Chenbo Shi

Depth estimation for speckle projection system using progressive reliable points growing matching

In this paper, we propose a progressive reliable points growing matching scheme to estimate the depth from the speckle projection image. First a self-adapting binarization is introduced to reduce the influence of inconsistent intensity. Then we apply local window-based correlation matching to get the initial disparity map. After the initialization, we formulate a progressive updating scheme to update the disparity estimation. There are two main steps in each round of updation. At first new reliable points are progressively selected based on three aspects of criterion including matching degree, confidence, and left-right consistency; then prediction-based growing matching is adopted to recalculate the disparity map from the reliable points. Finally, the more accurate depth map can be obtained by subpixel interpolation and transformation. The experimental results well demonstrate the effectiveness and low computational cost of our scheme.

Iterative transductive learning for alpha matting

In this paper, we propose a matting algorithm based on iterative transductive learning (for short: ITM). To avoid over-smooth results of recent methods, we introduce the influence of unlabeled regions as well as the consistency of neighboring pixels to re-design the optimization for alpha matting. A novel asymmetric Laplacian matrix is also proposed to further relieve the over-smoothness. To optimize the matting problem, we adjust the constrain coefficients between the initialized alpha matte and the asymmetric Laplacian matrix iteratively to achieve accurate alpha mattes. Consequently, during the iteration, high confidence pixels maintain their refined alpha values, whereas low confidence ones are updated by their neighbors gradually. Experimental results demonstrate that our algorithm is more precise than many state-of-the-art methods in terms of the accuracy.

Embedding metric learning into set-based face recognition for video surveillance

Abstract Face recognition in video surveillance is a challenging task, largely due to the difficulty in matching images across cameras of distinct viewpoints and illuminations. To overcome this difficulty, this paper proposes a novel method which embeds distance metric learning into set-based image matching. First we use sets of face images, rather than individual images, as the input for recognition, since in surveillance systems the former is a more natural way. We model each image set using a convex-hull space spanned by its member images and measure the dissimilarity of two sets using the distance between the closest points of their corresponding convex hull spaces. Then we propose a set-based distance metric learning scheme to learn a feature-space mapping to a discriminative subspace. Finally we project image sets into the learned subspace and achieve face recognition by comparing the projected sets. In this way, we can adapt the variation in viewpoints and illuminations across cameras in order to improve face recognition in video surveillance. Experiments on the public Honda/UCSD and ChokePoint databases demonstrate the superior performance of our method to the state-of-the-art approaches.

Efficient active depth sensing by laser speckle projection system

Abstract. An active depth sensing approach by laser speckle projection system is proposed. After capturing the speckle pattern with an infrared digital camera, we extract the pure speckle pattern using a direct-global separation method. Then the pure speckles are represented by Census binary features. By evaluating the matching cost and uniqueness between the real-time image and the reference image, robust correspondences are selected as support points. After that, we build a disparity grid and propose a generative graphical model to compute disparities. An iterative approach is designed to propagate the messages between blocks and update the model. Finally, a dense depth map can be obtained by subpixel interpolation and transformation. The experimental evaluations demonstrate the effectiveness and efficiency of our approach.

Label inspection of approximate cylinder based on adverse cylinder panorama

This paper presents a machine vision system for automated label inspection, with the goal to reduce labor cost and ensure consistent product quality. Firstly, the images captured from each single-camera are distorted, since the inspection object is approximate cylindrical. Therefore, this paper proposes an algorithm based on adverse cylinder projection, where label images are rectified by distortion compensation. Secondly, to overcome the limited field of viewing for each single-camera, our method novelly combines images of all single-cameras and build a panorama for label inspection. Thirdly, considering the shake of production lines and error of electronic signal, we design the real-time image registration to calculate offsets between the template and inspected images. Experimental results demonstrate that our system is accurate, real-time and can be applied for numerous real- time inspections of approximate cylinders.