Linear-time connected-component labeling based on sequential local operations

Suzuki, Kenji; Horiba, Isao; Sugie, Noboru

doi:10.1016/s1077-3142(02)00030-9

Cited by 382 publications

(206 citation statements)

References 27 publications

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“…To this end, we employ connected component analysis [21], well known from image processing, to extract all connected regions in the binarized tactile image and choose the largest one as the considered contact region R -neglecting all smaller regions as originating from noise or spurious contacts.…”

Section: Feature Extraction From Tactile Imagesmentioning

confidence: 99%

A Control Framework for Tactile Servoing

Li¹,

Schürmann²,

Haschke³

et al. 2013

Robotics: Science and Systems IX

117

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Abstract-The advent of sensor arrays providing tactile feedback with high spatial and temporal resolution asks for new control strategies to exploit this important and valuable sensory channel for grasping and manipulation tasks.In this paper, we introduce a control framework to realize a whole set of tactile servoing tasks, i.e. control tasks, which intend to realize a specific tactile interaction pattern. This includes such simple tasks like tracking a touched object, maintaining both contact location and contact force, as well as more elaborate tasks like tracking an object's pose or tactile object exploration.Exploiting methods known from image processing, we introduce robust feature extraction methods to estimate the 2D contact position, the contact force, and the orientation of an object edge being in contact to the sensor. The flexible control framework allows to adapt the PID-type controller to a large range of different tasks by specification of a projection matrix toggling certain control components on and off. We demonstrate and evaluate the capabilities of the proposed control framework in a series of experiments employing a 16×16 tactile sensor array attached to a Kuka LWR as a "large" fingertip.

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Section: Feature Extraction From Tactile Imagesmentioning

confidence: 99%

A Control Framework for Tactile Servoing

Li¹,

Schürmann²,

Haschke³

et al. 2013

Robotics: Science and Systems IX

117

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“…Recent publications by Suzuki et al [5] and Wu et al [11] give a nice description of the label equivalence procedure for the labeling of connected components in a binary image in the sequential case.…”

Section: Hoshen-kopelman or Label Equivalencementioning

confidence: 99%

“…Since the early 70s, numerous approaches for connected component labeling have been introduced [2,3,4,5]. Most of these approaches are suitable for sequential processing, but also some parallel algorithms have been developed [6,7].…”

Section: Introductionmentioning

confidence: 99%

Connected component labeling on a 2D grid using CUDA

Kalentev

Rai

Kemnitz

et al. 2011

Journal of Parallel and Distributed Computing

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Connected component labeling is an important but computationally expensive operation required in many fields of research. The goal in the present work is to label connected components on a 2D binary map. Two different iterative algorithms for doing this task are presented. The first algorithm (Row-Col Unify) is based upon the directional propagation labeling, whereas the second algorithm uses the label equivalence technique. Row-Col Unify algorithm uses a local array of references and the reduction technique intrinsically. The usage of shared memory extensively makes the code efficient. The label equivalence algorithm is an extended version of the one presented by Hawick et al. [1]. At the end the comparison depending on the performances of both of the algorithms is presented.

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“…It usually used a connected components labeling (CCL) algorithm. In papers by Suzuki, Wu et al [6][7][8], approaches and algorithms for CCL are categorized into a number of groups. Two pass algorithms show very high performance, but require large memory to store label equivalence.…”

Section: Introduction 1)mentioning

confidence: 99%

Real-Time Object Segmentation in Image Sequences

Kang¹,

Yoo²

2011

The KIPS Transactions:PartB

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This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

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Linear-time connected-component labeling based on sequential local operations

Cited by 382 publications

References 27 publications

A Control Framework for Tactile Servoing

A Control Framework for Tactile Servoing

Connected component labeling on a 2D grid using CUDA

Real-Time Object Segmentation in Image Sequences

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