Material classification with thermal imagery

Saponaro, Philip; Sorensen, Scott; Kolagunda, Abhishek; Kambhamettu, Chandra

doi:10.1109/cvpr.2015.7299096

Cited by 42 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[42,53,54,33]), detecting the pedestrian in adverse illumination conditions, occlusions and clutter background is still a challenging problem. Recently, many works in robot vision [4,49], facial expression recognition [9], material classification [41], and object detection [45,11,20,51] show that adopting a novel modality can improve the performance and offer competitive advantages over single sensor systems. Among the sensors, thermal camera is widely used in face recognition [3,44,27], human tracking [30,46] and action recognition [59,15] for its biometric robustness.…”

Section: Introductionmentioning

confidence: 99%

Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection

Zhang

Zhu²,

Chen

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

148

120

View full text Add to dashboard Cite

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way. Firstly, we design a Region Feature Alignment (RFA) module to capture the position shift and adaptively align the region features of the two modalities. Secondly, we present a new multimodal fusion method, which performs feature re-weighting to select more reliable features and suppress the useless ones. Besides, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns of different devices and system settings. Finally, since our method depends on a new kind of labelling: bounding boxes that match each modality, we manually relabel the KAIST dataset by locating bounding boxes in both modalities and building their relationships, providing a new KAIST-Paired Annotation. Extensive experimental validations on existing datasets are performed, demonstrating the effectiveness and robustness of the proposed method. Code and data are available at: https://github.com/luzhang16/AR-CNN .

show abstract

Section: Introductionmentioning

confidence: 99%

Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection

Zhang

Zhu²,

Chen

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

148

120

View full text Add to dashboard Cite

show abstract

“…Thermal imaging approaches, have traditionally, not been widely used to solve computer vision problems. Saponaro et al [43] estimate the material from the water permeation and heating/cooling process of the object. Miyazaki et al [44] resolve the ambiguity regarding polarization-based shape reconstruction using a thermal image.…”

Section: B Computational Thermal Imagingmentioning

confidence: 99%

Time-Resolved Far Infrared Light Transport Decomposition for Thermal Photometric Stereo

Tanaka

Ikeya

Takatani

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

We present a novel time-resolved light transport decomposition method using thermal imaging. Because the speed of heat propagation is much slower than the speed of light propagation, transient transport of far infrared light can be observed at a video frame rate. A key observation is that the thermal image looks similar to the visible light image in an appropriately controlled environment. This implies that conventional computer vision techniques can be straightforwardly applied to the thermal image. We show that the diffuse component in the thermal image can be separated and, therefore, the surface normals of objects can be estimated by the Lambertian photometric stereo. The effectiveness of our method is evaluated by conducting real-world experiments, and its applicability to black body, transparent, and translucent objects is shown.

show abstract

“…There is a small number of thermal imaging approaches to solve computer vision problems. Saponaro et al [39] estimate the material from the water permeation and heating/cooling process of the object. Miyazaki et al [23] resolve the ambiguity regarding polarization-based shape reconstruction using a thermal image.…”

Section: Related Workmentioning

confidence: 99%