Camera orientation estimation using voting approach on the Gaussian sphere for in-vehicle camera.

Calibration of a vehicle camera is a key technology for advanced driver assistance systems (ADAS). This paper presents a novel estimation method to measure the orientation of a camera that is mounted on a driving vehicle. By considering the characteristics of vehicle cameras and driving environment, we detect three orthogonal vanishing points as a basis of the imaging geometry. The proposed method consists of three steps: i) detection of lines projected to the Gaussian sphere and extraction of the plane normal, ii) estimation of the vanishing point about the optical axis using linear Hough transform, and iii) voting for the rest two vanishing points using circular histogram. The proposed method increases both accuracy and stability by considering the practical driving situation using sequentially estimated three vanishing points. In addition, we can rapidly estimate the orientation by converting the voting space into a 2D plane at each stage. As a result, the proposed method can quickly and accurately estimate the orientation of the vehicle camera in a normal driving situation.

Intelligent Image-Based Railway Inspection System Using Deep Learning-Based Object Detection and Weber Contrast-Based Image Comparison.

For sustainable operation and maintenance of urban railway infrastructure, intelligent visual inspection of the railway infrastructure attracts increasing attention to avoid unreliable, manual observation by humans at night, while trains do not operate. Although various automatic approaches were proposed using image processing and computer vision techniques, most of them are focused only on railway tracks. In this paper, we present a novel railway inspection system using facility detection based on deep convolutional neural network and computer vision-based image comparison approach. The proposed system aims to automatically detect wears and cracks by comparing a pair of corresponding image sets acquired at different times. We installed line scan camera on the roof of the train. Unlike an area-based camera, the line scan camera quickly acquires images with a wide field of view. The proposed system consists of three main modules: (i) image reconstruction for registration of facility positions, (ii) facility detection using an improved single shot detector, and (iii) deformed region detection using image processing and computer vision techniques. In experiments, we demonstrate that the proposed system accurately finds facilities and detects their potential defects. For that reason, the proposed system can provide various advantages such as cost reduction for maintenance and accident prevention.

Disparity-selective stereo matching using correlation confidence measure.

Recently, the cost-volume filtering (CVF) methods for local stereo matching have provided fast and accurate results compared to those of the other method. However, CVF still causes incorrect results in the occlusion and texture-free regions. In particular, cost aggregation by pixel units involves complex computation because of its dependence on the image resolution and search range. This paper presents a robust stereo matching method for occluded regions. First, we generate cost volumes using the CENSUS transform and the scale-invariant feature transform (SIFT). Then, label-based cost volumes are aggregated using adaptive support weight and the simple linear iterative clustering (SLIC) scheme from two generated cost volumes. In order to obtain optimal disparity by two label-based cost volumes, we select the disparity corresponding to high confidence similarity of CENSUS or SIFT with minimum cost point. Experimental results show that our method estimates the optimal disparity in occlusion information, which exists only in the scene of one of the stereo pairs.

Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path.

Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems.

Contrast-dependent saturation adjustment for outdoor image enhancement.

Outdoor images captured in bad-weather conditions usually have poor intensity contrast and color saturation since the light arriving at the camera is severely scattered or attenuated. The task of improving image quality in poor conditions remains a challenge. Existing methods of image quality improvement are usually effective for a small group of images but often fail to produce satisfactory results for a broader variety of images. In this paper, we propose an image enhancement method, which makes it applicable to enhance outdoor images by using content-adaptive contrast improvement as well as contrast-dependent saturation adjustment. The main contribution of this work is twofold: (1) we propose the content-adaptive histogram equalization based on the human visual system to improve the intensity contrast; and (2) we introduce a simple yet effective prior for adjusting the color saturation depending on the intensity contrast. The proposed method is tested with different kinds of images, compared with eight state-of-the-art methods: four enhancement methods and four haze removal methods. Experimental results show the proposed method can more effectively improve the visibility and preserve the naturalness of the images, as opposed to the compared methods.

Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models.

A fundamental limitation of hyperspectral imaging is the inter-band misalignment correlated with subject motion during data acquisition. One way of resolving this problem is to assess the alignment quality of hyperspectral image cubes derived from the state-of-the-art alignment methods. In this paper, we present an automatic selection framework for the optimal alignment method to improve the performance of face recognition. Specifically, we develop two qualitative prediction models based on: 1) a principal curvature map for evaluating the similarity index between sequential target bands and a reference band in the hyperspectral image cube as a full-reference metric; and 2) the cumulative probability of target colors in the HSV color space for evaluating the alignment index of a single sRGB image rendered using all of the bands of the hyperspectral image cube as a no-reference metric. We verify the efficacy of the proposed metrics on a new large-scale database, demonstrating a higher prediction accuracy in determining improved alignment compared to two full-reference and five no-reference image quality metrics. We also validate the ability of the proposed framework to improve hyperspectral face recognition.

Optical range-finding system using a single-image sensor with liquid crystal display aperture.

This Letter presents an optical range-finding camera using a liquid crystal display (LCD) to generate multiple, off-axis color-filtered apertures in a flexible manner. The disparity between the different color channels is measured from a pair of stereo images acquired by two off-axis apertures, and the distance of a scene point from the camera is then estimated from the pre-specified relationship between the color disparity and distance.

Depth map generation using a single image sensor with phase masks.

Conventional stereo matching systems generate a depth map using two or more digital imaging sensors. It is difficult to use the small camera system because of their high costs and bulky sizes. In order to solve this problem, this paper presents a stereo matching system using a single image sensor with phase masks for the phase difference auto-focusing. A novel pattern of phase mask array is proposed to simultaneously acquire two pairs of stereo images. Furthermore, a noise-invariant depth map is generated from the raw format sensor output. The proposed method consists of four steps to compute the depth map: (i) acquisition of stereo images using the proposed mask array, (ii) variational segmentation using merging criteria to simplify the input image, (iii) disparity map generation using the hierarchical block matching for disparity measurement, and (iv) image matting to fill holes to generate the dense depth map. The proposed system can be used in small digital cameras without additional lenses or sensors.

Sensor-based auto-focusing system using multi-scale feature extraction and phase correlation matching.

This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF) algorithm consists of four steps: (i) acquisition of left and right images using AF points in the region-of-interest; (ii) feature extraction in the left image under low illumination and out-of-focus blur; (iii) the generation of two feature images using the phase difference between the left and right images; and (iv) estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Human umbilical cord blood mononuclear cell transplantation in rats with intrinsic sphincter deficiency.

To evaluate the effectiveness of the human umbilical cord blood (HUCB) transplantation for the treatment of intrinsic sphincter deficiency (ISD), we analyzed the short term effects of HUCB mononuclear cell transplantation in rats with induced-ISD. ISD was induced in rats by electro-cauterization of periurethral soft tissue with HUCB mononuclear cell injection after 1 week. The sphincter function measured by mean leak point pressure was significantly improved in the experimental group compared to the control group at 4 weeks. (91.75+/-18.99 mmHg vs. 65.02+/-22.09 mmHg, P=0.001). Histologically, the sphincter muscle was restored without damage while in the control group it appeared markedly disrupted with atrophic muscle layers and collagen deposit. We identified injected HUCB cells in the tissue sections by Di-I signal and Prussian blue staining. HUCB mononuclear cell injection significantly improved urethral sphincter function, suggesting its potential efficacy in the treatment of ISD.