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Wyner-Ziv side information generation using a higher order piecewise trajectory temporal interpolation algorithm
Distributed video coding (DVC) reverses the traditional coding paradigm of complex encoders allied with basic decoding, to one where the computational cost is largely incurred by the decoder. This enables low-cost, resource-poor sensors to be used at the transmitter in various applications including multi-sensor surveillance. A key constraint governing DVC performance is the quality of side information (SI), a coarse representation of original video frames which are not available at the decoder. Techniques to generate SI have generally been based on linear temporal interpolation, though these do not always produce satisfactory SI quality especially in sequences exhibiting asymmetric (non-linear) motion. This paper presents a higher-order piecewise trajectory temporal interpolation (HOPTTI) algorithm for SI generation that quantitatively and perceptually affords better SI quality in comparison to existing temporal interpolation-based approaches
Motion-Compensated Coding and Frame-Rate Up-Conversion: Models and Analysis
Block-based motion estimation (ME) and compensation (MC) techniques are
widely used in modern video processing algorithms and compression systems. The
great variety of video applications and devices results in numerous compression
specifications. Specifically, there is a diversity of frame-rates and
bit-rates. In this paper, we study the effect of frame-rate and compression
bit-rate on block-based ME and MC as commonly utilized in inter-frame coding
and frame-rate up conversion (FRUC). This joint examination yields a
comprehensive foundation for comparing MC procedures in coding and FRUC. First,
the video signal is modeled as a noisy translational motion of an image. Then,
we theoretically model the motion-compensated prediction of an available and
absent frames as in coding and FRUC applications, respectively. The theoretic
MC-prediction error is further analyzed and its autocorrelation function is
calculated for coding and FRUC applications. We show a linear relation between
the variance of the MC-prediction error and temporal-distance. While the
affecting distance in MC-coding is between the predicted and reference frames,
MC-FRUC is affected by the distance between the available frames used for the
interpolation. Moreover, the dependency in temporal-distance implies an inverse
effect of the frame-rate. FRUC performance analysis considers the prediction
error variance, since it equals to the mean-squared-error of the interpolation.
However, MC-coding analysis requires the entire autocorrelation function of the
error; hence, analytic simplicity is beneficial. Therefore, we propose two
constructions of a separable autocorrelation function for prediction error in
MC-coding. We conclude by comparing our estimations with experimental results
Scene-based nonuniformity correction with video sequences and registration
We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity
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Intelligent Side Information Generation in Distributed Video Coding
Distributed video coding (DVC) reverses the traditional coding paradigm of complex encoders allied with basic decoding to one where the computational cost is largely incurred by the decoder. This is attractive as the proven theoretical work of Wyner-Ziv (WZ) and Slepian-Wolf (SW) shows that the performance by such a system should be exactly the same as a conventional coder. Despite the solid theoretical foundations, current DVC qualitative and quantitative performance falls short of existing conventional coders and there remain crucial limitations. A key constraint governing DVC performance is the quality of side information (SI), a coarse representation of original video frames which are not available at the decoder. Techniques to generate SI have usually been based on linear motion compensated temporal interpolation (LMCTI), though these do not always produce satisfactory SI quality, especially in sequences exhibiting non-linear motion.
This thesis presents an intelligent higher order piecewise trajectory temporal interpolation (HOPTTI) framework for SI generation with original contributions that afford better SI quality in comparison to existing LMCTI-based approaches. The major elements in this framework are: (i) a cubic trajectory interpolation algorithm model that significantly improves the accuracy of motion vector estimations; (ii) an adaptive overlapped block motion compensation (AOBMC) model which reduces both blocking and overlapping artefacts in the SI emanating from the block matching algorithm; (iii) the development of an empirical mode switching algorithm; and (iv) an intelligent switching mechanism to construct SI by automatically selecting the best macroblock from the intermediate SI generated by HOPTTI and AOBMC algorithms. Rigorous analysis and evaluation confirms that significant quantitative and perceptual improvements in SI quality are achieved with the new framework
Evaluation of servo, geometric and dynamic error sources on five axis high-speed machine tool
Many sources of errors exist in the manufacturing process of complex shapes.
Some approximations occur at each step from the design geometry to the machined
part. The aim of the paper is to present a method to evaluate the effect of
high speed and high dynamic load on volumetric errors at the tool center point.
The interpolator output signals and the machine encoder signals are recorded
and compared to evaluate the contouring errors resulting from each axis
follow-up error. The machine encoder signals are also compared to the actual
tool center point position as recorded with a non-contact measuring instrument
called CapBall to evaluate the total geometric errors. The novelty of the work
lies in the method that is proposed to decompose the geometric errors in two
categories: the quasi-static geometric errors independent from the speed of the
trajectory and the dynamic geometric errors, dependent on the programmed feed
rate and resulting from the machine structure deflection during the
acceleration of its axes. The evolution of the respective contributions for
contouring errors, quasi-static geometric errors and dynamic geomet- ric errors
is experimentally evaluated and a relation between programmed feed rate and
dynamic errors is highlighted.Comment: 13 pages; International Journal of Machine Tools and Manufacture
(2011) pp XX-X
๋น๋์ค ํ๋ ์ ๋ณด๊ฐ์ ์ํ ๋ค์ค ๋ฒกํฐ ๊ธฐ๋ฐ์ MEMC ๋ฐ ์ฌ์ธต CNN
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2019. 2. ์ดํ์ฌ.Block-based hierarchical motion estimations are widely used and are successful in generating high-quality interpolation. However, it still fails in the motion estimation of small objects when a background region moves in a different direction. This is because the motion of small objects is neglected by the down-sampling and over-smoothing operations at the top level of image pyramids in the maximum a posterior (MAP) method. Consequently, the motion vector of small objects cannot be detected at the bottom level, and therefore, the small objects often appear deformed in an interpolated frame. This thesis proposes a novel algorithm that preserves the motion vector of the small objects by adding a secondary motion vector candidate that represents the movement of the small objects. This additional candidate is always propagated from the top to the bottom layers of the image pyramid. Experimental results demonstrate that the intermediate frame interpolated by the proposed algorithm significantly improves the visual quality when compared with conventional MAP-based frame interpolation.
In motion compensated frame interpolation, a repetition pattern in an image makes it difficult to derive an accurate motion vector because multiple similar local minima exist in the search space of the matching cost for motion estimation. In order to improve the accuracy of motion estimation in a repetition region, this thesis attempts a semi-global approach that exploits both local and global characteristics of a repetition region. A histogram of the motion vector candidates is built by using a voter based voting system that is more reliable than an elector based voting system. Experimental results demonstrate that the proposed method significantly outperforms the previous local approach in term of both objective peak signal-to-noise ratio (PSNR) and subjective visual quality.
In video frame interpolation or motion-compensated frame rate up-conversion (MC-FRUC), motion compensation along unidirectional motion trajectories directly causes overlaps and holes issues. To solve these issues, this research presents a new algorithm for bidirectional motion compensated frame interpolation. Firstly, the proposed method generates bidirectional motion vectors from two unidirectional motion vector fields (forward and backward) obtained from the unidirectional motion estimations. It is done by projecting the forward and backward motion vectors into the interpolated frame. A comprehensive metric as an extension of the distance between a projected block and an interpolated block is proposed to compute weighted coefficients in the case when the interpolated block has multiple projected ones. Holes are filled based on vector median filter of non-hole available neighbor blocks. The proposed method outperforms existing MC-FRUC methods and removes block artifacts significantly.
Video frame interpolation with a deep convolutional neural network (CNN) is also investigated in this thesis. Optical flow and video frame interpolation are considered as a chicken-egg problem such that one problem affects the other and vice versa. This thesis presents a stack of networks that are trained to estimate intermediate optical flows from the very first intermediate synthesized frame and later the very end interpolated frame is generated by the second synthesis network that is fed by stacking the very first one and two learned intermediate optical flows based warped frames. The primary benefit is that it glues two problems into one comprehensive framework that learns altogether by using both an analysis-by-synthesis technique for optical flow estimation and vice versa, CNN kernels based synthesis-by-analysis. The proposed network is the first attempt to bridge two branches of previous approaches, optical flow based synthesis and CNN kernels based synthesis into a comprehensive network. Experiments are carried out with various challenging datasets, all showing that the proposed network outperforms the state-of-the-art methods with significant margins for video frame interpolation and the estimated optical flows are accurate for challenging movements. The proposed deep video frame interpolation network to post-processing is applied to the improvement of the coding efficiency of the state-of-art video compress standard, HEVC/H.265 and experimental results prove the efficiency of the proposed network.๋ธ๋ก ๊ธฐ๋ฐ ๊ณ์ธต์ ์์ง์ ์ถ์ ์ ๊ณ ํ์ง์ ๋ณด๊ฐ ์ด๋ฏธ์ง๋ฅผ ์์ฑํ ์ ์์ด ํญ๋๊ฒ ์ฌ์ฉ๋๊ณ ์๋ค. ํ์ง๋ง, ๋ฐฐ๊ฒฝ ์์ญ์ด ์์ง์ผ ๋, ์์ ๋ฌผ์ฒด์ ๋ํ ์์ง์ ์ถ์ ์ฑ๋ฅ์ ์ฌ์ ํ ์ข์ง ์๋ค. ์ด๋ maximum a posterior (MAP) ๋ฐฉ์์ผ๋ก ์ด๋ฏธ์ง ํผ๋ผ๋ฏธ๋์ ์ต์์ ๋ ๋ฒจ์์ down-sampling๊ณผ over-smoothing์ผ๋ก ์ธํด ์์ ๋ฌผ์ฒด์ ์์ง์์ด ๋ฌด์๋๊ธฐ ๋๋ฌธ์ด๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก ์ด๋ฏธ์ง ํผ๋ผ๋ฏธ๋์ ์ตํ์ ๋ ๋ฒจ์์ ์์ ๋ฌผ์ฒด์ ์์ง์ ๋ฒกํฐ๋ ๊ฒ์ถ๋ ์ ์์ด ๋ณด๊ฐ ์ด๋ฏธ์ง์์ ์์ ๋ฌผ์ฒด๋ ์ข
์ข
๋ณํ๋ ๊ฒ์ฒ๋ผ ๋ณด์ธ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์์ ๋ฌผ์ฒด์ ์์ง์์ ๋ํ๋ด๋ 2์ฐจ ์์ง์ ๋ฒกํฐ ํ๋ณด๋ฅผ ์ถ๊ฐํ์ฌ ์์ ๋ฌผ์ฒด์ ์์ง์ ๋ฒกํฐ๋ฅผ ๋ณด์กดํ๋ ์๋ก์ด ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ถ๊ฐ๋ ์์ง์ ๋ฒกํฐ ํ๋ณด๋ ํญ์ ์ด๋ฏธ์ง ํผ๋ผ๋ฏธ๋์ ์ต์์์์ ์ตํ์ ๋ ๋ฒจ๋ก ์ ํ๋๋ค. ์คํ ๊ฒฐ๊ณผ๋ ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ๋ณด๊ฐ ์์ฑ ํ๋ ์์ด ๊ธฐ์กด MAP ๊ธฐ๋ฐ ๋ณด๊ฐ ๋ฐฉ์์ผ๋ก ์์ฑ๋ ํ๋ ์๋ณด๋ค ์ด๋ฏธ์ง ํ์ง์ด ์๋นํ ํฅ์๋จ์ ๋ณด์ฌ์ค๋ค.
์์ง์ ๋ณด์ ํ๋ ์ ๋ณด๊ฐ์์, ์ด๋ฏธ์ง ๋ด์ ๋ฐ๋ณต ํจํด์ ์์ง์ ์ถ์ ์ ์ํ ์ ํฉ ์ค์ฐจ ํ์ ์ ๋ค์์ ์ ์ฌ local minima๊ฐ ์กด์ฌํ๊ธฐ ๋๋ฌธ์ ์ ํํ ์์ง์ ๋ฒกํฐ ์ ๋๋ฅผ ์ด๋ ต๊ฒ ํ๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๋ฐ๋ณต ํจํด์์์ ์์ง์ ์ถ์ ์ ์ ํ๋๋ฅผ ํฅ์์ํค๊ธฐ ์ํด ๋ฐ๋ณต ์์ญ์ localํ ํน์ฑ๊ณผ globalํ ํน์ฑ์ ๋์์ ํ์ฉํ๋ semi-globalํ ์ ๊ทผ์ ์๋ํ๋ค. ์์ง์ ๋ฒกํฐ ํ๋ณด์ ํ์คํ ๊ทธ๋จ์ ์ ๊ฑฐ ๊ธฐ๋ฐ ํฌํ ์์คํ
๋ณด๋ค ์ ๋ขฐํ ์ ์๋ ์ ๊ถ์ ๊ธฐ๋ฐ ํฌํ ์์คํ
๊ธฐ๋ฐ์ผ๋ก ํ์ฑ๋๋ค. ์คํ ๊ฒฐ๊ณผ๋ ์ ์๋ ๋ฐฉ๋ฒ์ด ์ด์ ์ localํ ์ ๊ทผ๋ฒ๋ณด๋ค peak signal-to-noise ratio (PSNR)์ ์ฃผ๊ด์ ํ์ง ํ๋จ ๊ด์ ์์ ์๋นํ ์ฐ์ํจ์ ๋ณด์ฌ์ค๋ค.
๋น๋์ค ํ๋ ์ ๋ณด๊ฐ ๋๋ ์์ง์ ๋ณด์ ํ๋ ์์จ ์ํฅ ๋ณํ (MC-FRUC)์์, ๋จ๋ฐฉํฅ ์์ง์ ๊ถค์ ์ ๋ฐ๋ฅธ ์์ง์ ๋ณด์์ overlap๊ณผ hole ๋ฌธ์ ๋ฅผ ์ผ์ผํจ๋ค. ๋ณธ ์ฐ๊ตฌ์์ ์ด๋ฌํ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ์๋ฐฉํฅ ์์ง์ ๋ณด์ ํ๋ ์ ๋ณด๊ฐ์ ์ํ ์๋ก์ด ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๋จผ์ , ์ ์๋ ๋ฐฉ๋ฒ์ ๋จ๋ฐฉํฅ ์์ง์ ์ถ์ ์ผ๋ก๋ถํฐ ์ป์ด์ง ๋ ๊ฐ์ ๋จ๋ฐฉํฅ ์์ง์ ์์ญ(์ ๋ฐฉ ๋ฐ ํ๋ฐฉ)์ผ๋ก๋ถํฐ ์๋ฐฉํฅ ์์ง์ ๋ฒกํฐ๋ฅผ ์์ฑํ๋ค. ์ด๋ ์ ๋ฐฉ ๋ฐ ํ๋ฐฉ ์์ง์ ๋ฒกํฐ๋ฅผ ๋ณด๊ฐ ํ๋ ์์ ํฌ์ํจ์ผ๋ก์จ ์ํ๋๋ค. ๋ณด๊ฐ๋ ๋ธ๋ก์ ์ฌ๋ฌ ๊ฐ์ ํฌ์๋ ๋ธ๋ก์ด ์๋ ๊ฒฝ์ฐ, ํฌ์๋ ๋ธ๋ก๊ณผ ๋ณด๊ฐ๋ ๋ธ๋ก ์ฌ์ด์ ๊ฑฐ๋ฆฌ๋ฅผ ํ์ฅํ๋ ๊ธฐ์ค์ด ๊ฐ์ค ๊ณ์๋ฅผ ๊ณ์ฐํ๊ธฐ ์ํด ์ ์๋๋ค. Hole์ hole์ด ์๋ ์ด์ ๋ธ๋ก์ vector median filter๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ฒ๋ฆฌ๋๋ค. ์ ์ ๋ฐฉ๋ฒ์ ๊ธฐ์กด์ MC-FRUC๋ณด๋ค ์ฑ๋ฅ์ด ์ฐ์ํ๋ฉฐ, ๋ธ๋ก ์ดํ๋ฅผ ์๋นํ ์ ๊ฑฐํ๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ CNN์ ์ด์ฉํ ๋น๋์ค ํ๋ ์ ๋ณด๊ฐ์ ๋ํด์๋ ๋ค๋ฃฌ๋ค. Optical flow ๋ฐ ๋น๋์ค ํ๋ ์ ๋ณด๊ฐ์ ํ ๊ฐ์ง ๋ฌธ์ ๊ฐ ๋ค๋ฅธ ๋ฌธ์ ์ ์ํฅ์ ๋ฏธ์น๋ chicken-egg ๋ฌธ์ ๋ก ๊ฐ์ฃผ๋๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ค๊ฐ optical flow ๋ฅผ ๊ณ์ฐํ๋ ๋คํธ์ํฌ์ ๋ณด๊ฐ ํ๋ ์์ ํฉ์ฑ ํ๋ ๋ ๊ฐ์ง ๋คํธ์ํฌ๋ก ์ด๋ฃจ์ด์ง ํ๋์ ๋คํธ์ํฌ ์คํ์ ๊ตฌ์กฐ๋ฅผ ์ ์ํ๋ค. The final ๋ณด๊ฐ ํ๋ ์์ ์์ฑํ๋ ๋คํธ์ํฌ์ ๊ฒฝ์ฐ ์ฒซ ๋ฒ์งธ ๋คํธ์ํฌ์ ์ถ๋ ฅ์ธ ๋ณด๊ฐ ํ๋ ์ ์ ์ค๊ฐ optical flow based warped frames์ ์
๋ ฅ์ผ๋ก ๋ฐ์์ ํ๋ ์์ ์์ฑํ๋ค. ์ ์๋ ๊ตฌ์กฐ์ ๊ฐ์ฅ ํฐ ํน์ง์ optical flow ๊ณ์ฐ์ ์ํ ํฉ์ฑ์ ์ํ ๋ถ์๋ฒ๊ณผ CNN ๊ธฐ๋ฐ์ ๋ถ์์ ์ํ ํฉ์ฑ๋ฒ์ ๋ชจ๋ ์ด์ฉํ์ฌ ํ๋์ ์ข
ํฉ์ ์ธ framework๋ก ๊ฒฐํฉํ์๋ค๋ ๊ฒ์ด๋ค. ์ ์๋ ๋คํธ์ํฌ๋ ๊ธฐ์กด์ ๋ ๊ฐ์ง ์ฐ๊ตฌ์ธ optical flow ๊ธฐ๋ฐ ํ๋ ์ ํฉ์ฑ๊ณผ CNN ๊ธฐ๋ฐ ํฉ์ฑ ํ๋ ์ ํฉ์ฑ๋ฒ์ ์ฒ์ ๊ฒฐํฉ์ํจ ๋ฐฉ์์ด๋ค. ์คํ์ ๋ค์ํ๊ณ ๋ณต์กํ ๋ฐ์ดํฐ ์
์ผ๋ก ์ด๋ฃจ์ด์ก์ผ๋ฉฐ, ๋ณด๊ฐ ํ๋ ์ quality ์ optical flow ๊ณ์ฐ ์ ํ๋ ์ธก๋ฉด์์ ๊ธฐ์กด์ state-of-art ๋ฐฉ์์ ๋นํด ์๋ฑํ ๋์ ์ฑ๋ฅ์ ๋ณด์๋ค. ๋ณธ ๋
ผ๋ฌธ์ ํ ์ฒ๋ฆฌ๋ฅผ ์ํ ์ฌ์ธต ๋น๋์ค ํ๋ ์ ๋ณด๊ฐ ๋คํธ์ํฌ๋ ์ฝ๋ฉ ํจ์จ ํฅ์์ ์ํด ์ต์ ๋น๋์ค ์์ถ ํ์ค์ธ HEVC/H.265์ ์ ์ฉํ ์ ์์ผ๋ฉฐ, ์คํ ๊ฒฐ๊ณผ๋ ์ ์ ๋คํธ์ํฌ์ ํจ์จ์ฑ์ ์
์ฆํ๋ค.Abstract i
Table of Contents iv
List of Tables vii
List of Figures viii
Chapter 1. Introduction 1
1.1. Hierarchical Motion Estimation of Small Objects 2
1.2. Motion Estimation of a Repetition Pattern Region 4
1.3. Motion-Compensated Frame Interpolation 5
1.4. Video Frame Interpolation with Deep CNN 6
1.5. Outline of the Thesis 7
Chapter 2. Previous Works 9
2.1. Previous Works on Hierarchical Block-Based Motion Estimation 9
2.1.1.โMaximum a Posterior (MAP) Framework 10
2.1.2.Hierarchical Motion Estimation 12
2.2. Previous Works on Motion Estimation for a Repetition Pattern Region 13
2.3. Previous Works on Motion Compensation 14
2.4. Previous Works on Video Frame Interpolation with Deep CNN 16
Chapter 3. Hierarchical Motion Estimation for Small Objects 19
3.1. Problem Statement 19
3.2. The Alternative Motion Vector of High Cost Pixels 20
3.3. Modified Hierarchical Motion Estimation 23
3.4. Framework of the Proposed Algorithm 24
3.5. Experimental Results 25
3.5.1. Performance Analysis 26
3.5.2. Performance Evaluation 29
Chapter 4. Semi-Global Accurate Motion Estimation for a Repetition Pattern Region 32
4.1. Problem Statement 32
4.2. Objective Function and Constrains 33
4.3. Elector based Voting System 34
4.4. Voter based Voting System 36
4.5. Experimental Results 40
Chapter 5. Multiple Motion Vectors based Motion Compensation 44
5.1. Problem Statement 44
5.2. Adaptive Weighted Multiple Motion Vectors based Motion Compensation 45
5.2.1. One-to-Multiple Motion Vector Projection 45
5.2.2. A Comprehensive Metric as the Extension of Distance 48
5.3. Handling Hole Blocks 49
5.4. Framework of the Proposed Motion Compensated Frame Interpolation 50
5.5. Experimental Results 51
Chapter 6. Video Frame Interpolation with a Stack of Deep CNN 56
6.1. Problem Statement 56
6.2. The Proposed Network for Video Frame Interpolation 57
6.2.1. A Stack of Synthesis Networks 57
6.2.2. Intermediate Optical Flow Derivation Module 60
6.2.3. Warping Operations 62
6.2.4. Training and Loss Function 63
6.2.5. Network Architecture 64
6.2.6. Experimental Results 64
6.2.6.1. Frame Interpolation Evaluation 64
6.2.6.2. Ablation Experiments 77
6.3. Extension for Quality Enhancement for Compressed Videos Task 83
6.4. Extension for Improving the Coding Efficiency of HEVC based Low Bitrate Encoder 88
Chapter 7. Conclusion 94
References 97Docto
A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm
This paper describes a self-organizing neural model for eye-hand coordination. Called the DIRECT model, it embodies a solution of the classical motor equivalence problem. Motor equivalence computations allow humans and other animals to flexibly employ an arm with more degrees of freedom than the space in which it moves to carry out spatially defined tasks under conditions that may require novel joint configurations. During a motor babbling phase, the model endogenously generates movement commands that activate the correlated visual, spatial, and motor information that are used to learn its internal coordinate transformations. After learning occurs, the model is capable of controlling reaching movements of the arm to prescribed spatial targets using many different combinations of joints. When allowed visual feedback, the model can automatically perform, without additional learning, reaches with tools of variable lengths, with clamped joints, with distortions of visual input by a prism, and with unexpected perturbations. These compensatory computations occur within a single accurate reaching movement. No corrective movements are needed. Blind reaches using internal feedback have also been simulated. The model achieves its competence by transforming visual information about target position and end effector position in 3-D space into a body-centered spatial representation of the direction in 3-D space that the end effector must move to contact the target. The spatial direction vector is adaptively transformed into a motor direction vector, which represents the joint rotations that move the end effector in the desired spatial direction from the present arm configuration. Properties of the model are compared with psychophysical data on human reaching movements, neurophysiological data on the tuning curves of neurons in the monkey motor cortex, and alternative models of movement control.National Science Foundation (IRI 90-24877); Office of Naval Research (N00014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI 90-24877
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