Evaluation of the relationships between saliency maps and keypoints

The saliency map is proposed by Itti et al., to represent the conspicuity or saliency in the visual field and to guide the selection of attended locations based on the spatial distribution of saliency, which works as the trigger of bottom-up attention. If a certain location in the visual field is sufficiently different from its surrounding, we naturally pay attention to the characteristic of visual scene. In the research of computer vision, image feature extraction methods such as Scale-Invariant Feature Transform (SIFT), Speed-Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoint (BRISK) etc., have been proposed to extract keypoints robust to size change or rotation of target objects. These feature extraction methods are inevitable techniques for image mosaicking and Visual SLAM (Simultaneous Localization and Mapping), on the other hand, have big influence to photographing condition change of luminance, defocusing and so on. However, the relation between human attention model, Saliency map, and feature extraction methods in computer vision is not well discussed. In this paper, we propose a new saliency map and discuss the stability of keypoints extraction and their locations using BRISK by comparing other saliency maps

Acoustic Impedance Measurement through the Modelling of Ultrasonic Wave Transmission

In food industry, shortage of workers is a serious problem. Automation of food handling is a critical nowadays. To alleviate the damage during food picking by robotic hand, we propose non-contact acoustic impedance estimation with ultrasonic wave. We have the assumption of the correlation between hardness and acoustic impedance, and, built up ultrasonic transmission model considering attenuation by reflection and absorption, then, made an experiment to estimate the impedance. As the result, we succeeded in detecting acoustic impedance without contact.The 2021 International Conference on Artificial Life and Robotics (ICAROB 2021), January 21-24, 2021, Higashi-Hiroshima (オンライン開催に変更

Reflection Coefficient Estimation through the Modelling of Ultrasonic Transmission

In food industry, shortage of workers is one of a serious problem. Automation of food handling is a critical isSsue nowadays. To alleviate the damage caused by food picking operation by robotic hand, we propose non-contact acoustic impedance estimation with ultrasonic wave, which should be preceded before the picking for optimization of grasp stiffness. We have the assumption of the correlation between hardness and acoustic impedance. The impedance is calculated by the product of sonic velocity and density of a medium. From the point of view, the harder the medium is, the larger the impedance should be. We built up ultrasonic transmission model considering attenuation by ultrasonic reflection and absorption, then, made an experiment to estimate internal reflection of samples with two overwrapped media of different acoustic impedance.The 2022 International Conference on Artificial Life and Robotics (ICAROB 2022), January 20-23, 2022, on line, Oita, Japa

Exercise on Environmental Monitoring and Control of Greenhouse by IoT Devices toward Smart Agriculture

As crops in greenhouses are widely distributed, IoT devices placed near the crops should be stand-alone and modular, and data from the devices are collected over the networks. Smart agriculture requires knowledge of a wide range of fields including electricity, information, and image processing. We have designed an AI and IoT technology exercise on environmental monitoring and control of a greenhouse where we have been preparing for grow up of tomatoes and other vegetables.The 2022 International Conference on Artificial Life and Robotics (ICAROB 2022), January 20-23, 2022, on line, Oita, Japa

Evaluation of the Relationships Between Saliency Maps and Keypoints

Saliency is a property of images that triggers bottom-up attention. For example, if a location in an image is sufficiently different from its surrounding and worthy of paying attention, such characteristic of image is saliency. From the point of view, the location of larger saliency is outstanding visually. On the other hand, As Image Feature extraction method, such as SIFT or SURF, robust feature matching has been realized under the existence of changing size or rotation of observed target. For the consequence, its advantage has been introduced into image stitching and Visual SRAM. However, the amount of image features is susceptive to changing photographing condition, such as luminance variety, defocus-ing etc. We assumed that feature extraction stability is large in salient region because of steep bright-ness gradient. We evaluated the relationship between saliency and feature extraction stability.The 2020 International Conference on Artificial Life and Robotics (ICAROB 2020), January 13-16, 2020, B-Con Plaza, Beppu, Oit

BASIC RESEARCH ON NAVIGATION OF AN OMNI-DIRECTIONAL MOBILE ROBOT

Human-beings, other animals and insects, etc. are able to reach a target place from a distant location, implement a collision avoidance and also detect landmarks from environments, mainly using

Evaluation of the Relationships between Saliency Maps and Keypoints

The saliency map is proposed by Itti et al., to represent the conspicuity or saliency in the visual field and to guide the selection of attended locations based on the spatial distribution of saliency, which works as the trigger of bottom-up attention. If a certain location in the visual field is sufficiently different from its surrounding, we naturally pay attention to the characteristic of visual scene. In the research of computer vision, image feature extraction methods such as Scale-Invariant Feature Transform (SIFT), Speed-Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoint (BRISK) etc., have been proposed to extract keypoints robust to size change or rotation of target objects. These feature extraction methods are inevitable techniques for image mosaicking and Visual SLAM (Simultaneous Localization and Mapping), on the other hand, have big influence to photographing condition change of luminance, defocusing and so on. However, the relation between human attention model, Saliency map, and feature extraction methods in computer vision is not well discussed. In this paper, we propose a new saliency map and discuss the stability of keypoints extraction and their locations using BRISK by comparing other saliency maps