Learning object-specific DAGs for multi-label material recognition

Xie, Xiaohua; Yang, Lei; Zheng, Wei‐Shi

doi:10.1016/j.cviu.2015.11.018

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Local features can be categorized into hand-crafted features-such as Markovian, illuminationinvariant texture features [21]; filter banks [22]; and wavelet filters [23]-as well as automatically extracted features, which include CNN feature vectors [24] and D-CNN models applied to texture datasets [2]. On the other hand, global featurebased methods predominantly utilize CNN or Vision Transformer models, trained on dataset formats that include the context of objects [20,25,26,27,28] and scenes [29]. These global feature methods have been shown to outperform their local counterparts, achieving test accuracies of 85.6% on the MINC dataset [20] and 87% on the Trash image dataset, making them more suitable for real-world applications.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Surface multi-spectral reflectivity and texture material recognition in non-constrained environments

Dhahri,

Amamou,

Faghihi

et al. 2024

Preprint

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Material recognition is the process of distinguishing various materials based on their inherent physical properties. It plays a pivotal role in numerous applications, including manufacturing, recycling, and robotic handling. Conventional recognition methods predominantly employ sensor-and vision-based approaches. However, these methods often face challenges such as the similarity and variability in material appearance, environmental conditions, and geometric constraints. In this research, we introduce a multimodal, vision-based, attention-driven model for material recognition. Contrary to preceding texture-based and multi-spectral based methods, our approach harnesses both the texture and light reflection distribution characteristics intrinsic to material surfaces. The proposed method features a collocated system that combines depth, RGB, and near-infrared (Near-IR) cameras, along with infrared laser projector. This specific setup was selected to capture reflection distribution and texture across the visible-near-infrared spectrum. Subsequently, the data captured by this setup were processed by a recognition model within a fusion framework. Our results outperform previous methods in terms of accuracy when additional modalities (Depth, Near-IR, laser projectors) are available, while also exhibiting equivalent performance to top RGB-based models when solely reliant on RGB data. Thus, proving the complementarity of the added modalities with visible information.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Surface multi-spectral reflectivity and texture material recognition in non-constrained environments

Dhahri,

Amamou,

Faghihi

et al. 2024

Preprint

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show abstract

“…Material recognition is a challenging problem by itself and many methods have been proposed for solving this problem [18, 35, 36]. Bell et al [18] presented a large scale dataset of materials in the wild and used learning techniques for material recognition.…”

Section: Discussion On Selecting ωmentioning

confidence: 99%

Intrinsic Image Decomposition via Structure-Preserving Image Smoothing and Material Recognition

2016

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Decoupling shading and reflectance from complex scene-images is a long-standing problem in computer vision. We introduce a framework for decomposing an image into the product of an illumination component and a reflectance component. Due to the ill-posed nature of the problem, prior information on shading and reflectance is mandatory. The proposed method adopts the premise that pixels in a region with similar chromaticity values should have the same reflectance. This assumption was used to minimize the l2 norm of the local per-pixel reflectance gradients to extract the shading and reflectance components. To obtain smooth chromatic regions, texture was treated in a new style. Texture was removed in the first step of the algorithm and the smooth image was processed for intrinsic decomposition. In the final step, texture details were added to the intrinsic components based on the material of each pixel. In addition, user-assistance was used to further refine the results. The qualitative and quantitative evaluation on the MIT intrinsic dataset indicated that the quality of intrinsic image decomposition was improved in comparison with previous methods.

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“…If the material region is smaller than the grid division, a lot of information of the background is processed as a material resulting in a non-accurate description. In [ 27 , 28 ], the authors proposed two methods able to identify multiple materials in object surfaces without the need of segmentation. They first recognized the object class and then used correlations of material labels for such object.…”

Section: Related Researchmentioning

confidence: 99%

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

García-Olalla

Alegre

Fernández-Robles

et al. 2018

Sensors

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Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.

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Learning object-specific DAGs for multi-label material recognition

Cited by 6 publications

References 38 publications

WITHDRAWN: Surface multi-spectral reflectivity and texture material recognition in non-constrained environments

WITHDRAWN: Surface multi-spectral reflectivity and texture material recognition in non-constrained environments

Intrinsic Image Decomposition via Structure-Preserving Image Smoothing and Material Recognition

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

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