Measurement of target motion in image sequence

Cue-based camera calibration and its application to digital moving image production

This paper presents a new efficient enhanced automatic target recognition approach based on hybrid feature and intensity based methods. The proposed approach is based on comparison and matching between the acquired target image and the stored database images through edge detection, morphological operation, image transformation, image histogram matching and mutual information for both target and stored database images. Sobel operator is used to detect edges in both acquired target image and the stored database images. Hough transform is used as an image transformation which groups the extracted edge points in edged images to an appropriate set of lines which helps in features extraction and matching processes in both of target and stored database images. This gives an initial indication about realization and recognition between target image and its corresponding database image. Comparisons of image histogram and mutual information for both target and database images are finally used to emphasize the recognition of the target image and its verification with its corresponding database image. The proposed recognition approach passed through four stages which are: edge detection by Sobel edge detector, thinning as a morphological operation, Hough transformation, matching process using median deviation and finally histogram matching and measuring the mutual information between target and the available database images. The experimental results proved that the proposed hybrid target recognition approach gives a more accurate and successful recognition rate than other techniques which depend on using feature based methods or intensity based methods.

In this paper, we present a method based on tangent distance to estimate motion in image sequences. Tangent distance combines an intuitive understanding and effective modeling of differences between patterns. This tool was first introduced and successfully applied in character recognition. It allows to compare patterns according to small transformations (translations, rotations, etc.). We show, how to take advantages of some properties of tangent distances to perform a robust motion estimation algorithm. Particularly, the presented algorithm can easily be adapted and optimized to various types of movements and can also be used to estimate optical flow in image sequences. Moreover, and despite a time of computation a bit long, this algorithm can be massively paralleled.

We present a binary hypothesis test to detect moving pixels in an image sequence that requires less computation than previous methods while maintaining a comparable level of performance. We provide a detailed description of the assumptions required to simplify the test and an example that demonstrates its effectiveness.

The focus of this work is the development of an efficient adaptive algorithm for source separation from a noisy image sequence with interferences when the underlying source signals are unknown. The sources to be extracted are the underlying activation signals who are collectively responsible for the intensity fluctuations of the pixels. This technique uses the concept of temporal Independent Component Analysis (tICA) which does not rely on source signal information for its unmixing process. Each pixel of an image is taken as a sensor which has its own intensity fluctuation pattern. Thus the whole image is a collection of two dimensional signal mixtures which can be used to separate both super and sub Gaussian sources through an adaption process. This work demonstrates the viability of utilizing the concept of subspace separation and tICA for removal of visual interferences from dynamic image sequences when extracting underlying source signals. Two methods are proposed: The first of which identifies the interference at the ICA output level while the second method removes the interference at the subspace separation stage. The proposed technique has a potential use in a wide array of applications such as computer vision, surveillance bio-medicine, etc.

ICA (Independent Component Analysis) technique can be used to detect difference in sequence of images. However, the algorithm is usually highly computation intensive. In this paper, FPGA (Field Programmable Gate Array) implementation to speed up the computation of a non-iterative algebraic ICA is described. System level approach to the design of FPGA prototype is explained. Although FPGA does not offer an optimized hardware implementation when compared to ASIC (Application Specific Integrated Circuit), it allows short development time and enables verification of algorithms in hardware at a low cost.

This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyper-spectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyper-spectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyper-spectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyper-spectral images are consistent with the theoretical analysis results.

We have reviewed the estimation of 2D motion from time-varying images, paying particular attention to the underlying models, estimation criteria, and optimization strategies. Several parametric and nonparametric models for the representation of motion vector fields and motion trajectory fields have been discussed. For a given region of support, these models determine the dimensionality of the estimation problem as well as the amount of data that has to be interpreted or transmitted thereafter. Also, the interdependence of motion and image data has been addressed. We have shown that even ideal constraints may not provide a well-defined estimation criterion. Therefore, the data term of an estimation criterion is usually supplemented with a smoothness term that can be expressed explicitly or implicitly via a constraining motion model. We have paid particular attention to the statistical criteria based on Markov random fields. Because the optimization of an estimation criterion typically involves a large number of unknowns, we have presented several fast search strategies.

Capturing the human hand motion from video involves the estimation of the rigid global hand pose as well as the nonrigid finger articulation. The complexity induced by the high degrees of freedom of the articulated hand challenges many visual tracking techniques. For example, the particle filtering technique is plagued by the demanding requirement of a huge number of particles and the phenomenon of particle degeneracy. This paper presents a novel approach to tracking the articulated hand in video by learning and integrating natural hand motion priors. To cope with the finger articulation, this paper proposes a powerful sequential Monte Carlo tracking algorithm based on importance sampling techniques, where the importance function is based on an initial manifold model of the articulation configuration space learned from motion-captured data. In addition, this paper presents a divide-and-conquer strategy that decouples the hand poses and finger articulations and integrates them in an iterative framework to reduce the complexity of the problem. Our experiments show that this approach is effective and efficient for tracking the articulated hand. This approach can be extended to track other articulated targets.

Narrow directional steerable filters in motion estimation

Long-range imaging with visible or infrared observation systems is typically hampered by atmospheric turbulence. Software-based turbulence mitigation methods aim to stabilize and sharpen such recorded image sequences based on the image data only. Although successful restoration has been achieved on static scenes in the past, a significant challenge remains in accounting for moving objects such that they remain visible as moving objects in the output. Here, we investigate a new approach for turbulence mitigation on background as well as large moving objects under moderate turbulence conditions. In our method, we apply and compare different optical flow algorithms to locally estimate both the apparent and true object motion in image sequences and subsequently apply dynamic super-resolution, image sharpening, and newly developed local stabilization methods to the aligned images. We assess the use of these stabilization methods as well as a new method for occlusion compensation for these conditions. The proposed methods are qualitatively evaluated on several visible light recordings of real-world scenes. We demonstrate that our methods achieve a similar image quality on background elements as our prior methods for static scenes, but at the same time obtain a substantial improvement in image quality and reduction in image artifacts on moving objects. In addition, we show that our stabilization and occlusion compensation methods can be robustly used for turbulence mitigation in imagery featuring complex backgrounds and occlusion effects, without compromising the performance in less challenging conditions.

Robust motion estimation under varying illumination

In this paper we present a comprehensive energy-based framework for the estimation and the segmentation of the apparent motion in image sequences. The robust cost functions and the associated hiera...

This paper presents a robust motion detection technique using unit gradient vectors (UGVs). UGVs carry only gradient orientation information that is remarkably invariant to changes of image intensities. Hence, UGVs can be used as a stable textural feature in place of conventional image features intensities and gradients. We propose to detect motions in image sequences using the pixel-wise variations of the UGVs over time. Simulation results show that the proposed method performs motion detection robustly irrespective of varying image intensities. The method also works well for low-contrast images.

Several video coding standards and techniques have been introduced for multimedia applications, particularly h.26x series for video processing. These standards employ motion estimation process for reducing the amount of data that is required to store or transmit the video. Motion estimation process is an inextricable part of the video coding as it removes the temporal redundancy between successive frames of video sequences. This paper is about these motion estimation algorithms, their search procedures, complexity, advantages, and limitations. A survey of motion estimation algorithms including full search algorithm, many fast search, and fast full search block-based algorithms has been presented. An evaluation of up to date motion estimation algorithms, based on a number of empirical results on several test video sequences, is presented as well.