scholarly journals Special Edition. Pattern Recognition and Image Understanding. 4. Motion Detection Mechanisms in the Human Visual System.

1994 ◽  
Vol 48 (2) ◽  
pp. 157-163 ◽  
Author(s):  
Shin'ya Nishida ◽  
Takao Sato
Keyword(s):  
Pattern Recognition ◽  
Visual System ◽  
Motion Detection ◽  
Human Visual System ◽  
Image Understanding ◽  
Special Edition ◽  
Detection Mechanisms

scholarly journals Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

2016 ◽  
Vol 24 (1) ◽  
pp. 143-182 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Mark Johnston
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Genetic Programming ◽  
Visual System ◽  
Image Classification ◽  
Human Visual System ◽  
Binary Classification ◽  
Programming Approach ◽  
Data Sets ◽  
New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

Two-Stroke Apparent Motion

2017 ◽  
Author(s):  
George Mather
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Apparent Motion ◽  
Motion Detection ◽  
Human Visual System ◽  
Motion Aftereffect ◽  
Temporal Response ◽  
Motion Sensing ◽  
Receptive Field Properties ◽  
Directional Motion

“Two-stroke” apparent motion is a powerful illusion of directional motion generated by alternating just two animation frames, which occurs when a brief blank interframe interval is inserted at alternate frame transitions. This chapter discusses this illusion, which can be explained in terms of the receptive field properties of motion-sensing neurons in the human visual system. The temporal response of these neurons contains both an excitatory phase and an inhibitory phase; when the timing of the interframe interval just matches the switch in response sign, the illusion occurs. Concepts covered in this chapter include four-stroke as well as two-stroke apparent motion, motion aftereffect, and motion detection.

Technical Vision Model of the Visual Systems for Industry Application

2021 ◽  
pp. 288-310
Author(s):  
Oleg Sytnik ◽  
Vladimir Kartashov
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Visual System ◽  
Mathematical Models ◽  
Human Visual System ◽  
Vision System ◽  
Vision Systems ◽  
Industrial Systems ◽  
Visual Systems ◽  
Technical Vision

The problems of highlighting the main informational aspects of images and creating their adequate models are discussed in the chapter. Vision systems can receive information about an object in different frequency ranges and in a form that is not accessible to the human visual system. Vision systems distort the information contained in the image. Therefore, to create effective image processing and transmission systems, it is necessary to formulate mathematical models of signals and interference. The chapter discusses the features of perception by the human visual system and the issues of harmonizing the technical characteristics of industrial systems for receiving and transmitting images. Methods and algorithms of pattern recognition are discussed. The problem of conjugation of the characteristics of the technical vision system with the consumer of information is considered.

A VISUAL MODEL FOR PATTERN RECOGNITION

1992 ◽  
Vol 03 (supp01) ◽  
pp. 31-39
Author(s):  
Luigi Stringa
Keyword(s):  
Pattern Recognition ◽  
Visual System ◽  
Human Visual System ◽  
General Model ◽  
Recognition System ◽  
Experimental Results ◽  
Visual Model ◽  
Optical Recognition ◽  
Processing Networks

A general model for an optical recognition system capable of simultaneous recognition of patterns at different resolution levels is outlined. The model is based on two hierarchic stages of processing networks and presents interesting analogies with the human visual system. Illustrative applications and preliminary experimental results are also briefly discussed.

Human Brain Regions Involved in Direction Discrimination

1998 ◽  
Vol 79 (5) ◽  
pp. 2749-2765 ◽  
Author(s):  
L. Cornette ◽  
P. Dupont ◽  
A. Rosier ◽  
S. Sunaert ◽  
P. Van Hecke ◽  
...  
Keyword(s):  
Visual System ◽  
Human Brain ◽  
Motion Detection ◽  
Human Visual System ◽  
Brain Regions ◽  
Successive Discrimination ◽  
Functional Specialization ◽  
Direction Discrimination ◽  
Cortical Regions ◽  
The Right

Cornette, L., P. Dupont, A. Rosier, S. Sunaert, P. Van Hecke, J. Michiels, L. Mortelmans, and G. A. Orban. Human brain regions involved in direction discrimination. J. Neurophysiol. 79: 2749–2765, 1998. To obtain further evidence for the functional specialization and task-dependent processing in the human visual system, we used positron emission tomography to compare regional cerebral blood flow in two direction discrimination tasks and four control tasks. The stimulus configuration, which was identical in all tasks, included the motion of a random dot pattern, dimming of a fixation point, and a tone burst. The discrimination tasks comprised the identification of motion direction and successive direction discrimination. The control tasks were motion detection, dimming detection, tone detection, and passive viewing. There was little difference in the activation patterns evoked by the three detection tasks except for decreased activity in the parietal cortex during the detection of a tone. Thus attention to a nonvisual stimulus modulated different visual cortical regions nonuniformly. Comparison of successive discrimination with motion detection yielded significant activation in the right fusiform gyrus, right lingual gyrus, right frontal operculum, left inferior frontal gyrus, and right thalamus. The fusiform and opercular activation sites persisted even after subtracting direction identification from successive discrimination, indicating their involvement in temporal comparison. Functional magnetic resonance imaging (fMRI) experiments confirmed the weak nature of the activation of human MT/V5 by successive direction discrimination but also indicated the involvement of an inferior satellite of human MT/V5. The fMRI experiments moreover confirmed the involvement of human V3A, lingual, and parietal regions in successive discrimination. Our results provide further evidence for the functional specialization of the human visual system because the cortical regions involved in direction discrimination partially differ from those involved in orientation discrimination. They also support the principle of task-dependent visual processing and indicate that the right fusiform gyrus participates in temporal comparison, irrespective of the stimulus attribute.

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