In this paper, we propose two new, perceptually motivated

Abstract

Segmentation propagation, similar to tracking, is the problem of transferring a segmentation of an image to a neighboring image in a sequence. This problem is of particular importance to materials science, where the accurate segmentation of a series of 2D serial-sectioned images of multiple, contiguous 3D structures has important applications. Such structures may have distinct shape, appearance, and topology, which can be considered to improve segmentation accuracy. For example, some materials images may have structures with a specific shape or appearance in each serial section slice, which only changes minimally from slice to slice, and some materials may exhibit specific inter-structure topology that constrains their neighboring relations. Some of these properties have been individually incorporated to segment specific materials images in prior work. In this paper, we develop a propagation framework for materials image segmentation where each propagation is formulated as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. Our framework makes three key contributions: 1) a homomorphic propagation approach, which considers the consistency of region adjacency in the propagation; 2) incorporation of shape and appearance consistency in the propagation; and 3) a local non-homomorphism strategy to handle newly appearing and disappearing substructures during this propagation. To show the effectiveness of our framework, we conduct experiments on various 3D materials images, and compare the performance against several existing image segmentation methods.

Offline handwritten text recognition is a very challenging problem. Aside

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Multi-target tracking plays a key role in many computer vision applications including robotics, human-computer interaction, event recognition, etc., and has received increasing attention in past several years. Starting with an object detector is one of many approaches used by existing multi-target tracking methods to create initial short tracks called tracklets. These tracklets are then gradually grouped into longer final tracks in a heirarchical framework. Although object detectors have greatly improved in recent years, these detectors are far from perfect and can fail to detect the object of interest or identify a false positive as the desired object. Due to the presence of false positives or misdetections from the object detector, these tracking methods can suffer from track fragmentations and identity switches. To address this problem, we formulate multi-target tracking as a min-cost flow graph problem which we call the average shortest path. This average shortest path is designed to be less biased towards the track length. In our average shortest path framework, object misdetection is treated as an occlusion and is represented by the edges between tracklet nodes across non consecutive frames. We evaluate our method on the publicly available ETH dataset. Camera motion and long occlusions in a busy street scene make ETH a challenging dataset. We achieve competitive results with lower identity switches on this dataset as compared to the state of the art methods.