Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach

In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available.The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios

MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter

Multiple-input multiple-output (MIMO) radar has become a thriving subject of research during the past decades. In the MIMO radar context, it is sometimes more accurate to model the radar clutter as a non-Gaussian process, more specifically, by using the spherically invariant random process (SIRP) model. In this paper, we focus on the estimation and performance analysis of the angular spacing between two targets for the MIMO radar under the SIRP clutter. First, we propose an iterative maximum likelihood as well as an iterative maximum a posteriori estimator, for the target's spacing parameter estimation in the SIRP clutter context. Then we derive and compare various Cram\'er-Rao-like bounds (CRLBs) for performance assessment. Finally, we address the problem of target resolvability by using the concept of angular resolution limit (ARL), and derive an analytical, closed-form expression of the ARL based on Smith's criterion, between two closely spaced targets in a MIMO radar context under SIRP clutter. For this aim we also obtain the non-matrix, closed-form expressions for each of the CRLBs. Finally, we provide numerical simulations to assess the performance of the proposed algorithms, the validity of the derived ARL expression, and to reveal the ARL's insightful properties.Comment: 34 pages, 12 figure

Negative affective state mimics effects of perceptual load on spatial perception

Recent electrophysiological evidence has shown that perceptual load and negative affective state can produce very similar, early-attention gating effects in early visual areas, modulating the processing of peripheral stimuli. Here we assessed the question of whether or not these modulatory effects of perceptual load and negative affect (NA) lead to comparable changes in spatial perception abilities, which could be captured at the behavioral level. High perceptual load at fixation impaired the precise spatial localization of peripheral textures, relative to a low perceptual load condition. By contrast, the coarse spatial encoding of these peripheral stimuli was not load-dependent, under neutral affective conditions. The transient experience of NA was induced in an independent sample of participants, who showed decreased performance in the localization task, even at a low perceptual-load level. These results were observed in the absence of any systematic eye movement toward the peripheral textures. These findings suggest that spatial location perception is an attention-dependent, as well as state-dependent process, in the sense that NA, very much like load, can dynamically shape early spatial perceptual abilities. Although NA mimics load during spatial localization, we discuss the possibility that these two effects likely depend upon nonoverlapping brain networks

3D Textured Model Encryption via 3D Lu Chaotic Mapping

In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. The surface models include point clouds, meshes and textured models. Previous work mainly focus on encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method of 3D textured model. We encrypt the vertexes, the polygons and the textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, edges and texture maps are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. In addition, our method can resistant several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI

Preparation and structural properties of thin films and multilayers of the Heusler compounds Cu2MnAl, Co2MnSn, Co2MnSi and Co2MnGe

We report on the preparation of thin films and multilayers of the intermetallic Heusler compound CuMnAl, Co2MnSn, Co2MnSi and Co2MnGe by rf-sputtering on MgO and Al2O3 substrates. Cu2MnAl can be grown epitaxially with (100)-orientation on MgO (100) and in (110)-orientation on Al2O3 a-plane. The Co based Heusler alloys need metallic seedlayers to induce high quality textured growth. We also have prepared multilayers with smooth interfaces by combining the Heusler compounds with Au and V. An analysis of the ferromagnetic saturation magnetization of the films indicates that the Cu2MnAl-compound tends to grow in the disordered B2-type structure whereas the Co-based Heusler alloy thin films grow in the ordered L21 structure. All multilayers with thin layers of the Heusler compounds exhibit a definitely reduced ferromagnetic magnetization indicating substantial disorder and intermixing at the interfaces.Comment: 21 pages, 8 figure

Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks

Microscopic analysis of histological sections is considered the "gold standard" to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does not scale with high throughput imaging. We present an automatic approach for parcellating histological sections at 2um resolution. It is based on a convolutional neural network that combines topological information from probabilistic atlases with the texture features learned from high-resolution cell-body stained images. The model is applied to visual areas and trained on a sparse set of partial annotations. We show how predictions are transferable to new brains and spatially consistent across sections.Comment: Accepted for oral presentation at International Symposium of Biomedical Imaging (ISBI) 201

Retinal vessel segmentation using Gabor Filter and Textons

This paper presents a retinal vessel segmentation method that is inspired by the human visual system and uses a Gabor filter bank. Machine learning is used to optimize the filter parameters for retinal vessel extraction. The filter responses are represented as textons and this allows the corresponding membership functions to be used as the framework for learning vessel and non-vessel classes. Then, vessel texton memberships are used to generate segmentation results. We evaluate our method using the publicly available DRIVE database. It achieves competitive performance (sensitivity=0.7673, specificity=0.9602, accuracy=0.9430) compared to other recently published work. These figures are particularly interesting as our filter bank is quite generic and only includes Gabor responses. Our experimental results also show that the performance, in terms of sensitivity, is superior to other methods