Introducing Depth Information Into Generative Target Tracking

Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.

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Cuttlefish use stereopsis to strike at prey

Science Advances ◽

10.1126/sciadv.aay6036 ◽

2020 ◽

Vol 6 (2) ◽

pp. eaay6036 ◽

Cited By ~ 9

Author(s):

R. C. Feord ◽

M. E. Sumner ◽

S. Pusdekar ◽

L. Kalra ◽

P. T. Gonzalez-Bellido ◽

...

Keyword(s):

Information Processing ◽

Visual Information ◽

Convergent Evolution ◽

Visual Fields ◽

Three Dimensional ◽

Visual Information Processing ◽

Depth Information ◽

Cephalopod Species ◽

Left And Right

The camera-type eyes of vertebrates and cephalopods exhibit remarkable convergence, but it is currently unknown whether the mechanisms for visual information processing in these brains, which exhibit wildly disparate architecture, are also shared. To investigate stereopsis in a cephalopod species, we affixed “anaglyph” glasses to cuttlefish and used a three-dimensional perception paradigm. We show that (i) cuttlefish have also evolved stereopsis (i.e., the ability to extract depth information from the disparity between left and right visual fields); (ii) when stereopsis information is intact, the time and distance covered before striking at a target are shorter; (iii) stereopsis in cuttlefish works differently to vertebrates, as cuttlefish can extract stereopsis cues from anticorrelated stimuli. These findings demonstrate that although there is convergent evolution in depth computation, cuttlefish stereopsis is likely afforded by a different algorithm than in humans, and not just a different implementation.

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FORCE AND VISUAL INFORMATION ACQUISITION IN AFM BASED ROBOTIC MWCNT MANIPULATION

International Journal of Information Acquisition ◽

10.1142/s0219878907001216 ◽

2007 ◽

Vol 04 (02) ◽

pp. 107-115

Author(s):

XIAO-JUN TIAN ◽

YUE-CHAO WANG ◽

NING XI ◽

ZAI-LI DONG ◽

STEVE TUNG

Keyword(s):

Real Time ◽

Information Acquisition ◽

Visual Information ◽

Three Dimensional ◽

Force Model ◽

Motion Model ◽

Atomic Force ◽

Applied Forces ◽

Visual Interface ◽

Manipulation Process

Real-time force and visual information during MWCNT manipulation is required for online controlling MWCNT assembly based on atomic force microscope (AFM). Here real-time three-dimensional (3D) interactive forces between probe and sample are obtained according to PSD signals based on the proposed force model, and MWCNT manipulation process can be online displayed on the visual interface according to probe's position and applied force based on the proposed MWCNT motion model. With real-time force and visual information acquisition and feedback, the operator can control online MWCNT's manipulation process by adjusting the probe's 3D motion and applied forces. MWCNT push and assembly experiments verify the effectiveness of the method, which will be used in assembling MWCNT based nano device.

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A Novel Method for Estimating Monocular Depth Using Cycle GAN and Segmentation

Sensors ◽

10.3390/s20092567 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2567

Author(s):

Dong-hoon Kwak ◽

Seung-ho Lee

Keyword(s):

Visual Information ◽

Spatial Information ◽

Three Dimensional ◽

Depth Estimation ◽

Autonomous Driving ◽

Depth Information ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Novel Method ◽

Monocular Depth

Modern image processing techniques use three-dimensional (3D) images, which contain spatial information such as depth and scale, in addition to visual information. These images are indispensable in virtual reality, augmented reality (AR), and autonomous driving applications. We propose a novel method to estimate monocular depth using a cycle generative adversarial network (GAN) and segmentation. In this paper, we propose a method for estimating depth information by combining segmentation. It uses three processes: segmentation and depth estimation, adversarial loss calculations, and cycle consistency loss calculations. The cycle consistency loss calculation process evaluates the similarity of two images when they are restored to their original forms after being estimated separately from two adversarial losses. To evaluate the objective reliability of the proposed method, we compared our proposed method with other monocular depth estimation (MDE) methods using the NYU Depth Dataset V2. Our results show that the benchmark value for our proposed method is better than other methods. Therefore, we demonstrated that our proposed method is more efficient in determining depth estimation.

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Three-dimensional Shape Coding in Grasping Circuits: A Comparison between the Anterior Intraparietal Area and Ventral Premotor Area F5a

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00332 ◽

2013 ◽

Vol 25 (3) ◽

pp. 352-364 ◽

Cited By ~ 33

Author(s):

Tom Theys ◽

Pierpaolo Pani ◽

Johannes van Loon ◽

Jan Goffin ◽

Peter Janssen

Keyword(s):

Real World ◽

Visual Information ◽

Neural Coding ◽

Premotor Cortex ◽

Cell Activity ◽

Three Dimensional ◽

Depth Information ◽

Curved Surfaces ◽

Ventral Premotor Cortex ◽

Shape Selective

Depth information is necessary for adjusting the hand to the three-dimensional (3-D) shape of an object to grasp it. The transformation of visual information into appropriate distal motor commands is critically dependent on the anterior intraparietal area (AIP) and the ventral premotor cortex (area F5), particularly the F5p sector. Recent studies have demonstrated that both AIP and the F5a sector of the ventral premotor cortex contain neurons that respond selectively to disparity-defined 3-D shape. To investigate the neural coding of 3-D shape and the behavioral role of 3-D shape-selective neurons in these two areas, we recorded single-cell activity in AIP and F5a during passive fixation of curved surfaces and during grasping of real-world objects. Similar to those in AIP, F5a neurons were either first- or second-order disparity selective, frequently showed selectivity for discrete approximations of smoothly curved surfaces that contained disparity discontinuities, and exhibited mostly monotonic tuning for the degree of disparity variation. Furthermore, in both areas, 3-D shape-selective neurons were colocalized with neurons that were active during grasping of real-world objects. Thus, area AIP and F5a contain highly similar representations of 3-D shape, which is consistent with the proposed transfer of object information from AIP to the motor system through the ventral premotor cortex.

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Criteria and Methods for Reliable Three-Dimensional Image Reconstruction

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051669 ◽

1974 ◽

Vol 32 ◽

pp. 330-331

Author(s):

R. A. Crowther

Keyword(s):

Image Reconstruction ◽

Finite Number ◽

Density Distribution ◽

Electron Micrographs ◽

Mathematical Problem ◽

Three Dimensional ◽

Ideal Case ◽

Dimensional Image ◽

General Object ◽

The Ideal

The reconstruction of a three-dimensional image of a specimen from a set of electron micrographs reduces, under certain assumptions about the imaging process in the microscope, to the mathematical problem of reconstructing a density distribution from a set of its plane projections.In the absence of noise we can formulate a purely geometrical criterion, which, for a general object, fixes the resolution attainable from a given finite number of views in terms of the size of the object. For simplicity we take the ideal case of projections collected by a series of m equally spaced tilts about a single axis.

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