Manufacturers have implemented continuous multistage manufacturing processes (MMPs) for their efficiency and flexibility, especially in high-volume production of liquid products. While significant attention has been given to the development of data-driven soft sensors in manufacturing fields, research explicitly addressing continuous MMPs of liquid products is very scarce due to the following challenges: obtaining intermediate output labels and determining lead-time between stages. To overcome these challenges, we introduce Multistage Net, a novel machine learning model designed for continuous MMPs of liquid products. In Multistage Net, several interstage blocks are organized in a hierarchical structure within a multistage module. The interstage block is proposed to capture the sequential dependency between the previous and current stages and concurrently explores the lead-time relationship. From the interconnected interstage blocks, the multistage module can learn the sequential nature of MMPs across all stages even in the absence of intermediate output labels. Through validation experiments on two real-world datasets, it is shown that Multistage Net demonstrates superior prediction performance compared to baseline models. Moreover, further analysis reveals that the prediction performance of Multistage Net is not significantly impacted by the nonexistence of lead-time labels.

This paper presents a novel scene text detection algorithm, Canny Text Detector, which takes advantage of the similarity between image edge and text for effective text localization with improved recall rate. As closely related edge pixels construct the structural information of an object, we observe that cohesive characters compose a meaningful word/sentence sharing similar properties such as spatial location, size, color, and stroke width regardless of language. However, prevalent scene text detection approaches have not fully utilized such similarity, but mostly rely on the characters classified with high confidence, leading to low recall rate. By exploiting the similarity, our approach can quickly and robustly localize a variety of texts. Inspired by the original Canny edge detector, our algorithm makes use of double threshold and hysteresis tracking to detect texts of low confidence. Experimental results on public datasets demonstrate that our algorithm outperforms the state-of the-art scene text detection methods in terms of detection rate.

The maximally stable extremal region (MSER) method has been widely used to extract character candidates, but because of its requirement for maximum stability, high text detection performance is difficult to obtain. To overcome this problem, we propose a robust character candidate extraction method that performs ER tree construction, sub-path partitioning, sub-path pruning, and character candidate selection sequentially. Then, we use the AdaBoost trained character classifier to verify the extracted character candidates. Then, we use heuristics to refine the classified character candidates and group the refined character candidates into text regions according to their geometric adjacency and color similarity. We also apply the proposed text detection method to two different color channels Crand Cband obtain the final detection result by combining the detection results on the three different channels. The proposed text detection method on ICDAR 2013 dataset achieved 8%, 1%, and 4% improvements in recall rate, precision rate and f-score, respectively, compared to the state-of-the-art methods.

Abstract Typical high dynamic range (HDR) imaging approaches based on multiple images have difficulties in handling moving objects and camera shakes, suffering from the ghosting effect and the loss of sharpness in the output HDR image. While there exist a variety of solutions for resolving such limitations, most of the existing algorithms are susceptible to complex motions, saturation, and occlusions. In this paper, we propose an HDR imaging approach using the coded electronic shutter which can capture a scene with row‐wise varying exposures in a single image. Our approach enables a direct extension of the dynamic range of the captured image without using multiple images, by photometrically calibrating rows with different exposures. Due to the concurrent capture of multiple exposures, misalignments of moving objects are naturally avoided with significant reduction in the ghosting effect. To handle the issues with under‐/over‐exposure, noise, and blurs, we present a coherent HDR imaging process where the problems are resolved one by one at each step. Experimental results with real photographs, captured using a coded electronic shutter, demonstrate that our method produces a high quality HDR images without the ghosting and blur artifacts.

This paper presents a novel structure-preserving image decomposition operator called bilateral texture filter . As a simple modification of the original bilateral filter [Tomasi and Manduchi 1998], it performs local patch-based analysis of texture features and incorporates its results into the range filter kernel. The central idea to ensure proper texture/structure separation is based on patch shift that captures the texture information from the most representative texture patch clear of prominent structure edges. Our method outperforms the original bilateral filter in removing texture while preserving main image structures, at the cost of some added computation. It inherits well-known advantages of the bilateral filter, such as simplicity, local nature, ease of implementation, scalability, and adaptability to other application scenarios.

Abstract This paper proposes an algorithm which uses image registration to estimate a non‐uniform motion blur point spread function (PSF) caused by camera shake. Our study is based on a motion blur model which models blur effects of camera shakes using a set of planar perspective projections (i.e., homographies). This representation can fully describe motions of camera shakes in 3D which cause non‐uniform motion blurs. We transform the non‐uniform PSF estimation problem into a set of image registration problems which estimate homographies of the motion blur model one‐by‐one through the Lucas‐Kanade algorithm. We demonstrate the performance of our algorithm using both synthetic and real world examples. We also discuss the effectiveness and limitations of our algorithm for non‐uniform deblurring.

Product line assets must be designed so that inclusion or exclusion of variable features causes little changes to components implementing other features. In order to achieve this goal, various relationships or dependencies that variable features have with other features must be analyzed thoroughly before designing product line assets. An object-oriented approach has been proposed to manage operational dependencies between features. However, it still suffers from code scattering and tangling problems. To address these problems, this paper introduces aspect-oriented implementation patterns which separate feature relations and dependencies from components implementing core functionality of features. Using these patterns, we can improve adaptability, scalability (modifiability in general), and configurability of product line assets. A simple calculator product line example is used to validate this approach.