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Abstract: Motivation: Physical properties of coal such as particle size distribution have a large influence on the stability and operational behavior of fluidized bed reactors and metallurgical furnaces. In particular, the presence of large amounts of “fine” particles invariably has a drastic effect on plant performance as a result of impaired gas permeability characteristics of the coal or ore burden. Therefore, monitoring and control of particle size distribution profiles of such aggregate material on reactor feed streams, such as moving conveyor belts, is critical for predictable operation of these processes. Traditionally, the method of sieve analysis using stock or belt cut samples has been widely used in industry. Unfortunately, the reliability and usefulness of belt cut techniques are constrained by frequency of sampling as well as laboratory analysis turnaround times. For real-time monitoring and control purposes, automated sampling and analysis methods are more desirable. Methods: In this study, the problem of estimating the particle size distribution profile of material on a moving conveyor belt is formulated within a texture classification framework, which has its basis in machine vision and incorporates elements from statistical pattern recognition. Using exemplar images of coal particles taken on a process stream, a set of local features that compactly describes the textural properties of each image are expressed in terms of localized nonlinear features called textons. Representation of image information using textons is primarily motivated by insights from neuroscience research on the optimality of linear oriented basis functions as models of perception in early processing of visual information in the cortex regions of the human brain. Using these representations for different textures, nearest neighbor and support vector machine classification models are subsequently used to classify test images. Results: Using a comprehensive evaluation, it is shown that the use of texton representation obtained from decomposing images with linear oriented basis functions can be sufficiently discriminative compared to the use of the widely used second-order statistical features or features from other baseline models. In particular, model performance obtained with appropriately tuned filters suggest the importance of including shape and spatial structure information in an image representation for texture classification of coal particles. Furthermore, using nonlinear support vector machines rather than nearest neighbor classifiers significantly improved classification performance. A texture classification approach to particle size profile estimation has potential applications in the online monitoring of the proportion of “fines” in coal material on moving conveyor belts. [Copyright &y& Elsevier]

Challenges such as complex backgrounds, drastic variations in target scales, and dense distributions exist in natural scenes. Some algorithms optimize multi-scale object detection performance by combining low-level and high-level information through feature fusion strategies. However, these methods overlook the inherent spatial properties of objects and the relationships between foreground and background. To fundamentally enhance the multi-scale detection capability, we propose a depth-constrained multi-scale object detection network that simultaneously learns object detection and depth estimation through a unified framework. In this network, depth features are merged into the detection branch as auxiliary information and constrained and guided to obtain better spatial representations, which enhances discrimination between multi-scale objects. We also introduce a novel cross-modal fusion (CmF) strategy that utilizes depth awareness and low-level detail clues to supplement edge information and adjust attention weight preferences. We find complementary information from RGB and high-quality depth features to achieve better multi-modal information fusion. Experimental results demonstrate that our method outperforms state-of-the-art methods on the KINS dataset, with an improvement of 3.0% in AP score over the baseline network. Furthermore, we validate the effectiveness of our proposed method on the KITTI dataset. [ABSTRACT FROM AUTHOR]

Cartoon-texture decomposition of images is a hot research topic in image processing, its main goal is to decompose a given image into cartoon and texture parts measured by different norms. In the existing partial decomposition model, the H−1 functional is often used to measure the oscillation function, which is obtained by the Hodge decomposition. Because a divergence-free vector function is neglected in the calculation of the Hodge decomposition, some directional information will be lost in the texture part, which will affect the decomposition results. In this paper, a new cartoon-texture decomposition model with divergence-free vector field constraint is established by analyzing the theory of the vector field. Mathematically, the new model makes the texture more accurate and complete. In order to solve this model, we transform the constrained optimization problem into an unconstrained problem by using the augmented Lagrange method, and then solve it with the alternating direction method. The proposed cartoon-texture decomposition model contains the divergence-free vector field information, so it improves the decomposition effect obviously, and the experimental results confirm the validity of the proposed model. [ABSTRACT FROM AUTHOR]

The total variation (TV) minimization algorithms is a classical representative of the optimization algorithms and have been widely used for their simpleness, robustness, expandability and their potential of accurately reconstructing images from sparse data or noisy data. The commonly used constrained TV (cTV) program is the data-divergence constrained TV (DDcTV) minimization. The other recently proposed cTV program is the TV constrained data-divergence minimization (TVcDM). In the work, we compare the two programs the convergence rate and the reconstruction accuracy via the sparse-view projection set and the noisy projection set in the context of CT. Further, we compare their reconstruction accuracy via real data in electron paramagnetic resonance imaging (EPRI). The studies show that TVcDM has much faster convergence rate relative to DDcTV and that TVcDM may achieve higher accuracy in most cases no matter in sparse-view projections CT reconstructions or in noisy projections CT reconstructions or in EPRI sparse reconstructions. From a comprehensive perspective, TVcDM has superior convergence and accurate reconstruction performance relative to DDcTV and should be recommended in the application of TV reconstruction algorithms. The knowledge and insights gained in the work may be extended to the performance analysis of other optimization-based reconstruction algorithms. [ABSTRACT FROM AUTHOR]

One of the greatest challenges in fabricating artificial tissues and organs is the incorporation of vascular networks to support the biological requirements of the embedded cells, encouraging tissue formation and maturation. With the advent of 3D printing technology, significant progress has been made with respect to generating vascularized artificial tissues. Current algorithms to generate arterial/venous trees are computationally expensive and offer limited freedom to optimize the resulting structures. Furthermore, there is no method for algorithmic generation of vascular networks that can recapitulate the complexity of the native vasculature for more than two trees, and export directly to a 3D printing format. Here, we report such a method, using an accelerated constructive constrained optimization approach, by decomposing the process into construction, optimization, and collision resolution stages. The new approach reduces computation time to minutes at problem sizes where previous implementations have reported days. With the optimality criterion of maximizing the volume of useful tissue which could be grown around such a network, an approach of alternating stages of construction and batch optimization of all node positions is introduced and shown to yield consistently more optimal networks. The approach does not place a limit on the number of interpenetrating networks that can be constructed in a given space; indeed we demonstrate a biomimetic, liver-like tissue model. Methods to account for the limitations of 3D printing are provided, notably the minimum feature size and infill at sharp angles, through padding and angle reduction, respectively. [ABSTRACT FROM AUTHOR]

In most cases, the conventional pencil-drawing-synthesized methods were in terms of geometry and stroke, or only used classic edge detection method to extract image edge characters. In this paper, we propose a new method to produce pencil drawing from natural image. The synthesized result can not only generate pencil sketch drawing, but also can save the color tone of natural image and the drawing style is flexible. The sketch and style are learned from the edge of original natural image and one pencil image exemplar of artist’s work. They are accomplished through using the convolutional neural network feature maps of a natural image and an exemplar pencil drawing style image. Large-scale bound-constrained optimization (L-BFGS) is applied to synthesize the new pencil sketch whose style is similar to the exemplar pencil sketch. We evaluate the proposed method by applying it to different kinds of images and textures. Experimental results demonstrate that our method is better than conventional method in clarity and color tone. Besides, our method is also flexible in drawing style. [ABSTRACT FROM AUTHOR]