直感的操作に基づくものづくりに向けて

Questions as a factor in Web search strategy

Reaching out for object is often described as consisting of two components that are based on different visual information. Information about the object's position and orientation guides the hand to the object, while information about the object's shape and size determines how the fingers move relative to the thumb to grasp it. We propose an alternative description, which consists of determining suitable positions on the object - on the basis of its shape, surface roughness, and so on - and then moving one's thumb and fingers more or less independently to these positions. We modeled this description using a minimum-jerk approach, whereby the finger and thumb approach their respective target positions approximately orthogonally to the surface. Our model predicts how experimental variable such as object size, movement speed, fragility, and required accuracy will influence the timing and size of the maximum aperture of the hand. An extensive review of experimental studies on grasping showed that the predicted influences correspond to human behavior.

The development of prehension was studied in babies aged between 6 and 20 months in a partial replication of the classic study by Halverson (1931). In our study the objects to be grasped were spheres and cubes carefully graded to allow comparisons of grips with object shape and size controlled. The trend noted by Halverson, for power grips to decrease and precision grips to increase, was replicated so that by the second year precision grasping is the dominant mode of prehension. However, the transition to precision grasping appears to occur by selection from a developmentally prior mixture of power and precision grips. In comparing spheres and cubes object size was the most important factor influencing grip selection but object shape also had a lesser influence. Object size may have acted as a scaling factor for grip selection. The findings are discussed in relation to a selectionist approach to the development of prehension.

In this study, we mainly investigated the visual selectivity of hand-manipulation-related neurons in the anterior intraparietal area (area AIP) while the animal was grasping or fixating on three-dimensional (3D) objects of different geometric shapes, sizes, and orientations. We studied the activity of 132 task-related neurons during the hand-manipulation tasks in the light and in the dark, as well as during object fixation. Seventy-seven percent (101/132) of the hand-manipulation-related neurons were visually responsive, showing either lesser activity during manipulation in the dark than during that in the light (visual-motor neurons) or no activation in the dark (visual-dominant neurons). Of these visually responsive neurons, more than half (n = 66) responded during the object-fixation task (object-type). Among these, 55 were tested for their shape selectivity during the object-fixation task, and many (n = 25) were highly selective, preferring one particular shape of the six different shapes presented (ring, cube, cylinder, cone, sphere, and square plate). For 28 moderately selective object-type neurons, we performed multidimensional scaling (MDS) to examine how the neurons encode the similarity of objects. The results suggest that some moderately selective neurons responded preferentially to common geometric features shared by similar objects (flat, round, elongated, etc.). Moderately selective nonobject-type visually responsive neurons, which did not respond during object fixation, were found by MDS to be more closely related to the handgrip than to the object shape. We found a similar selectivity for handgrip in motor-dominant neurons that did not show any visual response. With regard to the size of the objects, 16 of 26 object-type neurons tested were selective for both size and shape, whereas 9 object-type neurons were selective for shape but not for size. Seven of 12 nonobject-type and all (8/8) of the motor-dominant neurons examined were selective for size, and almost all of them were also selective for objects. Many hand-manipulation-related neurons that preferred the plate and/or ring were selective for the orientation of the objects (17/20). These results suggest that the visual responses of object-type neurons represent the shape, size, and/or orientation of 3D objects, whereas those of the nonobject-type neurons probably represent the shape of the handgrip, grip size, or hand-orientation. The activity of motor-dominant neurons was also, in part, likely to represent these parameters of hand movement. This suggests that the dorsal visual pathway is concerned with the aspect of form, orientation, and/or size perception that is relevant for the visual control of movements.

For stereoscopic photography or telepresence, orthostereoscopy occurs when the perceived size, shape, and relative position of objects in the three-dimensional scene being viewed match those of the physical objects in front of the camera. In virtual reality, the simulated scene has no physical counterpart, so orthostereoscopy must be defined in this case as constancy, as the head moves around, of the perceived size, shape, and relative positions of the simulated objects. Achieving this constancy requires that the computational model used to generate the graphics matches the physical geometry of the head-mounted display being used. This geometry includes the optics used to image the displays and the placement of the displays with respect to the eyes. The model may fail to match the geometry because model parameters are difficult to measure accurately, or because the model itself is in error. Two common modeling errors are ignoring the distortion caused by the optics and ignoring the variation in interpupillary distance across different users. A computational model for the geometry of a head-mounted display is presented, and the parameters of this model for the VPL EyePhone are calculated.

A Real-World Size Organization of Object Responses in Occipitotemporal Cortex

Image segmentation consists of dividing the image into regions in order to simplify the visualization and the identification of objects. The segmentation process can be build in several different ways, for example: segmentation using the shape of the objects, intensity in the gray level, histogram of colors, etc. This wide range of techniques exists because different situations generate specific problems and, therefore, the criteria for segmenting and identifying objects vary according to the situation presented. Within the segmentation universe, the techniques that use gray levels can be included in two main categories: (i) techniques that use the detection of contours and shapes and (ii) techniques that use hierarchical structures. In this work, we propose the use of the hierarchical Watershed transform as segmentation technique and the use of Tree of Critical Lakes as a representative structure. Additionally, we use the size and shape of objects. In more detail, we work with a size range and adopt the elliptical curvature metric to produce the final result, which is obtained through the weighted average between the size score and the elliptic curvature score. We developed three algorithms to obtain better results. The first algorithm, called Algen, consists of a genetic algorithm that improves the results during the segmentation. The second algorithm, called Algal, performs the segmentation process repeatedly, changing the input values (within the allowed range) semi-randomly. The third algorithm, called Almod, is executed in conjunction with Algen and Algal algorithms. Almod main function is to classifier all results and it is executed at the end of each segmentation. The processes and algorithms developed in this work had as main focus the segmentation of cell images, and all tests of optimization and adaptation of the genetic algorithm were performed on the referred type of image.

Stereoscopic shape discrimination is well preserved across changes in object size

Previous work has demonstrated that incorrect white balance (WB) in the camera image signal processing pipeline has a negative impact on the performance of deep neural networks (DNNs) in high-level vision tasks, and traditional image augmentation approaches are not well suited for modeling WB errors. However, it is still unclear when this impact will occur for which kinds of images and objects. In this paper, we manually labeled 2304 images from the RECommended dataset and NUS dataset and discovered that the effect of WB on DNNs is greatly associated with object size and occlusion level among objects. In images with incorrect WB, small objects and objects with heavily occluded backgrounds are the main factors resulting in the bad performance of DNNs, indicating that the effect of WB is clearly associated with the shape of objects. Our findings may support that the functional role of some neurons in the visual cortex (e.g., V1 or V4 areas) realizing color constancy (CC) and encoding object attributes such as color and shape dependently is to contribute to high-level vision. Furthermore, based on this scientific finding, we proposed a novel augmentation strategy to address the negative impact of incorrect WB by expanding the training datasets in both color transformation and synthetic occlusion. We compared our proposed strategy with the current augmentation strategies and showed that our approach clearly improves the performance of DNNs in detection and segmentation tasks with small objects and objects with heavily occluded backgrounds.

Detection of vessels from space-based synthetic aperture radar (SAR) data is an important area of research with many applications, including fisheries monitoring, counter-piracy, and maritime border security. The detection of vessels on the ocean surface in SAR imagery requires that the vessel has sufficiently high radar cross section (RCS). In general, the RCS of an object is a function of the object's material, size, and shape, as well as RADAR parameters such as center frequency. Even, two objects of the same size may have different RCSs based on construction materials (i.e., wood versus metal). In Ghana, as in much of the Gulf of Guinea, wooden canoes 6-25 m in length represent a significant percentage of maritime traffic. These canoes are not easy to detect and track in coastal RADAR, nor are they easily detected in SAR imagery. These vessels may represent a significant risk to maritime safety and security. Here, we describe one possible solution for the problem described-above based on inexpensive, versatile corner reflectors with high RCS. Specifically, we describe the design and construction of high RCS corner reflectors and results from a series of experiments in which corner reflectors were installed on wooden canoes. During the experiments, canoes were deployed to specific locations off the coast of Ghana at specific times, corresponding to the acquisition of space-based SAR imagery. We present results from these experiments, which indicate that wooden canoes with these corner reflectors can be detected in space-based SAR imagery.

Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis

Anti-collision systems have been developed for use with powered wheelchairs in order to enable people with cognitive or physical impairments to safely operate a powered wheelchair. Anti-collision systems consist of sensors that have the ability to detect objects near the wheelchair and a computer that can stop the chair if a collision is determined to be likely. This investigation considered the suitability of using ultrasound sensors in such a system when encountering objects typically found within a home or a long-term care facility. An ultrasound sensor's ability to detect an object was dependent on the object's size, shape, specularity, reflectivity, and sound absorption characteristics. Ultrasound sensors, by themselves, were found to be unsuitable for anti-collision systems due to an inability to detect objects commonly encountered in the target environment (the home or long-term care facility) without increasing the complexity of the system to such a degree that it would be prohibitive to deploy this technology to the public.

This study presents a novel capillary microgripper for manipulating micrometer-sized objects directly within aqueous environments. The system features dynamic, vision-based feedback control of a non-volatile silicone oil droplet volume, enabling precise adjustment of the capillary bridge force for the adaptable capture of varying object sizes. This approach ensures extended working time and stable operation in water, mitigating the issues associated with evaporation common in other systems. COMSOL Multiphysics simulations analyzed capillary bridge formation. Experimental validation demonstrated successful different object shapes and sizes capture in an aqueous environment and further explored active release strategies necessary due to the non-volatile fluid, confirming the system potential for robust underwater micromanipulation.

Three-dimensional finger joint angles by hand posture and object properties

The design parameters and principles for the synthesis of polymeric microscopic objects using a method that combines photolithography and liquid crystal (LC) molecular templates have been demonstrated. Specifically, mixtures of a reactive mesogen (RM257) and nonreactive LC (E7) were polymerized using UV light and a photomask. We used photomasks with circular, triangular, rectangular, square, star-shaped, and heart-shaped features to provide initial shapes to the objects. Then, the unreacted parts were extracted and the polymeric objects were allowed to shrink anisotropically as defined by the ordering symmetry of the LC mixture. The initial configuration of the LC mixtures played a critical role in determining the final shapes of the polymeric objects formed after shrinking, which resulted in chiral twisting and bending, leading to more than 20 different shapes. We found that the pitch size of the bulk chiral twisted objects depends linearly on the angle of chiral twist of the LCs, whereas it was independent of their thickness and length ranging from 1.5 to 160 μm and 100 μm to 2.45 cm, respectively. The shapes of the polymeric objects synthesized from LC films with bent LC ordering, however, were critically dependent on the thickness of the objects due to the interplay between the elastic energy and surface anchoring of the LCs. The critical role of LC elasticity was observed for thicknesses below 20 μm, above which surface anchoring was dominant in determining the shapes. Overall, the proposed method was shown to provide a precise control over the three-dimensional architectures of the objects with size range that covers the micro and macro scales, which would find use in fields ranging from emulsion stabilization and catalysis to micromachines and artificial muscles.