Seungkyu Lee

Shape Variation-Based Frieze Pattern for Robust Gait Recognition

Gait is an attractive biometric for vision-based human identification. Previous work on existing public data sets has shown that shape cues yield improved recognition rates compared to pure motion cues. However, shape cues are fragile to gross appearance variations of an individual, for example, walking while carrying a ball or a backpack. We introduce a novel, spatiotemporal shape variation-based frieze pattern (SVB frieze pattern) representation for gait, which captures motion information over time. The SVB frieze pattern represents normalized frame difference over gait cycles. Rows/columns of the vertical/horizontal SVB frieze pattern contain motion variation information augmented by key frame information with body shape. A temporal symmetry map of gait patterns is also constructed and combined with vertical/horizontal SVB frieze patterns for measuring the dissimilarity between gait sequences. Experimental results show that our algorithm improves gait recognition performance on sequences with and without gross differences in silhouette shape. We demonstrate superior performance of this computational framework over previous algorithms using shape cues alone on both CMU MoBo and UoS HumanID gait databases.

Curved Glide-Reflection Symmetry Detection

We generalize the concept of bilateral reflection symmetry to curved glide-reflection symmetry in 2D euclidean space, such that classic reflection symmetry becomes one of its six special cases. We propose a local feature-based approach for curved glide-reflection symmetry detection from real, unsegmented 2D images. Furthermore, we apply curved glide-reflection axis detection for curved reflection surface detection in 3D images. Our method discovers, groups, and connects statistically dominant local glide-reflection axes in an Axis-Parameter-Space (APS) without preassumptions on the types of reflection symmetries. Quantitative evaluations and comparisons against state-of-the-art algorithms on a diverse 64-test-image set and 1,125 Swedish leaf-data images show a promising average detection rate of the proposed algorithm at 80 and 40 percent, respectively, and superior performance over existing reflection symmetry detection algorithms. Potential applications in computer vision, particularly biomedical imaging, include saliency detection from unsegmented images and quantification of deviations from normality. We make our 64-test-image set publicly available.

Skewed Rotation Symmetry Group Detection

We present a novel and effective algorithm for affinely skewed rotation symmetry group detection from real-world images. We define a complete skewed rotation symmetry detection problem as discovering five independent properties of a skewed rotation symmetry group: 1) the center of rotation, 2) the affine deformation, 3) the type of the symmetry group, 4) the cardinality of the symmetry group, and 5) the supporting region of the symmetry group in the image. We propose a frieze-expansion (FE) method that transforms rotation symmetry group detection into a simple, 1D translation symmetry detection problem. We define and construct a pair of rotational symmetry saliency maps, complemented by a local feature method. Frequency analysis, using Discrete Fourier Transform (DFT), is applied to the frieze-expansion patterns (FEPs) to uncover the types (cyclic, dihedral, and O(2)), the cardinalities, and the corresponding supporting regions, concentric or otherwise, of multiple rotation symmetry groups in an image. The phase information of the FEP is used to rectify affinely skewed rotation symmetry groups. Our result advances the state of the art in symmetry detection by offering a unique combination of region-based, feature-based, and frequency-based approaches. Experimental results on 170 synthetic and natural images demonstrate superior performance of our rotation symmetry detection algorithm over existing methods.

Rotation symmetry group detection via frequency analysis of frieze-expansions

We present a novel and effective algorithm for rotation symmetry group detection from real-world images. We propose a frieze-expansion method that transforms rotation symmetry group detection into a simple translation symmetry detection problem. We define and construct a dense symmetry strength map from a given image, and search for potential rotational symmetry centers automatically. Frequency analysis, using discrete Fourier transform (DFT), is applied to the frieze-expansion patterns to uncover the types and the cardinality of multiple rotation symmetry groups in an image, concentric or otherwise. Furthermore, our detection algorithm can discriminate discrete versus continuous and cyclic versus dihedral symmetry groups, and identify the corresponding supporting regions in the image. Experimental results on over 80 synthetic and natural images demonstrate superior performance of our rotation detection algorithm in accuracy and in speed over the state of the art rotation detection algorithms.

Symmetry Detection from RealWorld Images Competition 2013: Summary and Results

Symmetry is a pervasive phenomenon presenting itself in all forms and scales in natural and manmade environments. Its detection plays an essential role at all levels of human as well as machine perception. The recent resurging interest in computational symmetry for computer vision and computer graphics applications has motivated us to conduct a US NSF funded symmetry detection algorithm competition as a workshop affiliated with the Computer Vision and Pattern Recognition (CVPR) Conference, 2013. This competition sets a more complete benchmark for computer vision symmetry detection algorithms. In this report we explain the evaluation metric and the automatic execution of the evaluation workflow. We also present and analyze the algorithms submitted, and show their results on three test sets of real world images depicting reflection, rotation and translation symmetries respectively. This competition establishes a performance baseline for future work on symmetry detection.

Curved glide-reflection symmetry detection

We generalize reflection symmetry detection to a curved glide reflection symmetry detection problem. We propose a unifying, local feature based approach for curved glide reflection symmetry detection from real, unsegmented images, where the classic reflection symmetry becomes one of four special cases. Our method detects and groups statistically dominant local reflection axes in a 3D parameter space. A curved glid reflection symmetry axis is estimated by a set of contiguous local straight reflection axes. Experimental results of the proposed algorithm on 40 real world images demonstrate promising performance.

Projective alignment of range and parallax data

An approximately Euclidean representation of the visible scene can be obtained directly from a range, or ‘time-of-flight’, camera. An uncalibrated binocular system, in contrast, gives only a projective reconstruction of the scene. This paper analyzes the geometric mapping between the two representations, without requiring an intermediate calibration of the binocular system. The mapping can be found by either of two new methods, one of which requires point-correspondences between the range and colour cameras, and one of which does not. It is shown that these methods can be used to reproject the range data into the binocular images, which makes it possible to associate high-resolution colour and texture with each point in the Euclidean representation.

Time-of-Flight Depth Camera Motion Blur Detection and Deblurring

Recently, many consumer time-of-flight depth cameras have been introduced that provide direct 3-D geometry measurement of real world objects in real-time. However, these cameras suffer from motion blur artifact when there is any movement of camera or target object in the scene causing serious geometry measurement distortions. Unlike other noises, depth camera motion blur is difficult to eliminate using any existing image processing method due to the unique principle of time-of-flight depth calculation. In this letter, we propose a novel depth motion blur detection and deblurring method that can be applied for any ToF depth sensor. Our method utilizes the relations between different phase offsets observed at multiple time slots in ToF sensor. Experimental results show that the proposed method successfully detects motion blur regions and accurately eliminates them with minimal computational cost in real time.

Symmetry-driven shape description for image retrieval ☆

Abstract We propose a novel symmetry-driven Bayesian framework to incorporate structural shape into conventional geometrical shape descriptor of an image indexing and retrieval. We use rotation and reflection symmetries for structural shape description. Symmetry detection on each shape image provides a qualitative and a quantitative categorization of the types and the degrees of symmetry level. The posterior shape similarity enhances the shape matching performance based on the symmetry structural discrimination. Experimental results show statistically significant improvement on retrieval accuracy over the state of the art methods on MPEG-7 data set.

Hybrid exposure for depth imaging of a time-of-flight depth sensor

A time-of-flight (ToF) depth sensor produces noisy range data due to scene properties such as surface materials and reflectivity. Sensor measurement frequently includes either a saturated or severely noisy depth and effective depth accuracy is far below its ideal specification. In this paper, we propose a hybrid exposure technique for depth imaging in a ToF sensor so to improve the depth quality. Our method automatically determines an optimal depth for each pixel using two exposure conditions. To show that our algorithm is effective, we compare the proposed algorithm with two conventional methods in qualitative and quantitative manners showing the superior performance of proposed algorithm.