Product of tracking experts for visual tracking of surgical tools

Kumar, Suren; Narayanan, Madusudanan Sathia; Singhal, Pankaj; Corso, Jason J.; Krovi, Venkat

doi:10.1109/coase.2013.6654037

Cited by 21 publications

(34 citation statements)

References 22 publications

(31 reference statements)

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“…In order to use this work in the non-robotic laparoscopic setting it would be necessary to use tool tracking models [18,8] with stereo video. It may be possible to detect the gripper state by extending one of these models.…”

Section: Discussionmentioning

confidence: 99%

An Improved Model for Segmentation and Recognition of Fine-Grained Activities with Application to Surgical Training Tasks

Lea

Hager

Vidal

2015

2015 IEEE Winter Conference on Applications of Computer Vision

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Automated segmentation and recognition of fine-grained activities is important for enabling new applications in industrial automation, human-robot collaboration, and surgical training. Many existing approaches to activity recognition assume that a video has already been segmented and perform classification using an abstract representation based on spatio-temporal features. While some approaches perform joint activity segmentation and recognition, they typically suffer from a poor modeling of the transitions between actions and a representation that does not incorporate contextual information about the scene. In this paper, we propose a model for action segmentation and recognition that improves upon existing work in two directions. First, we develop a variation of the Skip-Chain Conditional Random Field that captures long-range state transitions between actions by using higher-order temporal relationships. Second, we argue that in constrained environments, where the relevant set of objects is known, it is better to develop features using high-level object relationships that have semantic meaning instead of relying on abstract features. We apply our approach to a set of tasks common for training in robotic surgery: suturing, knot tying, and needle passing, and show that our method increases micro and macro accuracy by 18.46% and 44.13% relative to the state of the art on a widely used robotic surgery dataset.

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Section: Discussionmentioning

confidence: 99%

An Improved Model for Segmentation and Recognition of Fine-Grained Activities with Application to Surgical Training Tasks

Lea

Hager

Vidal

2015

2015 IEEE Winter Conference on Applications of Computer Vision

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“…Knowledge about this point restricts the search area for region seeds for color-based segmentation [52] and enables modeling of possible instrument motion [267]. Recently, a number of advanced and very sophisticated approaches for 3D instrument tracking and pose estimation have been proposed, e.g., training of appearance models of individual parts of an instrument (shaft, wrist and finger) using color and texture features [180], learning of fine-scaled natural features in the form of particular 3D landmarks on instrument tips using Randomized Trees [191], learning the shape of instruments using HOG descriptors and Latent SVM to probabilistically track them [107], and approaches to determine semantic attributes like "open/closed, stained with blood or not, state of cauterizing tools" etc. [108].…”

Section: Instrument Detection and Trackingmentioning

confidence: 99%

Content-based processing and analysis of endoscopic images and videos: A survey

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

108

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In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with realtime assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.

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“…The above formula indicates that two clusters are similar if they are spatially near (7)(8) and have got similar orientation (9). Since for each cluster we have its orientation, it is easy to extend the similarity also to the module, in analogous way of (5).…”

Section: Distance_x(c1c2) ≤ Max_dist_cluster_x (7) Distance_y(c1c2)mentioning

confidence: 99%

“…For what concerns region based, different approaches have been proposed: combined color based and region based particle filters, using multiple hypotheses for tracking objects [8], motion-based segmentation and a region-based Mean-shift tracking approach, fused by a Kalman filter, to extract object independent motion trajectory under uncontrolled environment [9], and so on. However the most popular and recent region based approaches is background subtraction [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%