Interactive object contour extraction for shape modeling

In this paper we present a semi-automatic segmentation approach suitable for extracting object contours as a precursor to 2D shape modeling. The approach is a modified and extended version of an existing state-of-the-art approach based on the concept of a Binary Partition Tree (BPT) [1]. The resulting segmentation tool facilitates quick and easy extraction of an object’s contour via a small amount of user interaction that is easy to perform, even in complicated scenes. Illustrative segmentation results are presented and the usefulness of the approach in generating object shape models is discussed

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Toward automated evaluation of interactive segmentation

We previously described a system for evaluating interactive segmentation by means of user experiments (McGuinness and O’Connor, 2010). This method, while effective, is time-consuming and labor-intensive. This paper aims to make evaluation more practicable by investigating if it is feasible to automate user interactions. To this end, we propose a general algorithm for driving the segmentation that uses the ground truth and current segmentation error to automatically simulate user interactions. We investigate four strategies for selecting which pixels will form the next interaction. The first of these is a simple, deterministic strategy; the remaining three strategies are probabilistic, and focus on more realistically approximating a real user. We evaluate four interactive segmentation algorithms using these strategies, and compare the results with our previous user experiment-based evaluation. The results show that automated evaluation is both feasible and useful

Video Data Visualization System: Semantic Classification And Personalization

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic

Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc

Optimization-based interactive segmentation interface for multiregion problems.

Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality

A multimedia package for patient understanding and rehabilitation of non-contact anterior cruciate ligament injuries

Non-contact anterior cruciate ligament (ACL) injury is one of the most common ligament injuries in the body. Many patients’ receive graft surgery to repair the damage, but have to undertake an extensive period of rehabilitation. However, non-compliance and lack of understanding of the injury, healing process and rehabilitation means patient’s return to activities before effective structural integrity of the graft has been reached. When clinicians educate the patient, to encourage compliance with treatment and rehabilitation, the only tools that are currently widely in use are static plastic models, line diagrams and pamphlets. As modern technology grows in use in anatomical education, we have developed a unique educational and training package for patient’s to use in gaining a better understanding of their injury and treatment plan. We have combined cadaveric dissections of the knee (and captured with high resolution digital images) with reconstructed 3D modules from the Visible Human dataset, computer generated animations, and images to produce a multimedia package, which can be used to educate the patient in their knee anatomy, the injury, the healing process and their rehabilitation, and how this links into key stages of improving graft integrity. It is hoped that this will improve patient compliance with their rehabilitation programme, and better long-term prognosis in returning to normal or near-normal activities. Feedback from healthcare professionals about this package has been positive and encouraging for its long-term use

TRECVid 2005 experiments at Dublin City University

In this paper we describe our experiments in the automatic and interactive search tasks and the BBC rushes pilot task of TRECVid 2005. Our approach this year is somewhat different than previous submissions in that we have implemented a multi-user search system using a DiamondTouch tabletop device from Mitsubishi Electric Research Labs (MERL).We developed two versions of oursystem one with emphasis on efficient completion of the search task (Físchlár-DT Efficiency) and the other with more emphasis on increasing awareness among searchers (Físchlár-DT Awareness). We supplemented these runs with a further two runs one for each of the two systems, in which we augmented the initial results with results from an automatic run. In addition to these interactive submissions we also submitted three fully automatic runs. We also took part in the BBC rushes pilot task where we indexed the video by semi-automatic segmentation of objects appearing in the video and our search/browsing system allows full keyframe and/or object-based searching. In the interactive search experiments we found that the awareness system outperformed the efficiency system. We also found that supplementing the interactive results with results of an automatic run improves both the Mean Average Precision and Recall values for both system variants. Our results suggest that providing awareness cues in a collaborative search setting improves retrieval performance. We also learned that multi-user searching is a viable alternative to the traditional single searcher paradigm, provided the system is designed to effectively support collaboration

Deep Extreme Cut: From Extreme Points to Object Segmentation

This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do so by adding an extra channel to the image in the input of a convolutional neural network (CNN), which contains a Gaussian centered in each of the extreme points. The CNN learns to transform this information into a segmentation of an object that matches those extreme points. We demonstrate the usefulness of this approach for guided segmentation (grabcut-style), interactive segmentation, video object segmentation, and dense segmentation annotation. We show that we obtain the most precise results to date, also with less user input, in an extensive and varied selection of benchmarks and datasets. All our models and code are publicly available on http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/.Comment: CVPR 2018 camera ready. Project webpage and code: http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr