Parametrized shape models for clothing

Miller, Stephen D.; Fritz, Mario; Darrell, Trevor; Abbeel, Pieter

doi:10.1109/icra.2011.5980453

Cited by 84 publications

(64 citation statements)

References 25 publications

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“…Figure 1 gives a high-level flow chart of these various steps. In the past, several bodies of work have attempted at solving the tasks of handling [1][2][3][4][5][6][7][8], separating/isolating [8][9][10][11][12], classifying [6,9,[11][12][13][14][15], unfolding/flattening [14,16], and folding [17] clothes. Figure 1 gives a flow chart of the various areas.…”

Section: Introductionmentioning

confidence: 99%

Classification of Clothing Using Midlevel Layers

2013

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We present a multilayer approach to classify articles of clothing within a pile of laundry. The classification features are composed of color, texture, shape, and edge information from 2D and 3D data within a local and global perspective. The contribution of this paper is a novel approach of classification termed L-M-H, more specifically LC-S-H for clothing classification. The multilayer approach compartmentalizes the problem into a high (H) layer, multiple midlevel (characteristics (C), selection masks (S)) layers, and a low (L) layer. This approach produces "local" solutions to solve the global classification problem. Experiments demonstrate the ability of the system to efficiently classify each article of clothing into one of seven categories (pants, shorts, shirts, socks, dresses, cloths, or jackets). The results presented in this paper show that, on average, the classification rates improve by +27.47% for three categories , +17.90% for four categories, and +10.35% for seven categories over the baseline system, using SVMs (Chang and Lin, 2001).

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Section: Introductionmentioning

confidence: 99%

Classification of Clothing Using Midlevel Layers

2013

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“…The proposed systems are able to pick up a piece of laundry and manipulate it until a desired configuration is reached. Other approaches are more focused on the perception capabilities: Miller et al [8] proposed a method based on parametrized shape models for estimating the pose of a crudely spread cloth item; in [9] the system is extended and combined with manipulation tools to complete the task of folding a garment with an open-loop sequence of movements.…”

Section: Garment Manipulationmentioning

confidence: 99%

A 3D descriptor to detect task-oriented grasping points in clothing

Ramisa

Alenyà

Moreno-Noguer

et al. 2016

Pattern Recognition

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Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor.In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.

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“…Almost all of the studies above use prohibitively time-consuming methods (requiring several tens of seconds for a single estimation [13]). For practical application, the efficiency of state estimation must be markedly improved.…”

Section: Clothing-state Estimationmentioning

confidence: 99%

Bottom Dressing by a Dual-Arm Robot Using a Clothing State Estimation Based on Dynamic Shape Changes

Yamazaki

Oya²,

Nagahama³

et al. 2016

International Journal of Advanced Robotic Systems

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This paper describes an autonomous robot's method of dressing a subject in clothing. Our target task is to dress a person in the sitting pose. We especially focus on the action whereby a robot automatically pulls a pair of trousers up the subject's legs, an action frequently needed in dressing assistance. To avoid injuring the subject's legs, the robot should be able to recognize the state of the manipulated clothing. Therefore, while handling the clothing, the robot is supplied with both visual and tactile sensory information. A dressing failure is detected by the visual sensing of the behaviour of optical flows extracted from the clothing's movements. The effectiveness of the proposed approach is implemented and validated in a life-sized humanoid robot.

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Parametrized shape models for clothing

Cited by 84 publications

References 25 publications

Classification of Clothing Using Midlevel Layers

Classification of Clothing Using Midlevel Layers

A 3D descriptor to detect task-oriented grasping points in clothing

Bottom Dressing by a Dual-Arm Robot Using a Clothing State Estimation Based on Dynamic Shape Changes

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