Tests of a Theory of Human Image Understanding: Part I the Perception of Colored and Partial Objects

1986 ◽  
Vol 30 (3) ◽  
pp. 297-300
Author(s):  
Ginny Ju ◽  
Irving Biederman
Keyword(s):  
Pattern Recognition ◽  
Surface Characteristics ◽  
Image Understanding ◽  
Rapid Identification ◽  
Complex Objects ◽  
Object A ◽  
Line Drawings ◽  
Full Color ◽  
Full Complement ◽  
Human Image

Object recognition can be conceptualized as a process in which the perceptual input is successfully matched with a stored representation of the object. A theory of pattern recognition, Recognition by Components(RBC) assumes that objects are represented as simple volumetric primatives (e.g., bricks, cylinders, etc.) in specifed relations to each other. According to RBC, speeded recognition should be possible from only a few components, as long as those components uniquely identify an object. Neither the full complement of an object's components, nor the object's surface characteristics (e.g., color and texture) need be present for rapid identification. The results from two experiments on the perception of briefly presented objects are offered for supporting the sufficiency of the theory. Line drawings are identified about as rapidly and as accurately as full color slides. Partial objects could be rapidly (though not optimally) identified. Complex objects are more readily identified than simple objects.

Cellular Automata Algorithms for Digital Image Processing

Biometrics ◽  
2017 ◽  
pp. 382-402
Author(s):  
Petre Anghelescu
Keyword(s):  
Image Processing ◽  
Cellular Automata ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Understanding ◽  
Software Application ◽  
Detection Algorithms ◽  
Human Image ◽  
The Impact

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

Pattern Recognition and Color Modularity in Mathematics and Art

2020 ◽  
Vol 11 (1) ◽  
pp. 59-70
Author(s):  
Jean Constant
Keyword(s):  
Pattern Recognition ◽  
Visual Communication ◽  
Complex Objects ◽  
Mathematical Visualization ◽  
Pointed Star

Pattern recognition is a useful tool for mathematics, mathematical visualization, and art. After a brief description of Bongard methodology in the field of pattern recognition, the author combines consequential elements of this technique and principles of color modularity to transform a five- to a ten-pointed star polygon, and in doing so attains a rich, persuasive visualization. This process that can be repeated for more complex objects brought to light additional valuable implication for mathematics visualization, visual communication, and pattern recognition practices.

scholarly journals Ear Detection Using Convolutional Neural Network on Graphs with Filter Rotation

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5510 ◽  
Author(s):  
Arkadiusz Tomczyk ◽  
Piotr S. Szczepaniak
Keyword(s):  
Semantic Segmentation ◽  
Gaussian Mixture ◽  
Image Understanding ◽  
Quality Level ◽  
Image Content ◽  
Continuous Space ◽  
Ear Detection ◽  
Human Image ◽  
Novel Concept ◽  
Effective Models

Geometric deep learning (GDL) generalizes convolutional neural networks (CNNs) to non-Euclidean domains. In this work, a GDL technique, allowing the application of CNN on graphs, is examined. It defines convolutional filters with the use of the Gaussian mixture model (GMM). As those filters are defined in continuous space, they can be easily rotated without the need for some additional interpolation. This, in turn, allows constructing systems having rotation equivariance property. The characteristic of the proposed approach is illustrated with the problem of ear detection, which is of great importance in biometric systems enabling image based, discrete human identification. The analyzed graphs were constructed taking into account superpixels representing image content. This kind of representation has several advantages. On the one hand, it significantly reduces the amount of processed data, allowing building simpler and more effective models. On the other hand, it seems to be closer to the conscious process of human image understanding as it does not operate on millions of pixels. The contributions of the paper lie both in GDL application area extension (semantic segmentation of the images) and in the novel concept of trained filter transformations. We show that even significantly reduced information about image content and a relatively simple, in comparison with classic CNN, model (smaller number of parameters and significantly faster processing) allows obtaining detection results on the quality level similar to those reported in the literature on the UBEAR dataset. Moreover, we show experimentally that the proposed approach possesses in fact the rotation equivariance property allowing detecting rotated structures without the need for labor consuming training on all rotated and non-rotated images.

Age, Familiarity, and Visual Processing Schemes

1978 ◽  
Vol 47 (2) ◽  
pp. 591-595 ◽  
Author(s):  
Diane T. De Haven ◽  
Cynthia Roberts-Gray
Keyword(s):  
Visual Processing ◽  
Partial Report ◽  
Age Difference ◽  
Perceptual Process ◽  
White Line ◽  
Line Drawings ◽  
Black And White ◽  
Full Color

In a partial-report task adults and 5-yr.-old children identified stimuli of two types (common objects and familiar common objects) in two representations (black-and-white line drawings or full color photographs). It was hypothesized that familiar items and photographic representation would enhance the children's accuracy. Although both children and adults were more accurate when the stimuli were from the familiar set, children performed poorly in all stimulus conditions. Results suggest that the age difference in this task reflects the “concrete” nature of the perceptual process in children.

scholarly journals Processing of data from complex objects through pattern recognition methods

2018 ◽  
Vol 2 (1) ◽  
pp. 30-38
Author(s):  
Mariya Ivanova Konsulova - Bakalova
Keyword(s):  
Pattern Recognition ◽  
Complex Objects ◽  
Pattern Recognition Methods ◽  
Unified Method

 In the description of complex objects, we need methods which could reflect the complex interconnections between components and sift out if possible those of them which are substantial for the specific application. It is offered in this publication the pattern recognition methods should be used as a unified method for processing of data from complex objects. The proposed algorithm may be used in the recognition of the condition of objects of various nature. The indicated examples prove the practical applicability of the methodology as they represent the solution of specific practical problems.

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