CATCHING BERKELEY'S SHADOW

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.

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.

Berkeley's Theory of Vision: A Critical Examination of Bishop Berkeley's Essay Towards a New Theory of Vision.

Event detection requires interpretation of the semantically object actions. To achieve this task, the gap between the numerical features of objects and the symbolic description of the meaningful activities needs to be bridged. We develop an ambiguity detection framework that has two significant advantages over past work. First, we introduce a fusion method for a set of time-wise and object-wise features including not only the trajectory coordinates but also the histograms and HMM based representations of object's speed, orientation, location, size, and aspect ratio. This fusion method enable detection of events that cannot be detected with the existing trajectory features reported so far. Second, we improve existing spectral clustering algorithms by automatically estimating the optimal number of clusters. Furthermore, we determine the conformity of the objects within the given data space. We compute a separate HMM for each object using a time-series that is composed of the mixture of its features. Then, we construct an aggregated affinity matrix from the pair-wise similarity scores of objects using the HMM's. We apply eigenvector decomposition and obtain object clusters. We show that the number of eigenvectors used in the decomposition is proportional to the optimal number of clusters. We examine the affinity matrix to determine the deviance of objects from common assemblages within the space. Our simulations reveal that the proposed detection methods accurately discover both usual and unusual events.

We present a technique for performing three-dimensional (3D) pattern recognition by use of in-line digital holography. The complex amplitude distribution generated by a 3D object at an arbitrary plane located in the Fresnel diffraction region is recorded by phase-shifting interferometry. The digital hologram contains information about the 3D object's shape, location, and orientation. This information allows us to perform 3D pattern-recognition techniques with high discrimination and to measure 3D orientation changes. Experimental results are presented.

Inverse problems arise from a situation where there is a need to perceive an unknown object with partially known electromagnetic fields. For instance, it can be applied to find tumour in human body, or even find cracks in building internal structure by non-destructive testing. The process to investigate the characteristic of the unknown object by measuring the scattered field outside the object is inverse scattering method, namely Forward-Backward Time-Stepping (FBTS). It is highly relying on a correct estimation of the local context to reduce the cost functional value. This can lead to the creation of artifacts on condition that arbitrary error occurs in the iterative reconstruction process. Extended algorithms fused inside FBTS to eliminate the creation of artifacts, however, would increase the computational cost. This research work therefore shall focus on implementing an Automated Scaling Region of Interest (AS-ROI) in FBTS, which can zoom in to object's size and location for image reconstruction by using adaptive K-means clustering method. Since the reconstruction area is reduced, and with smaller number of pixels involved, such method would be able to operate in a timely manner other than improving object's accuracy. Numerical analysis on five cases of observations have shown that by rescale down the reconstruction area would require considerably less amount of computational time with respect to FBTS. Apart from that, fused AS-ROI with FBTS able to locate object accurately for all case analysis. Therefore, the newly defined reconstruction area is analogous to the actual object's location. Plus, accuracy of the reconstructed object which measured by Mean Squared Error (MSE) in term of its intensities level or the dielectric profiles comprises relative permittivity and conductivity are increased significantly.

Various factors (e.g., hand preference, object properties) constrain reach-to-grasp in hemispace. With object use, end-state comfort (ESC) has been shown to supersede the preferential use of one hand at the midline. To assess how location, size, and orientation of objects (dowel, mallet, cup) influence preferred-hand use and ESC (N = 50; Mage = 20.83), three preferential reaching tasks were implemented. Object location influenced hand selection in all tasks, along with size (cups) and orientation (mallets). Object location and orientation influenced ESC, but only with dowels and mallets. When oriented away from the preferred hand in hemispace, there was a higher occurrence of non-preferred hand use to facilitate ESC. Overall, findings add to understanding of ESC and preferential reaching with varying task demands.

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.

Visual object counting aims to count the number of objects in a given image or video. Among many object counting methods, the counting by density estimation method draws attention because of its capability of counting and its object localization ability. The method utilizes the object density map of the image with multiple objects. The density is estimated by a regression model which learns the mapping between the local features of the given image and the density map generated from its corresponding object locations. Unlike conventional methods that only rely on object locations for density map generation, in this paper, we show that the system performance can be increased by considering object shapes as well as object locations. To this end, we propose two approaches to generate the ground truth density map from the object locations. Both methods generate the density map which reflects structural features of the objects. We show that the regression models trained with density maps which reflect the object shape outperforms the models trained with density maps generated by the conventional density map generation method on several challenging benchmarks. In other words, we observe that it is essential to generate the ground truth density map according to object shape in the image.

Recently different time domain (TD) electromagnetic induction sensors have been developed and tested for UXO detection and discrimination. These sensors produce well-located, multi-axis, high-density data. One of such sensors is the Man Portable Vector (MPV) TD sensor build by G&G., Inc., which has two 75-cm diameter transmitter loops and five tri-axial cubic receivers located around the transmitter coils. This sensor produces unprecedented high-fidelity complete vector data sets. To take advantage of these high-quality data, in this paper we adapt the normalized surface magnetic source (NSMS) model to the MPV. The NSMS is a very simple and robust technique for predicting the EMI responses of various objects. The technique is applicable to any combination of magnetic or electromagnetic induction data for any arbitrary homogeneous or heterogeneous 3-D object or set of objects. The NSMS approach uses magnetic dipoles distributed on a fictitious closed surface as responding sources for predicting objects' EMI responses. The amplitudes of the NSMS sources are determined from actual measured data, and at the end the total NSMS is used as a discriminator. Usually, discrimination between UXO and non-UXO items is processed by first recovering the buried object's location and orientation using standard non-linear minimization techniques; this is the most time consuming part of the UXO classification process. In order to avoid solving a traditional ill-posed inverse scattering problem, here we adapt to TD-MPV data a recently developed physics-based approach, called (HAP), to estimate a buried object's location and orientation. The approach assumes the target exhibits a dipolar response and uses only three global values: (1) the magnetic field vector H, (2) the vector potential A, and (3) the scalar magnetic potential at a point in space. Of these three global values only the flux of the H field is measurable by the MPV sensor. However, the vector and scalar magnetic potentials can be recovered from measured magnetic field data using a 2D NSMS approach. To demonstrate the applicability of the NSMS and HAP techniques we report the results of a blind-test analysis using multi-axis TD MPV data collected at the U.S. Army's ERDC UXO test site.

A new physics based expression is presented for determining a buried object's location, orientation and magnetic polarizability. The approach assumes the target exhibits a dipolar response and uses only three global values: (1) a H magnetic field vector, (2) a vector potential A and (3) a scalar magnetic potential psi all at a single location in space. To illustrate the accuracy of the proposed algorithm, several numerical results are demonstrated using synthetic data for one and three dipoles.

Human vision processes light and dark stimuli in visual scenes with separate ON and OFF neuronal pathways. In nature, stimuli lighter or darker than their local surround have different spatial properties and contrast distributions (Ratliff et al., 2010; Cooper and Norcia, 2015; Rahimi-Nasrabadi et al., 2021). Similarly, in human vision, we show that luminance contrast affects the perception of lights and darks differently. At high contrast, human subjects of both sexes locate dark stimuli faster and more accurately than light stimuli, which is consistent with a visual system dominated by the OFF pathway. However, at low contrast, they locate light stimuli faster and more accurately than dark stimuli, which is consistent with a visual system dominated by the ON pathway. Luminance contrast was strongly correlated with multiple ON/OFF dominance ratios estimated from light/dark ratios of performance errors, missed targets, or reaction times (RTs). All correlations could be demonstrated at multiple eccentricities of the central visual field with an ON-OFF perimetry test implemented in a head-mounted visual display. We conclude that high-contrast stimuli are processed faster and more accurately by OFF pathways than ON pathways. However, the OFF dominance shifts toward ON dominance when stimulus contrast decreases, as expected from the higher-contrast sensitivity of ON cortical pathways (Kremkow et al., 2014; Rahimi-Nasrabadi et al., 2021). The results highlight the importance of contrast polarity in visual field measurements and predict a loss of low-contrast vision in humans with ON pathway deficits, as demonstrated in animal models (Sarnaik et al., 2014).SIGNIFICANCE STATEMENT ON and OFF retino-thalamo-cortical pathways respond differently to luminance contrast. In both animal models and humans, low contrasts drive stronger responses from ON pathways, whereas high contrasts drive stronger responses from OFF pathways. We demonstrate that these ON-OFF pathway differences have a correlate in human vision. At low contrast, humans locate light targets faster and more accurately than dark targets but, as contrast increases, dark targets become more visible than light targets. We also demonstrate that contrast is strongly correlated with multiple light/dark ratios of visual performance in central vision. These results provide a link between neuronal physiology and human vision while emphasizing the importance of stimulus polarity in measurements of visual fields and contrast sensitivity.

: The research described in this report was conducted in fulfillment of Project MM-1592, Non-traditional physics-based inverse approaches for determining a buried object's location submitted to the Strategic Environmental Research and Development Program's (SERDP) Exploratory Development Program (SEED) in response to Statement of Need MMSEED-07-02, Advanced Technologies for Detection, Discrimination, and Remediation of Munitions and Explosives of Concern (MEC): UXO Technology. The main focus of this research was to explore and develop new electromagnetic inverse scattering approaches for estimating the locations and orientations of buried objects that would not involve the solution of an ill-posed nonlinear minimization problem and that would be sufficiently robust and efficient to be used in real field UXO discrimination. We concentrated on the fundamental aspects and potential practical applicability to the UXO problem of three novel physics-based approaches: (1) pole-series expansions, (2) energy gradients, and (3) use of the physical properties of left-handed media (LHM). All three methods assume that targets' responses can be approximately reproduced by means of a set of magnetic dipoles-the scattered field singularities-distributed in particular points inside the objects. The algorithms are implemented using a numerical technique called the Normalized Surface Magnetic Source (NSMS) model. We studied the accuracy with which the methods can estimate an object's location, orientation, and equivalent magnetic polarizability, their robustness with respect to noise, their computational speed, and their requirements with regard to data quality and quantity. We ultimately wanted to determine which of these three techniques would be practical and reliable for use in real UXO production runs.

We present a technique to reconstruct the electromagnetic properties of a medium or a set of objects buried inside it from boundary measurements when applying electric currents through a set of electrodes. The electromagnetic parameters may be recovered by means of a gradient method without a priori information on the background. The shape, location and size of objects, when present, are determined by a topological derivative-based iterative procedure. The combination of both strategies allows improved reconstructions of the objects and their properties, assuming a known background.

This paper presents efficient object tracking in video sequences using multiple features by embedding mean shift into particle filters. When clutter background and occlusions are present. Particle filtering is used because it is very robust and performs well for non-linear and non-Gaussian dynamic state estimation problems. The image features, such as shape, texture, color, contours, and random motion appearance can be used to track the moving object(s) in videos. We proposed a smart video surveillance system with real-time as well as offline stored video database for moving object detection, classification and tracking capabilities. The graphical user interface of the proposed system using MATLAB 2012b is implemented that operates on both color and gray scale video images from a stationary camera. In this method, we proposed real time moving object detection and tracking system using static webcam that can processes 800*600, 640*480, and 320*240 resolution video sequences for capturing live scene as well as stored database of segmented videos. Issues related with object detection and tracking is jointly employed using mean shift and particle filters. Tracker will estimate the dynamic shape and random appearance of objects. Tracking requires location and shape of object in every segmented frame. Rectangular bounding box is used to improve the estimate of its shape and appearance. Target object may experience partial occlusions, intersection with other objects with similar color distributions, abrupt motion speed changes, and cluttered background. Test result shows improvement in terms of robustness, tracking drifts, accuracy of tracked bounding box to occlusions.