Fusion and Rivalry of Illusory Contours

Perception ◽  
1973 ◽  
Vol 2 (2) ◽  
pp. 235-247 ◽  
Author(s):  
J P Harris ◽  
R L Gregory
Keyword(s):  
Stereo Vision ◽  
Striate Cortex ◽  
Three Dimensional ◽  
Scene Analysis ◽  
General Question ◽  
Feature Detector ◽  
Illusory Contours ◽  
Analysis Strategy ◽  
Detector Activity ◽  
Low Probability

The general question is raised: “Are visual contours given directly from striate-cortex feature-detector activity?‘’ Phenomena of ‘subjective’ or ‘cognitive’ contours are examined to challenge this view, on the ground that contours can be extrapolations across low-probability gaps. The contours may be curved and may have poor ‘gestalt’ qualities—so ‘gestalt closure’ is not appropriate, but may be a sub-class of these phenomena. It is suggested that these illusory contours (and brightness differences) are generated by perceptually postulated masking objects—these being part of perceptual ‘scene analysis strategy’, since strong evidence for nearer objects is provided by improbable gaps. Experiments are reported, in which each eye is given a different ‘cognitive’ contour figure such that there are disparate but illusory contours. It is found that these are fused to give three-dimensional illusory. surfaces bowing in front of the display. Masking objects must be in front of gaps; what happens here with reversal of stereo depth? Switching the eyes often gives rivalry of the illusory contours when masking is incompatible with the stereo depth. Implications for normal stereo vision are discussed.

scholarly journals High-Accuracy Recognition and Localization of Moving Targets in an Indoor Environment Using Binocular Stereo Vision

2021 ◽  
Vol 10 (4) ◽  
pp. 234
Author(s):  
Jing Ding ◽  
Zhigang Yan ◽  
Xuchen We
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Target Localization ◽  
Parallel Structure ◽  
Moving Target ◽  
Target Area ◽  
Moving Targets ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

To obtain effective indoor moving target localization, a reliable and stable moving target localization method based on binocular stereo vision is proposed in this paper. A moving target recognition extraction algorithm, which integrates displacement pyramid Horn–Schunck (HS) optical flow, Delaunay triangulation and Otsu threshold segmentation, is presented to separate a moving target from a complex background, called the Otsu Delaunay HS (O-DHS) method. Additionally, a stereo matching algorithm based on deep matching and stereo vision is presented to obtain dense stereo matching points pairs, called stereo deep matching (S-DM). The stereo matching point pairs of the moving target were extracted with the moving target area and stereo deep matching point pairs, then the three dimensional coordinates of the points in the moving target area were reconstructed according to the principle of binocular vision’s parallel structure. Finally, the moving target was located by the centroid method. The experimental results showed that this method can better resist image noise and repeated texture, can effectively detect and separate moving targets, and can match stereo image points in repeated textured areas more accurately and stability. This method can effectively improve the effectiveness, accuracy and robustness of three-dimensional moving target coordinates.

Image Processing Based on Three-Dimensional Stereo Vision

2014 ◽  
Vol 556-562 ◽  
pp. 5017-5020
Author(s):  
Ting Ting Wang
Keyword(s):  
Image Processing ◽  
Binocular Vision ◽  
Stereo Vision ◽  
Error Rate ◽  
Three Dimensional ◽  
Processing Method ◽  
Stereoscopic Vision ◽  
Average Error ◽  
Traditional Model ◽  
Average Error Rate

Three-dimensional stereo vision technology has the capability of overcoming drawbacks influencing by light, posture and occluder. A novel image processing method is proposed based on three-dimensional stereoscopic vision, which optimizes model on the basis of camera binocular vision and in improvement of adding constraints to traditional model, moreover ensures accuracy of later location and recognition. To verify validity of the proposed method, firstly marking experiments are conducted to achieve fruit location, with the result of average error rate of 0.65%; and then centroid feature experiments are achieved with error from 5.77mm to 68.15mm and reference error rate from 1.44% to 5.68%, average error rate of 3.76% while the distance changes from 300mm to 1200mm. All these data of experiments demonstrate that proposed method meets the requirements of three-dimensional imageprocessing.

Symbol recognition system based on 3D stereo vision

2020 ◽  
pp. 1-10
Author(s):  
Linlin Wang
Keyword(s):  
Camera Calibration ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Recognition System ◽  
Symbol Recognition ◽  
Two Dimensional ◽  
Measuring Machine ◽  
Left And Right

With the continuous development of computer science and technology, symbol recognition systems may be converted from two-dimensional space to three-dimensional space. Therefore, this article mainly introduces the symbol recognition system based on 3D stereo vision. The three-dimensional image is taken by the visual coordinate measuring machine in two places on the left and right. Perform binocular stereo matching on the edge of the feature points of the two images. A corner detection algorithm combining SUSAN and Harris is used to detect the left and right camera calibration templates. The two-dimensional coordinate points of the object are determined by the image stereo matching module, and the three-dimensional discrete coordinate points of the object space can be obtained according to the transformation relationship between the image coordinates and the actual object coordinates. Then draw the three-dimensional model of the object through the three-dimensional drawing software. Experimental data shows that the logic resources and memory resources occupied by image preprocessing account for 30.4% and 27.4% of the entire system, respectively. The results show that the system can calibrate the internal and external parameters of the camera. In this way, the camera calibration result will be more accurate and the range will be wider. At the same time, it can effectively make up for the shortcomings of traditional modeling techniques to ensure the measurement accuracy of the detection system.

Lane-Level Three-Dimensional Semantic Mapping Based on Stereo Vision

2021 ◽  
Author(s):  
Ruirong Wang ◽  
Chunlei Song ◽  
Yuwei Zhang ◽  
Jianhua Xu
Keyword(s):  
Stereo Vision ◽  
Three Dimensional ◽  
Semantic Mapping

scholarly journals The representation of three-dimensional visual space in the cat's striate cortex

1970 ◽  
Vol 209 (1) ◽  
pp. 155-178 ◽  
Author(s):  
Colin Blakemore
Keyword(s):  
Striate Cortex ◽  
Three Dimensional ◽  
Visual Space

Infants Perceive Three-Dimensional Subjective Contours

Perception ◽  
2018 ◽  
Vol 47 (12) ◽  
pp. 1153-1165 ◽  
Author(s):  
Michael Kavšek ◽  
Stephanie Braun
Keyword(s):  
Three Dimensional ◽  
Two Dimensional ◽  
Illusory Effect ◽  
Horizontal Disparity ◽  
Illusory Contours ◽  
Kanizsa Figure

The addition of crossed horizontal disparity enhances the clarity of illusory contours compared to pictorial illusory contours and illusory contours with uncrossed horizontal disparity. Two infant-controlled habituation–dishabituation experiments explored the presence of this effect in infants 5 months of age. Experiment 1 examined whether infants are able to distinguish between a Kanizsa figure with crossed horizontal disparity and a Kanizsa figure with uncrossed horizontal disparity. Experiment 2 tested infants for their ability to differentiate between a Kanizsa figure with crossed horizontal disparity and a two-dimensional Kanizsa figure. The results provided evidence that the participants perceived the two- and the three-dimensional illusory Kanizsa contour, the illusory effect in which was strengthened by the addition of crossed horizontal disparity.

scholarly journals STEREO VISION METHOD APPLICATION TO ROAD INSPECTION

2017 ◽  
Vol 12 (1) ◽  
pp. 38-47 ◽  
Author(s):  
Marcin Staniek
Keyword(s):  
Stereo Vision ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Corner Detection ◽  
Stereo Images ◽  
Processing Technologies ◽  
The Road ◽  
Road Pavement ◽  
Road Inspection ◽  
Set Of Points

The paper presents the stereo vision method for the mapping of road pavement. The mapped road is a set of points in three-dimensional space. The proposed method of measurement and its implementation make it possible to generate a precise mapping of a road surface with a resolution of 1 mm in transverse, longitudinal and vertical dimensions. Such accurate mapping of the road is the effect of application of stereo images based on image processing technologies. The use of matching measure CoVar, at the stage of images matching, help eliminate corner detection and filter stereo images, maintaining the effectiveness of the algorithm mapping. The proper analysis of image-based data and application of mathematical transformations enable to determine many types of distresses such as potholes, patches, bleedings, cracks, ruts and roughness. The paper also aims at comparing the results of proposed solution and reference test-bench. The statistical analysis of the differences permits the judgment of error types.

A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs

Robotica ◽  
2018 ◽  
Vol 36 (8) ◽  
pp. 1225-1243 ◽  
Author(s):  
Jose-Pablo Sanchez-Rodriguez ◽  
Alejandro Aceves-Lopez
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Stereo Vision ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Field Of View ◽  
Line Of Sight ◽  
Onboard Computer ◽  
Low Weight ◽  
Vision Based Navigation ◽  
Difficult Challenge

SUMMARYThis paper presents an overview of the most recent vision-based multi-rotor micro unmanned aerial vehicles (MUAVs) intended for autonomous navigation using a stereoscopic camera. Drone operation is difficult because pilots need the expertise to fly the drones. Pilots have a limited field of view, and unfortunate situations, such as loss of line of sight or collision with objects such as wires and branches, can happen. Autonomous navigation is an even more difficult challenge than remote control navigation because the drones must make decisions on their own in real time and simultaneously build maps of their surroundings if none is available. Moreover, MUAVs are limited in terms of useful payload capability and energy consumption. Therefore, a drone must be equipped with small sensors, and it must carry low weight. In addition, a drone requires a sufficiently powerful onboard computer so that it can understand its surroundings and navigate accordingly to achieve its goal safely. A stereoscopic camera is considered a suitable sensor because of its three-dimensional (3D) capabilities. Hence, a drone can perform vision-based navigation through object recognition and self-localise inside a map if one is available; otherwise, its autonomous navigation creates a simultaneous localisation and mapping problem.

Acquisition of three-dimensional information using omnidirectional stereo vision

1997 ◽  
pp. 288-295 ◽  
Author(s):  
Atsushi Chaen ◽  
Kazumasa Yamazawa ◽  
Naokazu Yokoya ◽  
Haruo Takemura
Keyword(s):  
Stereo Vision ◽  
Three Dimensional ◽  
Omnidirectional Stereo Vision
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