Jinbeum Jang

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.

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.

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.

Defocus-invariant image registration for phase-difference detection auto focusing

This paper presents a defocus-invariant image registration method for measuring the shifting value between two differently located patterns in an imaging sensor. Existing registration methods fail with unfocused images since features or regions of interest are degraded by defocus. In order to solve this problem, the proposed method consists of three stages: i) pre-generation of the set of point spread functions (PSFs) estimated in different focusing positions, ii) the geometric transformation estimation using estimated PSF data, and iii) registration using estimated transformation matrix. The proposed method improves out-of-focus degradation through estimation of PSF. For this reason, the proposed method can accurately estimate the difference of phase between two out-of-focus images. Furthermore, it can be applied to phase-difference detection auto focusing, and provide accurate auto focusing performance.

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.

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.

Image Fusion using Asymmetric Dual Camera for Digital Zooming

This paper presents a method for estimating a seam to fuse two images acquired by asymmetric dual cameras that have different field of views. This method consist of an optimization-based active contour algorithm and morphology.

Hybrid Auto-Focusing System Using Dual Pixel-Type CMOS Sensor With Contrast Detection Algorithm

This paper presents a hybrid auto-focusing system combining two passive methods. The proposed method measures a phase difference using dual pixel-type sensor with black masks, and detects the maximum contrast using multi-scale Laplacian of Gaussian filter.

Computational Image System with Real-Time Controllable Color Coded Aperture Using an LCD

This paper presents a novel computational camera system with variable aperture using a thin-film-transistor liquid crystal display. The proposed system can electronically change the free-form aperture and generate the best aperture for a specific application.