Binocular Stereo Vision Theory Research Articles

Design of laser scanning binocular stereo vision imaging system and target measurement

Binocular stereo vision is an important scheme of road environment perception for intelligent driving, because it can provide image information and spatial three-dimensional position information. However, binocular stereo matching is often disturbed by the environment, and noise, shadow, light transformation, and so on, will all cause mismatching, therefore it is impossible to carry out accurate 3D reconstruction. In this paper, an active imaging perception and target measurement method combining laser scanning and binocular vision is proposed. The binocular vision structure is used to simulate the human eye's observation of the environment in front of the vehicle. At the same time, through the laser lines scanned to the target, the strong characteristics of binocular matching are provided to enhance the anti-interference of binocular matching, and calculate the three-dimensional point cloud data of the front object according to the binocular stereo vision theory. Firstly, this paper studies the influence of the parameters changing of the system on the accuracy of object depth value in indoor environment, and then selects the most suitable system parameters to carry out three-dimensional reconstruction experiment on the actual target object in indoor environment. Finally, the scene experiment in outdoor environment is carried out. In the outdoor environment, the red spectrum laser equipped in the system can still effectively reconstruct the target surface radian, but there are some limitations due to the limitation of outdoor light intensity. This paper preliminarily summarizes the current structural design and experiment, and further discusses the follow-up research and improvement ideas. Research and experiments show that the laser scanning binocular stereo vision imaging system proposed in this paper has the function of forward target ranging, especially under some bad imaging environment, this method can also effectively match the binocular images, and present the three-dimensional information of the internal details of the target. Under the indoor experimental environment, the minimum relative error is 1.42 % for the target with a distance of about 1.5 m, and in the evening outdoor environment, the minimum the minimum relative error is 0.87 % for the target with a distance of about 3 m. Due to the advantages of laser line intensity feature matching, it can accelerate the speed of binocular matching, and has potential application prospects in autonomous system.

Altitude Information Acquisition of UAV Based on Monocular Vision and MEMS

Altitude information is one of the most important data pieces to realize unmanned aerial vehicle (UAV) autonomous flight. At present, the barometers equipped in the UAVs were used to measure the atmospheric pressure and convert the data to the altitude of UAVs. But the pressure will change drastically when the propeller of the UAV rotate and lead to inaccurate measurement values. In this paper, a UAV height information acquisition method based on the information fusion of monocular vision and a microelectromechanical system (MEMS) is designed, which can obtain accurate height information in the indoor environment and low-altitude outdoor environment. The parallax is obtained by using two images taken in a short time during UAV flight in this method, and the acceleration data measured by the MEMS are used to compute the displacements of the UAV in this time as the baseline. The angle information measured by the MEMS is used to calibrate the images taken by the monocular camera of the UAV. Finally, the altitude information of the UAV is obtained with the theory of binocular stereo vision. Experiments have proven that the altitude information errors obtained by this method at 2 m is approximately 4%, which is less than barometer errors. A new image crop method was proposed to reduce computation, which meets the steady, fast and accurate requirements for practical application. The experiments show that our proposed method can obtain satisfactory results with respect to the state-of-the-art methods.

Shape context and projection geometry constrained vasculature matching for 3D reconstruction of coronary artery

Vascular structure matching in X-ray angiographic images sequence obtained at different imaging angles is important for identifying vascular structures in computer-assisted diagnosis of coronary artery diseases. In this paper, a novel shape context and projection geometry constraint method is proposed for soft matching and identification of coronary artery structures. Firstly, the matching energy function between vasculatures in different angiographic views is constructed based on the local geometry constraint and perspective projection constraint. Secondly, initial matching of the vasculatures is established by the shape context constraint. Thirdly, the deterministic annealing method is used to optimize the matching function. Hence, optimal correspondences are obtained by iteratively reducing the temperature of the optimization function. Finally, on the basis of the obtained correspondences, 3D coronary artery structure can be reconstructed according to the theory of binocular stereo vision in computer vision. Experiments show that the relationship among the different views can be accurately constructed by the proposed method. The proposed method is fully automatic, so it can assist physician to rapidly identify and correlate vascular structures from angiographic images obtained at different imaging angles.

3D Human behavior recognition based on binocular vision and face–hand feature

To improve traditional video surveillance systems’ performance on human behavior recognition, a new method based on binocular vision was researched. This paper proposed a human behavior recognition method based on binocular stereo vision and human face–hand feature. Firstly, the paper got extrinsic and intrinsic parameters of the two cameras through binocular stereo vision calibration, then the paper located face and hands region through human face–hand feature segmentation method, and next then for getting the 3D information of face and hands by binocular stereo vision theory, the paper proposed a feature block matching algorithm based on local gray area correlation and epipolar constraint rule to match corresponding feature regional centers. And finally, the paper completed the analysis of the human upper body poses, 3D spatial moving and the measurement of moving velocity based on 3D information of face and hands. Experimental results showed that the method can successfully recognize human behaviors, and it can solve the occlusion and multi-direction movement problem of traditional video surveillance system. Our behavior recognition method is straightforward and robust. The uniqueness is different from traditional machine learning and classification framework about human behavior recognition.

A new method to evaluate fabric shape style based on motion capture

This study proposes a new method to evaluate the fabric shape style. Two high-speed CCD cameras are used to capture dynamic image sequences of the fabric motion. Then, the theory of binocular stereo vision is applied to obtain the 3D coordinates of the feature points in the fabric image sequences. The characteristic parameters that represent the motion style of the fabric are extracted, and fuzzy matter-element theory is applied to evaluate the fabric shape style. This method differs from traditional ones in which multiple measurements and multiple indexes are required to evaluate the fabric. The proposed method is simpler and more convenient; it can be used to evaluate the fabric style using only visual information differences between various fabrics and by one non-contact measurement of the fabric motion.

In vivo near-infrared fluorescence three-dimensional positioning system with binocular stereovision.

Fluorescence is a powerful tool for in-vivo imaging in living animals. The traditional in-vivo fluorescence imaging equipment is based on single-view two-dimensional imaging systems. However, they cannot meet the needs for accurate positioning during modern scientific research. A near-infrared in-vivo fluorescence imaging system is demonstrated, which has the capability of deep source signal detecting and three-dimensional positioning. A three-dimensional coordinates computing (TDCP) method including a preprocess algorithm is presented based on binocular stereo vision theory, to figure out the solution for diffusive nature of light in tissue and the emission spectra overlap of fluorescent labels. This algorithm is validated to be efficient to extract targets from multispectral images and determine the spot center of biological interests. Further data analysis indicates that this TDCP method could be used in three-dimensional positioning of the fluorescent target in small animals. The study also suggests that the combination of a large power laser and deep cooling charge-coupled device will provide an attractive approach for fluorescent detection from deep sources. This work demonstrates the potential of binocular stereo vision theory for three-dimensional positioning for living animal in-vivo imaging.