Visual-hint Boundary to Segment Algorithm for Image Segmentation

Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition does not always follow this pattern. A new image segmentation method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is more consistent with human perceptions. VHBS abides by two visual hint rules based on human perceptions: (i) the global scale boundaries tend to be the real boundaries of the objects; (ii) two adjacent regions with quite different colors or textures tend to result in the real boundaries between them. It has been demonstrated by experiments that, compared with traditional image segmentation method, VHBS has better performance and also preserves higher computational efficiency.Comment: 45 page

Joint Optical Flow and Temporally Consistent Semantic Segmentation

The importance and demands of visual scene understanding have been steadily increasing along with the active development of autonomous systems. Consequently, there has been a large amount of research dedicated to semantic segmentation and dense motion estimation. In this paper, we propose a method for jointly estimating optical flow and temporally consistent semantic segmentation, which closely connects these two problem domains and leverages each other. Semantic segmentation provides information on plausible physical motion to its associated pixels, and accurate pixel-level temporal correspondences enhance the accuracy of semantic segmentation in the temporal domain. We demonstrate the benefits of our approach on the KITTI benchmark, where we observe performance gains for flow and segmentation. We achieve state-of-the-art optical flow results, and outperform all published algorithms by a large margin on challenging, but crucial dynamic objects.Comment: 14 pages, Accepted for CVRSUAD workshop at ECCV 201

Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

Up in the Air Over Taxing Frequent Flyer Benefits: The American, Canadian, and Australian Experiences

Vessel segmentation is an important prerequisite for many medical applications. While automatic vessel segmentation is an active field of research, interaction and visualization techniques for semi-automatic solutions have gotten far less attention. Nevertheless, since automatic techniques do not generally achieve perfect results, interaction is necessary. Especially for tasks that require an in-detail inspection or analysis of the shape of vascular structures precise segmentations are essential. However, in many cases these can only be generated by incorporating expert knowledge. In this paper we propose a visual vessel segmentation system that allows the user to interactively generate vessel segmentations. Therefore, we employ multiple linked views which allow to assess different aspects of the segmentation and depict its different quality metrics. Based on these quality metrics, the user is guided, can assess the segmentation quality in detail and modify the segmentation accordingly. One common modification is the editing of branches, for which we propose a semi-automatic sketch-based interaction metaphor. Additionally, the user can also influence the shape of the vessel wall or the centerline through sketching. To assess the value of our system we discuss feedback from medical experts and have performed a thorough evaluation

Eye movements and pupil dilation during event perception

Human observers segment ongoing activities into events that are reliable across observers [Newtson and Engquist 1976]. Segments can be small ("fine") or large ("coarse") with clusters of fine-grained segments relating hierarchically to coarse segments. Segmentation behaviour occurs even without instruction indicated by neural activity in the Medial Temporal complex (MT+)and Frontal Eye Field (FEF). Similar activation is observed during active segmentation [Zacks et al. 2001]. These two brain regions are known to be active during the processing of visual motion (MT+) and guiding saccadic eye movements (FEF). This, along with behavioural evidence [Zacks 2004], indicates that visual motion may play an important role in identifying events