmidt
Keyword(s):  
Visual Perception ◽  
Human Skin ◽  
Material Properties ◽  
Vision Research ◽  
Texture Features ◽  
Viscous Fluids ◽  
Shape Features ◽  
Visual Artists ◽  
Material Perception ◽  
Single Block

Material perception — the visual perception of stuff — is an emerging field in vision research. We recognize materials from shape, color and texture features. This paper is a selective review and discussion of how artists have been using shape features to evoke vivid impressions of specific materials and material properties. A number of examples are presented in which visual artists render materials or their transformations, such as soft human skin, runny or viscous fluids, or wrinkled cloth. They achieve this by expressing the telltale shape features of these materials and transformations, often by carving them from a single block of marble or wood. Vision research has just begun to investigate these very shape features, making material perception a prime example of how art can inform science.

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

2018 ◽  
pp. 162-183
Author(s):  
Vanika Singhal ◽  
Preety Singh
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Principal Component ◽  
Texture Features ◽  
Automatic Detection ◽  
Support Vector ◽  
Shape Features ◽  
Geometrical Properties ◽  
Correlation Based Feature Selection ◽  
Human Interpretation

Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent shape features, Correlation based Feature Selection is applied. Principal Component Analysis is applied on the texture features to reduce their dimensionality. Support Vector Machine is used for classification. It is observed that 16 shape features are able to give a classification accuracy of 92.3% and that changes in the geometrical properties of the nucleus emerge as significant features contributing towards detecting a malignant lymphocyte.

A New Object Recognition Approach Using Combination of Texture, Color and Shape Features

2015 ◽  
Vol 761 ◽  
pp. 111-115
Author(s):  
Abdul Kadir ◽  
K.A.A. Aziz ◽  
Irianto
Keyword(s):  
Texture Features ◽  
Shape Features ◽  
Accuracy Rate ◽  
New Approach ◽  
Orthogonal Moments ◽  
Krawtchouk Moments ◽  
Image Histogram ◽  
Color Features ◽  
Tchebichef Moments ◽  
Skewness And Kurtosis

This paper reports a new approach for recognizing objects by using combination of texture, color and shape features. Texture features were generated by applying statistical calculation on the image histogram. Color features were computed by using mean, standard deviation, skewness and kurtosis. Shape features were generated using combination of Shen features and basic shapes such as eccentricity and dispersion. The total features were used much less compared to approaches that involve orthogonal moments such as Krawtchouk moments, Zernike moments, or Tchebichef moments. Testing was done by using a dataset that contains 53 kinds of objects. All objects contained in the dataset were various things that can be found in supermarkets or produced by manufacturing. The result shows that the system gave 98.11% of accuracy rate.

scholarly journals Visual and Haptic Representations of Material Properties

2013 ◽  
Vol 26 (5) ◽  
pp. 429-455 ◽  
Author(s):  
Elisabeth Baumgartner ◽  
Christiane B. Wiebel ◽  
Karl R. Gegenfurtner
Keyword(s):  
Principal Components ◽  
Material Properties ◽  
Perceptual Space ◽  
Visual Condition ◽  
Material Recognition ◽  
Haptic Modality ◽  
Fine Grained ◽  
Haptic Exploration ◽  
Material Perception

Research on material perception has received an increasing amount of attention recently. Clearly, both the visual and the haptic sense play important roles in the perception of materials, yet it is still unclear how both senses compare in material perception tasks. Here, we set out to investigate the degree of correspondence between the visual and the haptic representations of different materials. We asked participants to both categorize and rate 84 different materials for several material properties. In the haptic case, participants were blindfolded and asked to assess the materials based on haptic exploration. In the visual condition, participants assessed the stimuli based on their visual impressions only. While categorization performance was less consistent in the haptic condition than in the visual one, ratings correlated highly between the visual and the haptic modality. PCA revealed that all material samples were similarly organized within the perceptual space in both modalities. Moreover, in both senses the first two principal components were dominated by hardness and roughness. These are two material features that are fundamental for the haptic sense. We conclude that although the haptic sense seems to be crucial for material perception, the information it can gather alone might not be quite fine-grained and rich enough for perfect material recognition.

scholarly journals Expectations affect the perception of material properties

2019 ◽  
Author(s):  
Lorilei M. Alley ◽  
Alexandra C. Schmid ◽  
Katja Doerschner
Keyword(s):  
Optical Properties ◽  
Visual Perception ◽  
Material Properties ◽  
Time Data ◽  
Material Appearance ◽  
Typical Material ◽  
Material Behaviors ◽  
Set Up ◽  
High Level ◽  
Cue Conflict

ABSTRACTMany objects that we encounter have ‘typical’ material qualities: spoons are hard, pillows are soft, and jell-O dessert is wobbly. Over a lifetime of experiences, strong associations between an object and its typical material properties may be formed, and these associations not only include how glossy, rough or pink an object is, but also how it behaves under force: we expect knocked over vases to shatter, popped bike tires to deflate, and gooey grilled cheese to hang between two slices of bread when pulled apart. Here we ask how such rich visual priors affect the visual perception of material qualities and present a particularly striking example of expectation violation. In a cue conflict design, we pair computer-rendered familiar objects with surprising material behaviors (a linen curtain shattering, a porcelain teacup wrinkling, etc.) and find that material qualities are not solely estimated from the object’s kinematics (i.e. its physical (atypical) motion while shattering, wrinkling, wobbling etc.); rather, material appearance is sometimes “pulled” towards the “native” motion, shape, and optical properties that are associated with this object. Our results, in addition to patterns we find in response time data, suggest that visual priors about materials can set up high-level expectations about complex future states of an object and show how these priors modulate material appearance.

Indexing of Image Features Using Quadtree

2016 ◽  
pp. 185-203
Author(s):  
N. Puviarasan ◽  
R. Bhavani
Keyword(s):  
Extraction Method ◽  
Dimensional Space ◽  
Texture Features ◽  
Image Features ◽  
High Dimensional ◽  
Shape Features ◽  
Indexing Methods ◽  
Feature Extraction Method ◽  
Indexing Method ◽  
Color Texture

In Content based image retrieval (CBIR) applications, the idea of indexing is mapping the extracted descriptors from images into a high-dimensional space. In this paper, visual features like color, texture and shape are considered. The color features are extracted using color coherence vector (CCV), texture features are obtained from Segmentation based Fractal Texture Analysis (SFTA). The shape features of an image are extracted using the Fourier Descriptors (FD) which is the contour based feature extraction method. All features of an image are then combined. After combining the color, texture and shape features using appropriate weights, the quadtree is used for indexing the images. It is experimentally found that the proposed indexing method using quadtree gives better performance than the other existing indexing methods.

Approach of Using Texture and Shape for Image Retrieval

2013 ◽  
pp. 30-45
Author(s):  
Gang Zhang ◽  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Feature Integration ◽  
Shape Features ◽  
Important Research ◽  
Information Redundancy ◽  
Single Feature ◽  
Descriptor Approach

Feature integration is one of important research contents in content-based image retrieval. Single feature extraction and description is foundation of the feature integration. Features from a single feature extraction approach are a single feature or composite features, whether integration features are more discriminative than them or not. An approach of integrating shape and texture features was presented and used to study these problems. Gabor wavelet transform with minimum information redundancy was used to extract texture features, which would be used for feature analyses. Fourier descriptor approach with brightness was used to extract shape features. Then both features were integrated in parallel by weights. Comparisons were carried out among the integration features, the texture features, and the shape features, so that discrimination of the integration features can be testified.

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