Seungryong Kim

Mahalanobis Distance Cross-Correlation for Illumination-Invariant Stereo Matching

A robust similarity measure called the Mahalanobis distance cross-correlation (MDCC) is proposed for illumination-invariant stereo matching, which uses a local color distribution within support windows. It is shown that the Mahalanobis distance between the color itself and the average color is preserved under affine transformation. The MDCC converts pixels within each support window into the Mahalanobis distance transform (MDT) space. The similarity between MDT pairs is then computed using the cross-correlation with an asymmetric weight function based on the Mahalanobis distance. The MDCC considers correlation on cross-color channels, thus providing robustness to affine illumination variation. Experimental results show that the MDCC outperforms state-of-the-art similarity measures in terms of stereo matching for image pairs taken under different illumination conditions.

Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields

We present a method for jointly predicting a depth map and intrinsic images from single-image input. The two tasks are formulated in a synergistic manner through a joint conditional random field (CRF) that is solved using a novel convolutional neural network (CNN) architecture, called the joint convolutional neural field (JCNF) model. Tailored to our joint estimation problem, JCNF differs from previous CNNs in its sharing of convolutional activations and layers between networks for each task, its inference in the gradient domain where there exists greater correlation between depth and intrinsic images, and the incorporation of a gradient scale network that learns the confidence of estimated gradients in order to effectively balance them in the solution. This approach is shown to surpass state-of-the-art methods both on single-image depth estimation and on intrinsic image decomposition.

DASC: Dense adaptive self-correlation descriptor for multi-modal and multi-spectral correspondence

Establishing dense visual correspondence between multiple images is a fundamental task in many applications of computer vision and computational photography. Classical approaches, which aim to estimate dense stereo and optical flow fields for images adjacent in viewpoint or in time, have been dramatically advanced in recent studies. However, finding reliable visual correspondence in multi-modal or multi-spectral images still remains unsolved. In this paper, we propose a novel dense matching descriptor, called dense adaptive self-correlation (DASC), to effectively address this kind of matching scenarios. Based on the observation that a self-similarity existing within images is less sensitive to modality variations, we define the descriptor with a series of an adaptive self-correlation similarity for patches within a local support window. To further improve the matching quality and runtime efficiency, we propose a randomized receptive field pooling, in which a sampling pattern is optimized with a discriminative learning. Moreover, the computational redundancy that arises when computing densely sampled descriptor over an entire image is dramatically reduced by applying fast edge-aware filtering. Experiments demonstrate the outstanding performance of the DASC descriptor in many cases of multi-modal and multi-spectral correspondence.

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. Unlike traditional dense correspondence approaches for estimating depth or optical flow, semantic correspondence estimation poses additional challenges due to intra-class appearance and shape variations among different instances within the same object or scene category. To robustly match points across semantically similar images, we formulate FCSS using local self-similarity (LSS), which is inherently insensitive to intra-class appearance variations. LSS is incorporated through a proposed convolutional self-similarity (CSS) layer, where the sampling patterns and the self-similarity measure are jointly learned in an end-to-end and multi-scale manner. Furthermore, to address shape variations among different object instances, we propose a convolutional affine transformer (CAT) layer that estimates explicit affine transformation fields at each pixel to transform the sampling patterns and corresponding receptive fields. As training data for semantic correspondence is rather limited, we propose to leverage object candidate priors provided in most existing datasets and also correspondence consistency between object pairs to enable weakly-supervised learning. Experiments demonstrate that FCSS significantly outperforms conventional handcrafted descriptors and CNN-based descriptors on various benchmarks.

A multi-vision sensor-based fast localization system with image matching for challenging outdoor environments

Robust feature point extraction to provide viewpoint invariances with virtual viewpoint.Robust feature descriptor to provide illumination invariances with modified binary pattern.Efficient computational model to reduce time complexity of localization system.Extensive experiments and simulation demos for objective evaluations. A sensor-based vision localization system is one of the most essential technologies in computer vision applications like an autonomous navigation, surveillance, and many others. Conventionally, sensor-based vision localization systems have three inherent limitations, These include, sensitivity to illumination variations, viewpoint variations, and high computational complexity. To overcome these problems, we propose a robust image matching method to provide invariance to the illumination and viewpoint variations by focusing on how to solve these limitations and incorporate this scheme into the vision-based localization system. Based on the proposed image matching method, we design a robust localization system that provides satisfactory localization performance with low computational complexity. Specifically, in order to solve the problem of illumination and viewpoint, we extract a key point using a virtual view from a query image and the descriptor based on the local average patch difference, similar to HC-LBP. Moreover, we propose a key frame selection method and a simple tree scheme for fast image search. Experimental results show that the proposed localization system is four times faster than existing systems, and exhibits better matching performance compared to existing algorithms in challenging environments with difficult illumination and viewpoint conditions.

Local self-similarity frequency descriptor for multispectral feature matching

This paper describes a robust feature descriptor called the local self-similarity frequency (LSSF) for the multispectral RGB-NIR feature matching, which uses the frequency response of the local internal layout of self-similarities. A nonlinear relationship between multi-spectral image pairs makes conventional descriptors be sensitive to spectral deformation. To alleviate this problem, the LSSF employs a weighted correlation surface reducing the discrepancy between mul-tispectral images. Furthermore, the LSSF provides a rotation invariance exploiting the frequency response of maximal values on logpolar bins based on the fact that a cyclic shift on the log-polar representation leads only a phase shift in a frequency domain. Experimental results show that LSSF outperforms state-of-the-art descriptors in terms of a recognition rate for multispectral RGB-NIR image pairs.

DASC: Robust Dense Descriptor for Multi-Modal and Multi-Spectral Correspondence Estimation

Establishing dense correspondences between multiple images is a fundamental task in many applications. However, finding a reliable correspondence between multi-modal or multi-spectral images still remains unsolved due to their challenging photometric and geometric variations. In this paper, we propose a novel dense descriptor, called dense adaptive self-correlation (DASC), to estimate dense multi-modal and multi-spectral correspondences. Based on an observation that self-similarity existing within images is robust to imaging modality variations, we define the descriptor with a series of an adaptive self-correlation similarity measure between patches sampled by a randomized receptive field pooling, in which a sampling pattern is obtained using a discriminative learning. The computational redundancy of dense descriptors is dramatically reduced by applying fast edge-aware filtering. Furthermore, in order to address geometric variations including scale and rotation, we propose a geometry-invariant DASC (GI-DASC) descriptor that effectively leverages the DASC through a superpixel-based representation. For a quantitative evaluation of the GI-DASC, we build a novel multi-modal benchmark as varying photometric and geometric conditions. Experimental results demonstrate the outstanding performance of the DASC and GI-DASC in many cases of dense multi-modal and multi-spectral correspondences.

Feature Augmentation for Learning Confidence Measure in Stereo Matching

Confidence estimation is essential for refining stereo matching results through a post-processing step. This problem has recently been studied using a learning-based approach, which demonstrates a substantial improvement on conventional simple non-learning based methods. However, the formulation of learning-based methods that individually estimates the confidence of each pixel disregards spatial coherency that might exist in the confidence map, thus providing a limited performance under challenging conditions. Our key observation is that the confidence features and resulting confidence maps are smoothly varying in the spatial domain, and highly correlated within the local regions of an image. We present a new approach that imposes spatial consistency on the confidence estimation. Specifically, a set of robust confidence features is extracted from each superpixel decomposed using the Gaussian mixture model, and then these features are concatenated with pixel-level confidence features. The features are then enhanced through adaptive filtering in the feature domain. In addition, the resulting confidence map, estimated using the confidence features with a random regression forest, is further improved through K-nearest neighbor based aggregation scheme on both pixel- and superpixel-level. To validate the proposed confidence estimation scheme, we employ cost modulation or ground control points based optimization in stereo matching. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on various benchmarks including challenging outdoor scenes.Confidence estimation is essential for refining stereo matching results through a post-processing step. This problem has recently been studied using a learning-based approach, which demonstrates a substantial improvement on conventional simple non-learning based methods. However, the formulation of learning-based methods that individually estimates the confidence of each pixel disregards spatial coherency that might exist in the confidence map, thus providing a limited performance under challenging conditions. Our key observation is that the confidence features and resulting confidence maps are smoothly varying in the spatial domain, and highly correlated within the local regions of an image. We present a new approach that imposes spatial consistency on the confidence estimation. Specifically, a set of robust confidence features is extracted from each superpixel decomposed using the Gaussian mixture model, and then these features are concatenated with pixel-level confidence features. The features are then enhanced through adaptive filtering in the feature domain. In addition, the resulting confidence map, estimated using the confidence features with a random regression forest, is further improved through K-nearest neighbor based aggregation scheme on both pixel- and superpixel-level. To validate the proposed confidence estimation scheme, we employ cost modulation or ground control points based optimization in stereo matching. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on various benchmarks including challenging outdoor scenes.

Multi-spectral pedestrian detection based on accumulated object proposal with fully convolutional networks

This paper presents a method for detecting a pedestrian by leveraging multi-spectral image pairs. Our approach is based on the observation that a multi-spectral image, especially far-infrared (FIR) image, enables us to overcome inherent limitations for pedestrian detection under challenging circumstances, such as even dark environments. For that task, multi-spectral color-FIR image pairs are used in a synergistic manner for pedestrian detection through deep convolutional neural networks (CNNs) learning and support vector regression (SVR). For inferring the confidence of a pedestrian, we first learn CNNs between color images (or FIR images) and bounding box annotations of pedestrians, respectively. Furthermore, for each object proposal, we extract intermediate activation features from network, and learn the probability of pedestrian using SVR. To improve the detection performance, the learned probability of pedestrian for each proposal is accumulated on the image domain. Based on the pedestrian confidence estimated from each network and accumulated pedestrian probabilities, the most probable pedestrian is finally localized among object proposal candidates. Thanks to its high robustness of multi-spectral imaging in dark environments and its high discriminative power of deep CNNs, our framework is shown to surpass state-of-the-art pedestrian detection methods on multi-spectral pedestrian benchmark.

ABFT: Anisotropic binary feature transform based on structure tensor space

Local feature matching is a fundamental step for many computer vision applications. Recently, binary feature transforms have been popularly proposed to improve the computational efficiency while preserving high matching performance. However, it is sensitive to noise and geometrical distortion such as affine transformation. In this paper, we propose ABFT framework, composed of a noise robust feature detection and affine invariant binary feature description based on a structure tensor space. Experimental results show that ABFT outperforms other state-of-the-art feature transforms in terms of the repeatability, recognition rate, and computational time.