Optical Flow Data Research Articles

Direct Model-Based Tracking of 3D Object Deformations in Depth and Color Video

The tracking of deformable objects using video data is a demanding research topic due to the inherent ambiguity problems, which can only be solved using additional assumptions about the deformation. Image feature points, commonly used to approach the deformation problem, only provide sparse information about the scene at hand. In this paper a tracking approach for deformable objects in color and depth video is introduced that does not rely on feature points or optical flow data but employs all the input image information available to find a suitable deformation for the data at hand. A versatile NURBS based deformation space is defined for arbitrary complex triangle meshes, decoupling the object surface complexity from the complexity of the deformation. An efficient optimization scheme is introduced that is able to calculate results in real-time (25 Hz). Extensive synthetic and real data tests of the algorithm and its features show the reliability of this approach.

Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data

Currently, assessment of broiler (meat) chicken welfare relies largely on labour-intensive or post-mortem measures of welfare. We here describe a method for continuously and robustly monitoring the welfare of living birds while husbandry changes are still possible. We detail the application of Bayesian modelling to motion data derived from the output of cameras placed in commercial broiler houses. We show that the forecasts produced by the model can be used to accurately assess certain key aspects of the future health and welfare of a flock. The difference between healthy flocks and less-healthy ones becomes predictable days or even weeks before clinical symptoms become apparent. Hockburn (damaged leg skin, usually only seen in birds of two weeks or older) can be well predicted in flocks of only 1-2 days of age, using this approach. Our model combines optical flow descriptors of bird motion with robust multivariate forecasting and provides a sparse, efficient model with sparsity-inducing priors to achieve maximum predictive power with the minimum number of key variables.

Sonification of Face

In this paper, the integration of face segments detection and extraction, and facial expression recognition and Sonification are discussed. In this paper, we propose an algorithm that utilizes haar classifiers detecting face followed by dividing face into Regions of interest (ROI) to extract facial segments. Furthermore, in this project, we propose a statistical approach to process the optical flow data to obtain the overall value for the respective feature region in the face. This approach has eliminated the requirement of accurate identification of the feature boundary. Optical flow computations are utilized to identify the directions and the amount of motions in image sequences that are caused by human facial expressions. These calculated values are then compared with the values from table to estimate the expression .Furthermore after getting the expression; these are then converted to sound by mapping functions. Sounds are generated by simple MIDI tunes based on instruments assigned to each region.

Multi-frame structure from motion using optical flow probability distributions

We present a novel multi-frame structure from motion algorithm in which camera motion and object structure are calculated from optical flow probability distributions instead of a single optical flow estimate at each feature point. Optical flow distributions of the selected feature points allow us to quantify the accuracy of the optical flow estimate in any direction. With this additional knowledge, a more accurate structure from motion algorithm is created which relies on this more accurate optical flow data. This novel method is designed to use the optical flow values taken from multiple frames of video or an image sequence. It is an optimal solution to the structure from motion problem with respect to a chosen norm. We will demonstrate that this new method performs significantly better than similar methods which do not use optical flow distributions or which do not use multiple frames.

Biomedical Image Sequence Analysis with Application to Automatic Quantitative Assessment of Facial Paralysis

Facial paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann scale. Experiments show the radial basis function (RBF) neural network to have superior performance.

An embedded vision system based on an analog VLSI optical flow vision sensor

We propose a novel programmable miniature vision module based on a custom designed analog very-large-scale-integrated (aVLSI) vision sensor. The sensor provides the logarithmically compressed and temporally adapted intensity image as well as a dense smooth optical flow estimate. The fully parallel and analog network architecture of the sensor guarantees efficient real-time visual processing. The module embeds the aVLSI sensor within commercial off-the-shelves digital hardware that controls the sensor read-out and permits additional higher-level post-processing of the intensity image and optical flow data. The combination of analog and digital processing leads to a computationally very powerful, yet small and potentially cheap vision module. It can be used stand-alone or integrated in larger vision or robotic systems. Standard interfaces guarantee easy control and programming, and make the vision module especially well suited for education as well as applied research in the robotic sciences.

Optical Flow Based Navigation for Mobile Robots Using an Embedded Optical Correlator

The proposed navigation concept solves the problem of autonomous real time navigation of mobile robots with a single wide angle camera as primary perception sensor and a standard odometry as auxiliary navigation sensor. The key feature of this navigation concept is the synergetic combination of the optical flow concept and a special computer hardware technology on the base of photonic computing using an advanced embedded optical correlator. The navigation information derived from the optical flow measurements is used for a high rate continuous trajectory estimation using auxiliary odometry data as well as for a periodic absolute navigation with sole optical flow data. The paper presents the general system layout, describes the key components and discusses performance and simulation results.

Low-Technology Demonstrations of the Role of Peripheral Vision in Human Factors and Ergonomics

This paper provides two demonstrations that highlight the importance of peripheral vision issues in system design. The first demonstration concerns the ability of the visual system to detect motion, while the second addresses the impact of reduction of visual field on the perception of optical flow data. Educators can use these demonstrations as the basis for discussions about the design implications for warning displays, attention gathering devices, optical flow, off-axis accidents, aging, attention, and night vision goggles. These demonstrations can be used at both the undergraduate and graduate levels. Relevant research findings are also discussed, and references for further exploration of the topics are provided.

Estimation of moving information for tracking of moving objects

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.

Determining the egomotion of an uncalibrated camera from instantaneous optical flow

A procedure is described for self-calibration of a moving camera from instantaneous optical flow. Under certain assumptions this procedure allows the egomotion and some intrinsic parameters of the camera to be determined solely from the instantaneous positions and velocities of a set of image features. The proposed method relies on the use of a differential epipolar equation that relates optical flow to the egomotion and the internal geometry of the camera. A detailed derivation of this equation is presented. This aspect of the work may be seen as a recasting into an analytical framework of the pivotal research of Vieville Faugeras [Proceedings of the Fifth International Conference on Computer Vision (IEEE Computer Society Press, Los Alamitos, Calif., 1995), pp. 750–756]. The information about the camera's egomotion and internal geometry enters the differential epipolar equation via two matrices. It emerges that the optical flow determines the composite ratio of some of the entries of the two matrices. It is shown that a camera with unknown focal length undergoing arbitrary motion can be self-calibrated by means of closed-form expressions in the composite ratio. The corresponding formulas specify five egomotion parameters as well as the focal length and its derivative. A procedure is presented for reconstructing the viewed scene, up to a scale factor, from the derived self-calibration data and the optical flow data. Experimental results are given to demonstrate the correctness of the approach.

Visual servoing in the task-function framework: A contour following task

We describe an approach to contour following unknown objects using a handeye robotic system. Relevant and sufficient feature points providing optical flow data are extracted from the edges of the target object. The desired motion of the end-effector is computed with the objective of keeping the visual features always at the same target location in the image plane. A cartesian PD controller is used to perform the desired motion by the robot's end-effector. To address thecontrol issues, we take advantage of the unifying robot control theory stated in the literature as thetask-function approach [21]. To validate our approach, we restricted our experiments to motionless objects positioned in a plane parallel to the image plane: three degrees of freedom (two of translation, one of rotation) are thus controlled.

Gabor filter-based edge detection

It is common practice to utilize evidence from biological and psychological vision experiments to develop computational models for low-level feature extraction. The receptive profiles of simple cells in mammalian visual systems have been found to closely resemble Gabor filters. Daugman proved that Gabor filters achieve joint minimal joint uncertainty. These results led researchers to develop computational models based on Gabor filters for several low-level vision applications such as edge detection, texture classification, optical flow estimation and data compression. In this paper, the performance of a Gabor odd filter-based edge detector is investigated using the measures proposed by Canny. Based on this performance analysis a design criterion for one-dimensional (1D) Gabor filter-based edge detector is derived. It is shown that this design criterion also holds good for a two-dimensional (2D) Gabor filter-based edge detector. Experimental results are presented to demonstrate the performance of the Gabor filter-based edge detector.

Automatic lipreading by optical‐flow analysis

AbstractWhile the acoustic signal is the primary cue in human speech recognition, the visual cue is also very useful, especially when the acoustic signal is distorted. A computer system is developed for automatic recognition of continuously spoken words by using only visual data. The velocity of lip motions may be measured from optical flow data which allows muscle action to be estimated. Pauses in muscle action result in zero velocity of the flow and are used to locate word boundaries. The pattern of muscle action is used to recognize the spoken words.In limited experiments involving the recognition of digits, it appears that the visually derived patterns of muscle action are stable for multiple utterances of the same word. Even across speakers the patterns are so similar that speaker‐independent recognition is possible. An overall accuracy including word spotting and recognition of approximately 70 percent is obtained across for continuously spoken test samples from three speakers.

Neural mapping and parallel optical flow computation for autonomous navigation

In this paper, the authors present information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. In most previous approaches, the extraction of motion data from varying image intensities is complicated by the so-called aperture and correspondence problems. The correspondence problem arises if motion detection is based on image features that have to be identified in subsequent frames. If this problem is avoided by continuously registering image intensity changes not necessarily corresponding to features, the motion signal obtained becomes ambiguous due to the aperture problem. Recently a new algorithm for the computation of optical flow has been developed that produces dense motion data which are not subject to the aperture problem. Once the velocity vector field is established, optical flow analysis has to deal with the global space-variance of this field which carries much of the information. Local detectors for divergence (looming) and curl, that can be used in tasks such as obstacle avoidance, produce space-variant results even in the absence of obstacles. Also, motion detection itself could be restricted to just one direction per site for certain information processing tasks, were it not for the space-variance of that direction. For observer motion on amore » planar surface, these problems can be overcome by a retinotopic mapping, or transform, applied to image coordinates which inverts the perspective for points on this surface.« less

Asynchronous maturation of visual system structures: Lessons from color blindness

It is common practice to utilize evidence from biological and psychological vision experiments to develop computational models for low-level feature extraction. The receptive profiles of simple cells in mammalian visual systems have been found to closely resemble Gabor filters. Daugman proved that Gabor filters achieve joint minimal joint uncertainty. These results led researchers to develop computational models based on Gabor filters for several low-level vision applications such as edge detection, texture classification, optical flow estimation and data compression. In this paper, the performance of a Gabor odd filter-based edge detector is investigated using the measures proposed by Canny. Based on this performance analysis a design criterion for one-dimensional (1D) Gabor filter-based edge detector is derived. It is shown that this design criterion also holds good for a two-dimensional (2D) Gabor filter-based edge detector. Experimental results are presented to demonstrate the performance of the Gabor filter-based edge detector.