Local Object Shape Research Articles

Softness and weight from shape: Material properties inferred from local shape features.

Object shape is an important cue to material identity and for the estimation of material properties. Shape features can affect material perception at different levels: at a microscale (surface roughness), mesoscale (textures and local object shape), or megascale (global object shape) level. Examples for local shape features include ripples in drapery, clots in viscous liquids, or spiraling creases in twisted objects. Here, we set out to test the role of such shape features on judgments of material properties softness and weight. For this, we created a large number of novel stimuli with varying surface shape features. We show that those features have distinct effects on softness and weight ratings depending on their type, as well as amplitude and frequency, for example, increasing numbers and pointedness of spikes makes objects appear harder and heavier. By also asking participants to name familiar objects, materials, and transformations they associate with our stimuli, we can show that softness and weight judgments do not merely follow from semantic associations between particular stimuli and real-world object shapes. Rather, softness and weight are estimated from surface shape, presumably based on learned heuristics about the relationship between a particular expression of surface features and material properties. In line with this, we show that correlations between perceived softness or weight and surface curvature vary depending on the type of surface feature. We conclude that local shape features have to be considered when testing the effects of shape on the perception of material properties such as softness and weight.

An Adaptive Image Steganography Method Based on Histogram of Oriented Gradient and PVD-LSB Techniques

Pixel value differencing (PVD) and least significant bit substitution (LSB) are two widely used schemes in image steganography. These two methods do not consider different content in a cover image for hiding the secret data. The content of most digital images has different edge directions in each pixel, and the local object shape or appearance is mostly characterized by the distribution of its intensity gradients or edge directions. Exploiting these characteristics for embedding various secret information in different edge directions will eliminate sequential embedding and improve robustness. Thus, a histogram of oriented gradient (HOG) algorithm is proposed to find the dominant edge direction for each $2\times 2$ block of cover images. Blocks of interest (BOIs) are determined adaptively based on the gradient magnitude and angle of the cover image. Then, the PVD algorithm is used to hide secret data in the dominant edge direction, while the LSB substitution is utilized in the other two remaining pixels. Extensive experiments using various standard images reveal that the proposed scheme provides high embedding capacity and better visual quality compared with several other PVD- and LSB-based methods. Moreover, it resists various steganalysis techniques, such as pixel difference histogram and RS analysis.

Planning High-Quality Grasps Using Mean Curvature Object Skeletons

In this letter, we present a grasp planner that integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identify object regions which are suitable for grasping. We show how this information can be used to derive different grasping strategies, which also allows to distinguish between precision and power grasps. Second, the local surface structure is investigated to construct feasible and robust grasping poses by aligning the hand according to the local object shape. We apply the approach to a wide variety of object models of the KIT and the YCB real-world object model databases and evaluate it with several robotic hands. The results show that the skeleton-based grasp planner is capable to generate high-quality grasps in an efficient manner. In addition, we evaluate how robust the planned grasps are against hand positioning errors as they occur in real-world applications due to perception and actuation inaccuracies. The evaluation shows that the majority of the generated grasps are of high quality since they can be successfully applied even when the hand is not exactly positioned.

Recognition of vision-based activities of daily living using linear predictive coding of histogram of directional derivative

In this paper, we have introduced a novel approach for recognition of activities of daily living (ADL). These activities are the ones that the human beings perform in daily life. At the object level, we used computational color model for efficient object segmentation and tracking to handle dynamic background change in indoor environment. To make it computationally efficient, cosine of the angle between the expected image color vector and current image color vector is used. At feature level, we have presented a linear predictive coding of histogram of directional derivative as a spatio-temporal descriptor. Our proposed descriptor describes the local object shape and appearance within cuboids effectively and distinctively. A multiclass support vector machine has been used to classify the human activities. The proposed framework for recognition of indoor human activity has been extensively validated on the benchmark of ADL datasets, with a focus that this methodology is robust and attains more precise human activity recognition rate as compared to current methodologies available.

Robust and fast pedestrian detection method for far-infrared automotive driving assistance systems

Despite considerable effort has been contributed to night-time pedestrian detection for automotive driving assistance systems recent years, robust and real-time pedestrian detection is by no means a trivial task and is still underway due to the moving cameras, uncontrolled outdoor environments, wide range of possible pedestrian presentations and the stringent performance criteria for automotive applications. This paper presents an alternative night-time pedestrian detection method using monocular far-infrared (FIR) camera, which includes two modules (regions of interest (ROIs) generation and pedestrian recognition) in a cascade fashion. Pixel-gradient oriented vertical projection is first proposed to estimate the vertical image stripes that might contain pedestrians, and then local thresholding image segmentation is adopted to generate ROIs more accurately within the estimated vertical stripes. A novel descriptor called PEWHOG (pyramid entropy weighted histograms of oriented gradients) is proposed to represent FIR pedestrians in recognition module. Specifically, PEWHOG is used to capture both the local object shape described by the entropy weighted distribution of oriented gradient histograms and its pyramid spatial layout. Then PEWHOG is fed to a three-branch structured classifier using support vector machines (SVM) with histogram intersection kernel (HIK). An off-line training procedure combining both the bootstrapping and early-stopping strategy is introduced to generate a more robust classifier by exploiting hard negative samples iteratively. Finally, multi-frame validation is utilized to suppress some transient false positives. Experimental results on FIR video sequences from various scenarios demonstrate that the presented method is effective and promising.

Complementing visual tracking of moving targets by fusion of tactile sensing

Robot control in uncertain and dynamic environments can be greatly improved using sensor-based control. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Vision can be used to estimate a 6-DOF pose of an object by model-based pose-estimation methods, but the estimate is typically not accurate along all degrees of freedom. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined together to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. We show that the fusion of tactile and visual measurements enables to estimate the pose of a moving target at high rate and accuracy. Making assumptions of the object shape and carefully modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. Experimental results show greatly improved pose estimates with the proposed sensor fusion.

LiveSync: Deformed Viewing Spheres for Knowledge-Based Navigation

Although real-time interactive volume rendering is available even for very large data sets, this visualization method is used quite rarely in the clinical practice. We suspect this is because it is very complicated and time consuming to adjust the parameters to achieve meaningful results. The clinician has to take care of the appropriate viewpoint, zooming, transfer function setup, clipping planes and other parameters. Because of this, most often only 2D slices of the data set are examined. Our work introduces LiveSync, a new concept to synchronize 2D slice views and volumetric views of medical data sets. Through intuitive picking actions on the slice, the users define the anatomical structures they are interested in. The 3D volumetric view is updated automatically with the goal that the users are provided with expressive result images. To achieve this live synchronization we use a minimal set of derived information without the need for segmented data sets or data-specific pre-computations. The components we consider are the picked point, slice view zoom, patient orientation, viewpoint history, local object shape and visibility. We introduce deformed viewing spheres which encode the viewpoint quality for the components. A combination of these deformed viewing spheres is used to estimate a good viewpoint. Our system provides the physician with synchronized views which help to gain deeper insight into the medical data with minimal user interaction.

Direct extraction of curvature-based metric shape from stereo by view-modulated receptive fields

Any computation of metric surface structure from horizontal disparities depends on the viewing geometry, and analysing this dependence allows us to narrow down the choice of viable schemes. For example, all depth-based or slant-based schemes (i.e. nearly all existing models) are found to be unrealistically sensitive to natural errors in vergence. Curvature-based schemes avoid these problems and require only moderate, more robust view-dependent corrections to yield local object shape, without any depth coding. This fits the fact that humans are strikingly insensitive to global depth but accurate in discriminating surface curvature. The latter also excludes coding only affine structure. In view of new adaptation results, our goal becomes to directly extract retinotopic fields of metric surface curvatures (i.e. avoiding intermediate disparity curvature). To find a robust neural realisation, we combine new exact analysis with basic neural and psychophysical constraints. Systematic, step-by-step 'design' leads to neural operators which employ a novel family of 'dynamic' receptive fields (RFs), tuned to specific (bi-)local disparity structure. The required RF family is dictated by the non-Euclidean geometry that we identify as inherent in cyclopean vision. The dynamic RF-subfield patterns are controlled via gain modulation by binocular vergence and version, and parameterised by a cell-specific tuning to slant. Our full characterisation of the neural operators invites a range of new neurophysiological tests. Regarding shape perception, the model inverts widely accepted interpretations: It predicts the various types of errors that have often been mistaken for evidence against metric shape extraction.

Computing curvature-adaptive surface triangulations of three-dimensional image data

An adaptive surface triangulation algorithm for binary 3D image data is presented. The proposed method is robust and efficient and yields a high-quality surface-mesh description. The main idea of the algorithm is based on selecting a subset of the object voxel surface elements as the vertices of the triangulation and to obtain a discrete approximation of the Voronoi graph on the object surface to generate its dual, the surface Delaunay triangulation. The presented approach incorporates local object shape into the early stages of the algorithm, yielding an elegant method for obtaining shape-adaptive triangular meshes. One of the main advantages of the presented method over previous approaches is that no intermediate surface representation other than the trivial voxel representation provided by the input data is necessary. The method is presented in detail and tested on synthetic as well as real images.

Shape‐adaptive 3‐D mesh simplification based on local optimality measurement

AbstractMesh simplification is the process of reducing the number of triangles in a mesh representation of object surface. For a given level of detail or error tolerance, the conventional mesh simplification algorithms maximize the edge length globally, without explicitly considering local object shape. In this paper, we present a shape‐adaptive mesh simplification algorithm that locally maximizes edge length, depending on local shape. The proposed algorithm achieves shape‐adaptive simplification by iteratively maximizing edges between vertices, based on comparison with the ‘optimal’ edge lengths derived from local directional curvatures for a given error tolerance. Edge‐based processing facilitates the local shape adaptation and preserves sharp features. Experimental results demonstrate the efficacy of the proposed algorithm, by showing good visual quality and extremely small approximation error. Copyright © 2003 John Wiley & Sons, Ltd.

Binocular motion tracking by gaze fixation control and three-dimensional shape reconstruction

It is an easy task for the human visual system to gaze continuously at an object moving in three-dimensional (3-D) space. While tracking the object, human vision seems able to comprehend its 3-D shape with binocular vision. We conjecture that, in the human visual system, the function of comprehending the 3-D shape is essential for robust tracking of a moving object. In order to examine this conjecture, we constructed an experimental system of binocular vision for motion tracking. The system is composed of a pair of active pan-tilt cameras and a robot arm. The cameras are for simulating the two eyes of a human while the robot arm is for simulating the motion of the human body below the neck. The two active cameras are controlled so as to fix their gaze at a particular point on an object surface. The shape of the object surface around the point is reconstructed in real-time from the two images taken by the cameras based on the differences in the image brightness. If the two cameras successfully gaze at a single point on the object surface, it is possible to reconstruct the local object shape in real-time. At the same time, the reconstructed shape is used for keeping a fixation point on the object surface for gazing, which enables robust tracking of the object. Thus these two processes, reconstruction of the 3-D shape and maintaining the fixation point, must be mutually connected and form one closed loop. We demonstrate the effectiveness of this framework for visual tracking through several experiments.

Compliant motion based active sensing by robotic fingers

When a robot approaches an unknown object and grasps it stably, it is important to know the local shape of the object to be grasped. Assuming that the position of object is roughly specified and the object is convex, this paper discusses the issue on how to detect the local shape of object without using any fingertip tactile sensor. A link system having one compliant joint and one position-controlled joint is capable of changing its posture with maintaining contact between the link and the object, when imparting a small angular displacement continuously to the position controlled joint This motion planning is so simple but still guarantees to make contact between the link and the object which is essential for detecting the local shape of object. We show how to construct the local shape of object from the obtained link trajectories. Finally we show some experimental results to confirm the validation of the proposed approach.