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Text-line extraction in unconstrained handwritten documents remains a challenging problem due to nonuniform character scale, spatially varying text orientation, and the interference between text lines. In order to address these problems, we propose a new cost function that considers the interactions between text lines and the curvilinearity of each text line. Precisely, we achieve this goal by introducing normalized measures for them, which are based on an estimated line spacing. We also present an optimization method that exploits the properties of our cost function. Experimental results on a database consisting of 853 handwritten Chinese document images have shown that our method achieves a detection rate of 99.52% and an error rate of 0.32%, which outperforms conventional methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel framework that exploits an informative reference channel in the processing of another channel. We formulate the problem as a maximum a posteriori estimation problem considering a reference channel and develop a probabilistic model encoding the interchannel correlations based on Markov random fields. Interestingly, the proposed formulation results in an image-specific and region-specific linear filter for each site. The strength of filter response can also be controlled in order to transfer the structural information of a channel to the others. Experimental results on satellite image fusion and chrominance image interpolation with denoising show that our method provides improved subjective and objective performance compared with conventional approaches. [ABSTRACT FROM AUTHOR]

Interpretability and explainability of machine learning systems have received ever-increasing attention, especially for deep neural networks. In the case of convolutional neural networks (CNNs), their properties are usually explained by generating local explanation maps (e.g., visualizing the contribution of individual pixels to a given prediction). In this paper, we propose a new framework that analyzes the inner workings of CNNs in terms of neural activations. To be precise, we consider a forward-pass as sequential activations of neurons and develop its inverse process, so that the inverse preserves the physical meaning of neuron activations. Our inverse process is formulated as a constrained optimization problem, and we solve the problem with the gradient projection algorithm. The proposed approach can provide equivalent visualization results to several conventional methods, and thus can be a reference tool for CNN visualization. Also, the attributions generated by our inverse method yield the state-of-the-art deletion scores and visualize the contribution of colors as well as shape features. [ABSTRACT FROM AUTHOR]

Proposes an algorithm for the design of optimal one-dimensional (1-D) and two-dimensional far-infrared radiation (FIR) filters over a discrete coefficient space. Discussion of the algorithm for the design of FIR filters; Comparison of the algorithms; Conclusion.

We propose a new method that classifies wafer images according to their defect types for automatic defect classification in semiconductor fabrication processes. Conventional image classifiers using global properties cannot be used in this problem, because the defects usually occupy very small regions in the images. Hence, the defects should first be segmented, and the shape of the segment and the features extracted from the region are used for classification. In other words, we need to develop a classification-after-segmentation approach for the use of features from the small regions corresponding to the defects. However, the segmentation of scratch defects is not easy due to the shrinking bias problem when using conventional methods. We propose a new Markov random field-based method for the segmentation of wafer images. Then we design an AdaBoost-based classifier that uses the features extracted from the segmented local regions. [ABSTRACT FROM AUTHOR]

This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, line-spacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg–Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. [ABSTRACT FROM AUTHOR]

Abstract: Conventional object retrieval or recognition methods based on feature matching sometimes fail when an object contains repetitive patterns, because features from repetitive patterns are too similar to each other. Specifically, when there arise many similar features in a query object due to repetitive patterns, they are usually not matched to the ones at the same positions of the reference object. Hence, the matching pairs between the query and reference image do not appear regular and thus homography estimation fails. In case when we use “nearest neighbor distance ratio” as a matching criterion, where enough distinction between the matched pairs should be secured, matching also fails due to similarity of features. In this paper, we propose a new feature matching strategy to alleviate this problem by discriminating repetitive patterns from the other salient ones and also by developing a way of utilizing the patterns for robust feature matching. Specifically, we first apply a conventional feature extraction method to a given image. Then we cluster features based on their similarity, i.e., we design a classifier that tells whether a feature is from a repetitive pattern or from a salient structure. For the effective use of repetitive patterns, we define a new descriptor based on support vector data description (SVDD) for describing clusters of similar features. In other words, a set of features from a pattern is defined to be a new feature in terms of its center and radius. For object recognition, the homography is found over the salient features by excluding repetitive features at first, which is then validated and refined by the repetitive patterns. The proposed method is tested with examples of matching buildings with repetitive patterns, and it is shown to be robuster and more reliable than the conventional methods. [Copyright &y& Elsevier]

This paper presents methods based on convolutional neural networks (CNNs) for removing compression artifacts. We modify the Inception module for the image restoration problem and use it as a building block for constructing blind and non-blind artifact removal networks. It is known that a CNN trained in a non-blind scenario (known compression quality factor) performs better than the one trained in a blind scenario (unknown factor), and our network is not an exception. However, the blind system is more practical because the compression quality factor is not always available or does not reflect the actual quality when the image is a transcoded or requantized image. Hence, in this paper, we also propose a pseudo-blind system that estimates the quality factor for a given compressed image and then applies a network that is trained with a similar quality factor. For this purpose, we propose a CNN that estimates the compression quality factor and prepare several non-blind artifact removal networks that are trained for some specific compression quality factors. We train the networks and conduct experiments on widely used compression standards, such as JPEG, MPEG-2, H.264, and HEVC. In addition, we conduct experiments for dynamically changing and transcoded videos to demonstrate the effectiveness of the quality estimation method. The experimental results show that the proposed pseudo-blind network performs better than the blind one for the various cases stated above and requires fewer computations. [ABSTRACT FROM AUTHOR]

This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the degradation models are known or assumed. For a step closer to real-world applications, CNNs are also blindly trained with the whole dataset, including diverse degradations. However, the conditional distribution of a high-quality image given a diversely degraded one is too complicated to be learned by a single CNN. Therefore, there have also been some methods that provide additional prior information to train a CNN. Unlike previous approaches, we focus more on the objective of restoration based on the Bayesian perspective and how to reformulate the objective. Specifically, our method relaxes the original posterior inference problem to better manageable sub-problems and thus behaves like a divide-and-conquer scheme. As a result, the proposed framework boosts the performance of several restoration problems compared to the previous ones. Specifically, our method delivers state-of-the-art performance on Gaussian denoising, real-world noise reduction, blind image super-resolution, and JPEG compression artifacts reduction. Our code and more details are available on our project page, https://github.com/JWSoh/VDIR.

Study Design: A retrospective clinical study., Objective: The purpose of this study was to identify risk factors for postoperative distal adding-on in Lenke 1A adolescent idiopathic scoliosis., Summary of Background Data: Distal adding-on is a postoperative complication associated with the Lenke type 1A curve. Although various factors are known to cause postoperative adding-on, no study has reported a correlation between sacral slanting and adding-on., Methods: A total of 126 consecutive patients who underwent posterior correction and fusion surgery for Lenke type 1A adolescent idiopathic scoliosis were included in this study. Curve type was further categorized into L4-left (L4-L) or L4-right (L4-R), based on the direction of the L4 vertebral tilt. Several clinical and radiological parameters including sacral slanting were investigated to identify risk factors associated with postoperative distal adding-on., Results: A total of 36 patients (28.6%) exhibited sacral slanting. Nineteen out of 20 L4-L type patients had left-sided sacral slanting, whereas 12 out of 16 L4-R type patients had right-sided sacral slanting. The group with adding-on (n = 13) demonstrated a significantly lower age than the group without adding-on (n = 113). Preoperative lumbar Cobb angle (P = 0.022) was determined to be an independent factor for adding-on according to logistic regression analysis. In the L4-R type, the last touching vertebra (LTV) level and the gap difference in levels between lowest instrumented vertebra and LTV (lowest instrumented vertebra-LTV) comprised significant variables., Conclusion: Sacral slanting typically occurs to the left in the L4-L type and to the right in the L4-R type. The size of the preoperative lumbar curve was found to be an independent risk factor that caused adding-on in patients with Lenke type 1A scoliosis. In the L4-R type, right-sided sacral slanting tended to lower the LTV. Therefore, the fusion level might be shorter to save the motion segments resulting in distal adding-on., Level of Evidence: 4.

A cooperative cognitive radio scheme exploiting primary signals for energy harvesting is proposed. The relay sensor node denoted as the secondary transmitter (ST) harvests energy from the primary signal transmitted from the primary transmitter, and then uses it to transmit power superposed codes of the secrecy signal of the secondary network (SN) and of the primary signal of the primary network (PN). The harvested energy is split into two parts according to a power splitting ratio, one for decoding the primary signal and the other for charging the battery. In power superposition coding, the amount of fractional power allocated to the primary signal is determined by another power allocation parameter (e.g., the power sharing coefficient). Our main concern is to investigate the impact of the two power parameters on the performances of the PN and the SN. Analytical or mathematical expressions of the outage probabilities of the PN and the SN are derived in terms of the power parameters, location of the ST, channel gain, and other system related parameters. A jointly optimal power splitting ratio and power sharing coefficient for achieving target outage probabilities of the PN and the SN, are found using these expressions and validated by simulations.

Background: To describe and assess clinical outcomes of the semi-circumferential decompression technique for microsurgical en-bloc total ligamentum flavectomy with preservation of the facet joint to treat the patients who have a lumbar spinal stenosis with degenerative spondylolisthesis., Methods: We retrospectively analyzed the clinical and radiologic outcomes of 19 patients who have a spinal stenosis with Meyerding grade I degenerative spondylolisthesis. They were treated using the "semi-circumferential decompression" method. We evaluated improvements in back and radiating pain using a visual analogue scale (VAS) and the Oswestry Disability Index (ODI). We also evaluated occurrence of spinal instability on radiological exam using percentage slip and slip angle., Results: The mean VAS score for back pain decreased significantly from 6.3 to 4.3, although some patients had residual back pain. The mean VAS for radiating pain decreased significantly from 8.3 to 2.5. The ODI score improved significantly from 25.3 preoperatively to 10.8 postoperatively. No significant change in percentage slip was observed (10% preoperatively vs. 12.2% at the last follow-up). The dynamic percentage slip (gap in percentage slip between flexion and extension X-ray exams) did not change significantly (5.2% vs. 5.8%). Slip angle and dynamic slip angle did not change (3.2° and 8.2° vs. 3.6° and 9.2°, respectively)., Conclusions: The results suggested that semi-circumferential decompression is a clinically recommendable procedure that can improve pain. This procedure does not cause spinal instability when treating patients who have a spinal stenosis with degenerative spondylolisthesis.

In this paper, we propose an algorithm to compose a geometrically dewarped and visually enhanced image from two document images taken by a digital camera at different angles. Unlike the conventional works that require special equipment or assumptions on the contents of books or complicated image acquisition steps, we estimate the unfolded book or document surface from the corresponding points between two images. For this purpose, the surface and camera matrices are estimated using structure reconstruction, 3-D projection analysis, and random sample consensus-based curve fitting with the cylindrical surface model. Because we do not need any assumption on the contents of books, the proposed method can be applied not only to optical character recognition (OCR), but also to the high-quality digitization of pictures in documents. In addition to the dewarping for a structurally better image, image mosaic is also performed for further improving the visual quality. By finding better parts of images (with less out of focus blur and/or without specular reflections) from either of views, we compose a better image by stitching and blending them. These processes are formulated as energy minimization problems that can be solved using a graph cut method. Experiments on many kinds of book or document images show that the proposed algorithm robustly works and yields visually pleasing results. Also, the OCR rate of the resulting image is comparable to that of document images from a flatbed scanner.