Binocular Imaging System Research Articles

Image-to-image machine translation enables computational defogging in real-world images.

Computational defogging using machine learning presents significant potential; however, its progress is hindered by the scarcity of large-scale datasets comprising real-world paired images with sufficiently dense fog. To address this limitation, we developed a binocular imaging system and introduced Stereofog-an open-source dataset comprising 10,067 paired clear and foggy images, with a majority captured under dense fog conditions. Utilizing this dataset, we trained a pix2pix image-to-image (I2I) translation model and achieved a complex wavelet structural similarity index (CW-SSIM) exceeding 0.7 and a peak signal-to-noise ratio (PSNR) above 17, specifically under dense fog conditions (characterized by a Laplacian variance, vL < 10). We note that Stereofog contains over 70% of dense-fog images. In contrast, models trained on synthetic data, or real-world images augmented with synthetic fog, exhibited suboptimal performance. Our comprehensive performance analysis highlights the model's limitations, such as issues related to dataset diversity and hallucinations-challenges that are pervasive in machine-learning-based approaches. We also propose several strategies for future improvements. Our findings emphasize the promise of machine-learning techniques in computational defogging across diverse fog conditions. This work contributes to the field by offering a robust, open-source dataset that we anticipate will catalyze advancements in both algorithm development and data acquisition methodologies.

Binocular reconstruction of breaking ship bow waves in circulating water channel

This study conducts an experiment on the bow waves generated by a 1:80 KRISO container ship (KCS) model in a circulating water channel. The Froude numbers (characterized by the length of perpendiculars) reach high conditions of 0.377, 0.424, and 0.471. A binocular imaging system is employed to reconstruct three-dimensional configurations of the bow wave surface. The dynamic characteristics of bow wave fields are investigated by observing the standard deviation and employing the Proper Orthogonal Decomposition (POD) method. The power spectral density function presents a power-law relationship with frequency. A novel technique referred as Stereoscopic Foam Image Velocimetry (SFIV) is developed to extract the instantaneous three-dimensional velocity of the foam in breaking area. The velocity field obtained through SFIV defines the nominal time-averaged velocity and nominal turbulent energy. A correlation mapping from pixel coordinates to world coordinates is established to evaluate the actual physical uncertainty. The results demonstrate the superior spatiotemporal accuracy and resolution of binocular imaging technology in measuring the characteristics of bow waves. This research provides significant experimental data of surface three-dimensional configurations for the bow waves breaking issues at high Froude numbers.

Binocular stereo matching of real scenes based on a convolutional neural network and computer graphics.

The binocular stereo matching method based on deep learning has limited cross-domain generalization ability, and it is a tricky problem to obtain a large amount of data from real scenes for training. The most advanced stereo matching network is difficult to apply to new real scenes. In this paper, we propose a real-scene stereo matching method based on a convolutional neural network and computer graphics. A virtual binocular imaging system is constructed by introducing graphics software, and a high-quality semi-synthetic dataset close to the texture characteristics of the real scene is constructed for training the network. A feature standardization layer is embedded in the feature extraction module of the proposed network to further reduce the feature space difference between semi-synthetic data and real scene data. Three small 4D cost volumes are constructed to replace one large 4D cost volume, which reduces GPU memory consumption and improves the matching performance of the network. The experimental results show that compared with the traditional stereo matching method, the matching accuracy of the proposed method is significantly improved by about 60%. Compared with other learning-based methods, the matching accuracy is increased by about 30%, the matching speed is increased by 38%, and it has good robustness to the interference of defocus blur and Gaussian noise.

Foreign object recognition technology for port transportation channel based on automatic image recognition

In the port transportation, the transportation vehicle is extremely easy to cause a bad condition in the transportation process due to the influence of foreign matter, and the current foreign matter detection is still carried out by manual methods. Based on this, this study is based on the automatic image recognition technology, using the binocular imaging system for video collection and analyzing the video images for foreign object recognition. We use extreme median filtering to perform image noise processing and improve the traditional Canny edge detection algorithm to obtain an improved edge detection method for Canny operator. The algorithm is used to perform image edge detection, and the image gray histogram is used to enhance the foreign object image processing. Combined with the experimental analysis, the detection method of this study has certain effects, which can be used for foreign object identification in port transportation channels and can provide theoretical reference for subsequent related research.

Stereo fusion: Combining refractive and binocular disparity

The performance of depth reconstruction in binocular stereo relies on how adequate the predefined baseline for a target scene is. Wide-baseline stereo is capable of discriminating depth better than the narrow-baseline stereo, but it often suffers from spatial artifacts. Narrow-baseline stereo can provide a more elaborate depth map with fewer artifacts, while its depth resolution tends to be biased or coarse due to the short disparity. In this paper, we propose a novel optical design of heterogeneous stereo fusion on a binocular imaging system with a refractive medium, where the binocular stereo part operates as wide-baseline stereo, and the refractive stereo module works as narrow-baseline stereo. We then introduce a stereo fusion workflow that combines the refractive and binocular stereo algorithms to estimate fine depth information through this fusion design. In addition, we propose an efficient calibration method for refractive stereo. The quantitative and qualitative results validate the performance of our stereo fusion system in measuring depth in comparison with homogeneous stereo approaches.

A Novel Binocular Vision System for Wearable Devices.

We present a novel binocular imaging system for wearable devices incorporating the biology knowledge of the human eyes. Unlike the camera system in smartphones, two fish-eye lenses with a larger angle of view are used, the visual field of the new system is larger, and the central resolution of output images is higher. This design leads to more effective image acquisition, facilitating computer vision tasks such as target recognition, navigation and object tracking.

The Error Analysis about Gauge Visual Detection System between High Signals and Contact Net

t is need to experiment to verify the correctness and validity of various stages in system design after gauge visual detection system designing between high signals and contact net. Then it does error analysis from lighting conditions, camera resolution, binocular imaging system installation structure, camera out of synchronized, noise and subsequent image processing operations, etc. It analysis the systematic errors principle and specific impact, then identifies specific improvements.

Detecting binocular half-occlusions: empirical comparisons of five approaches

Binocular half-occlusion points are those that are visible in one of the two views provided by a binocular imaging system. Due to their importance in binocular matching as well as, subsequent interpretation tasks, a number of approaches have been developed for dealing with such points. In the current paper, we consider five methods that explicitly detect half-occlusions and report on a more uniform comparison than has previously been performed. Taking a disparity image and its associated match goodness image as input, we generate images that show the half-occluded points in the underlying scene. We quantitatively and qualitatively compare these methods under a variety of conditions.

Intermediate view synthesis considering occluded and ambiguously referenced image regions

In this paper, we present an algorithm for synthesizing intermediate views from a single stereo-pair. The key contributions of this algorithm are the incorporation of scene assumptions and a disparity estimation confidence measure that lead to the accurate synthesis of occluded and ambiguously referenced regions. The synthesized views have been displayed on a multi-view binocular imaging system, with subjectively effective motion parallax and diminished eye strain.