ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Brahmbhatt, Samarth; Ham, Cusuh; Kemp, Charles C.; Hays, James

doi:10.1109/cvpr.2019.00891

Cited by 164 publications

(110 citation statements)

References 52 publications

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“…To confirm that the proposed algorithm is applicable to various environments, additional experiments were performed using the existing thermal image open database. Although there are many different thermal image open databases available [2], [44]- [54], there are few open databases with thermal reflections in thermal imaging. Therefore, in this study, comparative experiments were performed using a thermal soccer dataset [44], an open database with thermal reflection, as shown in Figure 16.…”

Section: ) Testing Results With An Open Databasementioning

confidence: 99%

A Study on the Elimination of Thermal Reflections

et al. 2019

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Recently, thermal cameras have been used in various surveillance and monitoring systems. In particular, in camera-based surveillance systems, algorithms are being developed for detecting and recognizing objects from images acquired in dark environments. However, it is difficult to detect and recognize an object due to the thermal reflections generated in the image obtained from a thermal camera. For example, thermal reflection often occurs on a structure or the floor near an object, similar to shadows or mirror reflections. In this case, the object and the areas of thermal reflection overlap or are connected to each other and are difficult to separate. Thermal reflection also occurs on nearby walls, which can be detected as artifacts when an object is not associated with this phenomenon. In addition, the size and pixel value of the thermal reflection area vary greatly depending on the material of the area and the environmental temperature. In this case, the patterns and pixel values of the thermal reflection and the object are similar to each other and difficult to differentiate. These problems reduce the accuracy of object detection and recognition methods. In addition, no studies have been conducted on the elimination of thermal reflection of objects under different environmental conditions. Therefore, to address these challenges, we propose a method of detecting reflections in thermal images based on deep learning and their elimination via post-processing. Experiments using a self-collected database (Dongguk thermal image database (DTh-DB), Dongguk items and vehicles database (DI&V-DB)) and an open database showed that the performance of the proposed method is superior compared to that of other state-of-the-art approaches. INDEX TERMS Thermal reflection, thermal imaging, deep learning, thermal reflection detection, thermal reflection removal.

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Section: ) Testing Results With An Open Databasementioning

confidence: 99%

A Study on the Elimination of Thermal Reflections

et al. 2019

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“…Additional experiments were conducted using the existing thermal image open database to check whether the proposed method can be applied in other types of environments. There are several existing open thermal image databases [59]- [69]. However, there are few open databases having thermal images with thermal reflection.…”

Section: ) Testing Results Using Open Databasementioning

confidence: 99%

Region-Based Removal of Thermal Reflection Using Pruned Fully Convolutional Network

et al. 2020

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In general, an image obtained from a thermal camera often has a mirror reflection or shadow reflected off the ground around an object, which is referred to as thermal reflection. Sometimes the thermal reflections are connected to their objects in images, which makes it difficult to detect or recognize the object only. Thermal reflections sometimes occur on the wall near an object and are detected as another object when they are not connected to the object. Furthermore, the size of thermal reflection and pixel value significantly vary with the medium of the reflected range and the surrounding temperature. In these cases, the patterns and pixel values of thermal reflection and the object become similar and difficult to distinguish. However, there are insufficient studies on removing the thermal reflection of various kinds of objects in diverse environments. Therefore, in this paper, we propose a pruned fully convolutional network (PFCN)-based method for removing the thermal reflection of an object using the surrounding information when image transformation is performed only within the region of an object. When experiments were conducted using self-collected databases (Dongguk thermal image database (DTh-DB) and Dongguk items & vehicles database (DI&V-DB)) and open databases, the method proposed herein exhibited more outstanding performance in removing thermal reflection when compared with the state-of-the-art methods. INDEX TERMS Thermal image, image transform, thermal reflection removal, pruned fully convolutional network.

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“…The concept of affordances applies mostly to objects and it is typically posed as a semantic segmentation task, in which the pixels of an input image are classified based on their affordance label. Recent works, address this problem using deep learning [36,53,17,6,59].…”

Section: Related Workmentioning

confidence: 99%