Segmentation Clues Research Articles

Complementary Coarse-to-Fine Matching for Video Object Segmentation

Semi-supervised Video Object Segmentation (VOS) needs to establish pixel-level correspondences between a video frame and preceding segmented frames to leverage their segmentation clues. Most works rely on features at a single scale to establish those correspondences, e.g., perform dense matching with Convolutional Neural Network (CNN) features from a deep layer. Differently, this work explores complementary features at different scales to pursue more robust feature matching. A coarse feature from a deep layer is first adopted to get coarse pixel-level correspondences. We hence evaluate the quality of those correspondences, and select pixels with low-quality correspondences for fine-scale feature matching. Segmentation clues of previous frames are propagated by both coarse and fine-scale correspondences, which are fused with appearance features for object segmentation. Compared with previous works, this coarse-to-fine matching scheme is more robust to distractions by similar objects and better preserves object details. The sparse fine-scale matching also ensures a fast inference speed. On popular VOS datasets including DAVIS and YouTube-VOS, the proposed method shows promising performance compared with recent works.

Learning efficient multi-task stereo matching network with richer feature information

Accurate depth estimation is a research hotspot in the field of stereo vision. The accuracy of stereo matching algorithm directly determines the quality of depth map. Recent researches have transformed stereo matching methods into a supervised learning task. However, the previous methods may have mismatches in the regions of non-textures, boundaries and tiny details. In this paper, we propose a multi-task attention stereo network (MASNet) to integrate the feature information from a stereo image pairs for disparity estimation. Firstly, a segmentation attention head module (SAH) is proposed, which adds semantic segmentation clues for disparity estimation, uses global receptive field to guide network feature extraction learning refined features, and alleviates the negative impact of depth addition of the network. Secondly, we construct a multiple cost volume (MCV) to make full use of the aggregation ability of 3D convolution and provide a better similarity measures for disparity estimation. Thirdly, we embed Top-k pooling layer into the 3D CNN module to obtain the reduced aggregation feature. The feature is fed into the proposed shallow merging network and fused with the intermediate feature to obtain richer low-level features and make up for the comprehensiveness of network neck feature. The results of experiment on Scene Flow, KITTI 2012, and KITTI 2015 datasets show that our proposed network has a significant superiority over state-of-art stereo matching methods.

A shape guided C–V model to segment the levator ani muscle in axial magnetic resonance images

This paper proposes a modified Chan–Vese model to segment levator ani muscles from axial magnetic resonance (MR) images. Intensity variances of the foreground and the background are used as the main segmentation clues. As in most cases the boundary of the rectum can be successfully segmented in axial MR images, it is assumed as a priori and is used to define the region of interest and the initial contour. In order to handle the complex influences of the connective tissues, a shape influence field is formed based on the shape information of the rectum and is integrated into the Chan–Vese model. Several segmentation examples are presented to show the effectiveness of the proposed method and the necessity of adding the shape influences.

Pruning-Based Unsupervised Segmentation for Korean

Compound noun segmentation is a key component for Korean language processing. Supervised approaches require some types of human intervention such as maintaining lexicons, manually segmenting the corpora, or devising heuristic rules. Thus, they suffer from the unknown word problem, and cannot distinguish domain-oriented or corpus-directed segmentation results from the others. These problems can be overcome by unsupervised approaches that employ segmentation clues obtained purely from a raw corpus. However, most unsupervised approaches require tuning of empirical parameters or learning of the statistical dictionary. To develop a tuning-less, learning-free unsupervised segmentation algorithm, this study proposes a pruning-based unsupervised technique that eliminates unhelpful segmentation candidates. In addition, unlike previous unsupervised methods that have relied on purely character-based segmentation clues, this study utilizes word-based segmentation clues. Experimental evaluations show that the pruning scheme is very effective to unsupervised segmentation of Korean compound nouns, and the use of word-based prior knowledge enables better segmentation accuracy. This study also shows that the proposed algorithm performs competitively with or better than other unsupervised methods.

Collection-based compound noun segmentation for Korean information retrieval

Compound noun segmentation is a key first step in language processing for Korean. Thus far, most approaches require some form of human supervision, such as pre-existing dictionaries, segmented compound nouns, or heuristic rules. As a result, they suffer from the unknown word problem, which can be overcome by unsupervised approaches. However, previous unsupervised methods normally do not consider all possible segmentation candidates, and/or rely on character-based segmentation clues such as bi-grams or all-length n-grams. So, they are prone to falling into a local solution. To overcome the problem, this paper proposes an unsupervised segmentation algorithm that searches the most likely segmentation result from all possible segmentation candidates using a word-based segmentation context. As word-based segmentation clues, a dictionary is automatically generated from a corpus. Experiments using three test collections show that our segmentation algorithm is successfully applied to Korean information retrieval, improving a dictionary-based longest-matching algorithm.

A nonlinear regularization approach to early vision.

We propose a new class of approaches to smooth visual data while preserving significant transitions of these data as clues for segmentation. Formally, the given visual data are represented as a noisy (image) function g, and we present a class of continuously formulated global minimization problems to smooth g. The resulting function u can be characterized as the minimizer of a specific nonquadratic functional or, equivalently, as the result of an associated nonlinear diffusion process. Our approach generalizes the well-known quadratic regularization principle while retaining its attractive properties: For any given g, the solution u to the proposed minimization problem is unique and depends continuously on the data g. Furthermore, convergence of approximate solutions obtained by finite element discretization holds true. We show that the nodal variables of any chosen finite element subspace can be interpreted as computational units whose activation dynamics due to the nonlinear smoothing process evolve like a globally asymptotically stable network. A corresponding analogue implementation is thus feasible and would provide a real time processing stage for the transition preserving smoothing of visual data. Using artificial as well as real data we illustrate our approach by numerical examples. We demonstrate that solutions to our approach improve those obtained by quadratic minimization and show the influence of global parameters which allow for a continuous, scale-dependent, and selective control of the smoothing process.