Perception and characterization of materials using signal processing techniques

Femmam, Smain; M'Sirdi, Kouider; Ouahabi, Abdeldjalil

doi:10.1109/19.963184

Cited by 21 publications

(19 citation statements)

References 11 publications

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“…In the field of image fusion, the wavelet transform-based method, which was initially developed for signal processing [60], is widely used as high-pass filtering. The detailed image is the result of high contrast corresponding to high values in the frequency domain.…”

Section: Wavelet Transformation Based Methodsmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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Section: Wavelet Transformation Based Methodsmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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“…Femmam et al (Femmam et al, 2001) and Krotkov et al present an approach for characterizing the material of the object based on its internal friction. Here they use the acoustic information acquired from striking the object.…”

Section: Related Workmentioning

confidence: 99%

Visual Estimation of Object Density Distribution through Observation of its Impulse Response

Mkhitaryan

Burschka

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:In this paper we introduce a novel vision based approach for estimating physical properties of an object such as its center of mass and mass distribution. Passive observation only allows to approximate the center of mass with the centroid of the object. This special case is only true for objects that consist of one material and have unified mass distribution. We introduce an active interaction technique with the object derived from the analogon to system identification with impulse functions. We treat the object as a black box and estimate its internal structure by analyzing the response of the object to external impulses. The impulses are realized by striking the object at points computed based on its external geometry. We determine the center of mass from the profile of the observed angular motion of the object that is captured by a high frame-rate camera. We use the motion profiles from multiple strikes to compute the mass distribution. Knowledge of these properties of the object leads to more energy efficient and stable object manipulation. As we show in our real world experiments, our approach is able to estimate the intrinsic layered density structure of an object.

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“…Moreover, these agents have diverse types and behaviors, which further increases the environment perception complexity. To provide a safe autonomous driving service, the system should be able to detect the different road agents and predict their future paths in order to have a complete environment perception [6] and make the right navigation decision [7][8][9]. Therefore, obstacle detection is considered as a crucial task for autonomous driving in complex urban traffic.…”

Section: Introductionmentioning

confidence: 99%

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

et al. 2020

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A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

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Perception and characterization of materials using signal processing techniques

Cited by 21 publications

References 11 publications

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Visual Estimation of Object Density Distribution through Observation of its Impulse Response

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

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