Space-air-ground integrated networks (SAGINs) augmented with mobile edge computing (MEC) provide a unified yet heterogeneous substrate for latency-sensitive services. Deploying service function chains (SFCs) over satellites, aerial platforms, and ground nodes, however, is difficult due to hierarchical resource heterogeneity, time-varying network states, and stringent end-to-end (E2E) delay requirements. In this paper, we study online SFC embedding in a three-layer SAGIN-MEC architecture under coupled computing and networking constraints. We model the deployment as a two-stage process: (i) placing each virtual network function (VNF) onto feasible nodes subject to computing-capacity constraints, and (ii) mapping inter-VNF traffic onto feasible paths subject to bandwidth and delay constraints. To achieve adaptive decisions under dynamic states, we cast the problem as graph reinforcement learning by jointly encoding the substrate topology and each SFC into a unified graph state, and propose a structure-aware PPO agent that combines graph convolution with domain features and an action-masking mechanism to eliminate infeasible placement/routing actions. Extensive experiments in dynamic large-scale scenarios show that the proposed method consistently outperforms competitive baselines, improving the acceptance rate by 6.991% under high load, reducing the average E2E delay by 13.556%, and increasing the long-term revenue-to-cost ratio by 27.763% on average.

Autonomous aerial vehicles (AAVs), as the key Internet of Things (IoT) devices, play a dominant position in low-altitude environments. Semantic communication (SC), as the next-generation communication technology, serves as a bridge for surpassing the Shannon limit toward the 6G wireless network. Establishing air-ground SC to provide intelligent IoT services is a crucial initiative for building future smart cities. In this article, we propose an AAV-based SC framework, named diffusion joint source-channel coding (D-JSCC). Abandoning traditional convolutional neural networks, we use transformers as the backbone and innovatively incorporate the diffusion model (DM) for image enhancement, achieving an optimal balance between image distortion and human perception. To accurately capture the numerical and perceptual loss induced by wireless channels and seamlessly amalgamate the DM with SC, we integrate channel states as strong prior information to refine the sampling process. Furthermore, we employ the gradient guidance strategy, which counteracts the randomness of sampling ensuring high robustness in harsh communication conditions. Additionally, we strike a balance between performance and sampling steps, ensuring both efficient computation and high-quality image enhancement. Comprehensive experiments demonstrate the advantages of D-JSCC across different communication environments.

Infrared target detection is essential for many computer vision tasks. Generally, the IR images present common infrared characteristics, such as poor texture information, low resolution, and high noise. However, these characteristics are ignored in the existing detection methods, making them fail in real-world scenarios. In this paper, we take infrared intensity into account and propose a novel backbone network named Deep-IRTarget. We first extract infrared intensity saliency by a convolution with a Gaussian kernel filtering the images in the frequency domain. We then propose the triple self-attention network to further extract spatial domain image saliency by selectively emphasize interdependent semantic features in each channel. Jointly exploiting infrared characteristics in the frequency domain and the overall semantic interdependencies in the spatial domain, the proposed Deep-IRTarget outperforms existing methods in real-world Infrared target detection tasks. Experimental results on two infrared imagery datasets demonstrate the superiorly of our model.

Document Camera digitalization devices are low-cost, easy to use, produce good quality images, are able to digitalize pages of bound books without damaging their spine, etc. On the other hand, they may bring two serious problems. The first one appears if the document to be digitalized is printed on glossy paper. The paper reflects the different illumination sources from the environment producing a specular noise in the document image. The second problem occurs when the document to be digitalized does not lay flat on the digitalization surface. This paper presents solutions to both problems. The results obtained in almost 400 test images may be considered satisfactory.

Today, students are offered a wide variety of alternatives to printed material for the consumption of educational content. Previous research suggests that, while digital content has its advantages, printed content still offers benefits that cannot be matched by digital media. This paper introduces the Meaningful Education and Training Information System (METIS), a multi-faceted hybrid book learning platform. The goal of the system is to provide an easy digital-to-print-to-digital content creation and reading service. METIS incorporates technology for layout, personalization, co-creation and assessment. These facilitate and, in many cases, significantly simplify common teacher/student tasks. Our system has been demonstrated at several international education events, partner engagements, and pilots with local universities and high schools. We present the system and discuss how it enables hybrid learning.

We propose a multi-scale approach to extract annotations from document images in a meaningful way. It compares the rectified captured image with original image, which can be obtained through image retrieval technology, at various resolutions, in order to remove the noise caused by nonuniform distortions, such as camera lens distortion and document surface curvature, while preserving the true annotations. It also provides users lots of flexibility with a voting scheme and potentially different weights at different resolution levels. In addition, we analyzed the final annotation image and found the meaningful pieces out of it, such as an image patch of a handwritten paragraph. This broadens the application of annotation extraction, and makes it easier to share the notes. It can be applied to various imaging systems, such as flatbed scanner, mobile phone camera, or fixed camera etc. Experimental results are presented and compared with previous approaches.

A modern smartphone is a convenient and capable tool for document capture. However, without proper enhancement, a casually taken digital photograph is likely to bear visual artifacts of its capture and offers limited utility as a document. Detection of quadrilateral document borders enables perspective correction and cropping, two critical tasks in the enhancement pipeline. This paper presents a method for detecting quadrilateral document borders based on local features extracted from line segments and a machine learning framework. In contrast to many previous methods, this method does not assume printed text content and is thus applicable to a broader range of documents. This method encompasses: 1) shape-based rejection rules that reduce the pool of candidate quadrilaterals; 2) features that take document area, borders, and collinear content into account; 3) a metric that ranks quadrilaterals according to a ground-truth quadrilateral; and 4) a machine learning framework based on learning-to-rank. Experimental results on real-world images show that the listwise learning method ListNet performs significantly better than the pairwise learning method Ranking SVM for our application. The results also show that our multi-feature method significantly outperform an existing single feature method.

Web pages consist of not only actual content, but also other elements such as branding banners, navigational elements, advertisements, copyright etc. This noisy content is typically not related to the main subjects of the webpages. Identifying the part of actual content, or clipping web pages, has many applications, such as high quality web printing, e-reading on mobile devices and data mining. Although there are many existing methods attempting to address this task, most of them can either work only on certain types of Web pages, e.g. article pages, or has to develop different models for different websites. We formulate the actual content identifying problem as a DOM tree node selection problem. We develop multiple features by utilizing the DOM tree node properties to train a machine learning model. Then candidate nodes are selected based on the learning model. Based on the observation that the actual content is usually located in a spatially continuous block, we develop a grouping technology to further filter out noisy data and pick missing data for the candidate nodes. We conduct extensive experiments on a real dataset and demonstrate our solution has high quality outputs and outperforms several baseline methods.

Retailers need to monitor products on store shelves in order to maintain adequate stocking and in many cases to comply with a placement arrangement defined by a planogram. It is possible that such inspections be performed automatically or semi-automatically using computer vision techniques. Towards this goal, we propose an algorithm to detect shelves from images captured with a handheld digital camera. First, we identify the vanishing point and the associated line segments corresponding to the shelves. Second, we divide the image into equal-angle wedges centered at the vanishing point and project the associated line segments into the wedges. Finally, we identify shelves by analyzing the projections.

Warped text-lines often appear whenever one performs the digitalization of bound documents using flatbed scanners or digital cameras. Compensating such distortion is an important pre-processing step in document transcription via OCR, for instance. This paper presents an efficient algorithm for text-line segmentation for document images. A typographic study and parameter tuning are done yielding into high values for precision, recall and f-measure metrics. The method presented outperforms the competing algorithms using a public available dataset.

Managing large document databases has become an important task. Being able to automatically compare document layouts and classify and search documents with respect to their visual appearance proves to be desirable in many applications. We propose a new algorithm that approximates a metric function between documents based on their visual similarity. The comparison is based only on the visual appearance of the document without taking into consideration its text content. We measure the similarity of single page documents with respect to distance functions between three document components: background, text, and saliency. Each document component is represented as a Gaussian mixture distribution; and distances between the components of different documents are calculated as an approximation of the Hellinger distance between corresponding distributions. Since the Hellinger distance obeys the triangle inequality, it proves to be favorable in the task of nearest neighbor search in a document database. Thus, the computation required to find similar documents in a document database can be significantly reduced.

Text segmentation is usually the first step taken towards the reuse and repurposing of PDF documents. Through experimental evaluation, we found that the leading text segmentation algorithms have limitations for contemporary consumer magazines. We propose a new local homogeneity measure based on line space, and incorporate this new feature into a region growing algorithm. Using a fixed set of parameters, our algorithm achieved robust performance on PDF magazines with wide-ranging layouts and styles.

Portable digital cameras have become omnipresent. Their low-lost, simplicity to use, flexibility, and good quality images have widened their applicability far beyond their original purpose of taking personal photos. Every day people discover new uses for them from photographing teaching boards to documents. One of the difficulties of using cameras is the occurrence of specular noise whenever the photographed object is glossy. This paper presents an efficient algorithm for removing the specular noise of photographed documents by taking multiple images with different illumination sources.

Many people use the Web as the main source of information in their daily lives. However, most web pages contain non-informative components such as side bars, footers, headers, and advertisements, which are undesirable for certain applications like printing. We demonstrate a system that automatically extracts the informative contents from news- and blog-like web pages. In contrast to many existing methods that are limited to identifying only the text or the bounding rectangular region, our system not only identifies the content but also the structural roles of various content components such as title, paragraphs, images and captions. The structural information enables re-layout of the content in a pleasing way. Besides the article text extraction, our system includes the following components: 1) print-link detection to identify the URL link for printing, and to use it for more reliable analysis and recognition; 2) title detection incorporating both visual cues and HTML tags; 3) image and caption detection utilizing extensive visual cues; 4) multiple-page and next page URL detection. The performance of our system has been thoroughly evaluated using a human labeled ground truth dataset consisting of 2000 web pages from 100 major web sites. We show accurate results using such a dataset.

The user experience of printing web pages has not been very good. Web pages typically contain contents that are not print-worthy or informative such as side bars, footers, headers, advertisements, and auxiliary information for further browsing. Since the inclusion of such contents degrades the web printing experience, we have developed a tool that first selects the main part of the web page automatically and then allows users to make adjustments. In this paper, we describe the algorithm for selecting the main content automatically during the first pass. The web page is first segmented into several coherent areas or blocks using our web page segmentation method that clusters content based on the affinity values between basic elements. The relative importance values for the segmented blocks are computed using various features and the main content is extracted based on the constraint of one DOM (Document Object Model) sub-tree and high important scores. We evaluated our algorithm on 65 web pages and computed the accuracy based on area of overlap between the ground truth and the extracted result of the algorithm.

In this paper, we show a multimedia system for processing mobile camera captured documents. Using a client application on a mobile phone, a user can capture a document image, and send the image to a processing server so that the document image can be restored using automatic perspective and illumination corrections. The restored document can then be sent to a web-connected printer to complete the copying task.

In this paper, we discuss some unique challenges of sustainable interaction design (SID) and present our work that aims to reduce paper waste from web printing. We conducted a two-month field study of current behaviors and attitudes around printing, and the results confirmed the affordances of paper, but also revealed many problems associated with printing web content. We then designed and implemented a browser extension, PrintMarmoset, that targets these problems while simultaneously addressing user needs and environmental responsibility. It allows users to effortless select or remove web content for printing. We have also incorporated a data sharing mechanism into our solution to assist in the adoption of the tool and created visualizations to encourage user reflection and exploration.

This work studies the problem of extracting articles from Web pages for better printing. Different from existing approaches of article extraction, Web printing poses several unique requirements: 1) Identifying just the boundary surrounding the text-body is not the ideal solution for article extraction. It is highly desirable to filter out some uninformative links and advertisements within this boundary. 2) It is necessary to identify paragraphs, which may not be readily separated as DOM nodes, for the purpose of better layout of the article. 3) Its performance should be independent of content domains, written languages, and Web page templates. Toward these goals we propose a novel method of article extraction using both DOM (Document Object Model) and visual features. The main components of our method include: 1) a text segment/paragraph identification algorithm based on line-breaking features, 2) a global optimization method, Maximum Scoring Subsequence, based on text segments for identifying the boundary of the article body, 3) an outlier elimination step based on left or right alignment of text segments with the article body. Our experiments showed the proposed method is effective in terms of precision and recall at the level of text segments.

We present a spatially variant framework for correcting uneven illumination and color cast, problems commonly associated with digitized books. The core of our method is a color image segmentation algorithm based on watershed transform and noise thresholding. We propose a threshold estimation method using the histogram of the gradient magnitude. The critical parameters of the segmentation are further constrained by statistical analysis of multiple book pages conducted in a bootstrap pass. We demonstrate the consistent performance of the proposed method on books with wide-ranging content and image quality.

Functional image classification is the assignment of different image types to separate classes to optimize their rendering for reading or other specific end task, and is an important area of research in the publishing and multi-Average industries. This paper presents recent research on optimizing the simultaneous classification of documents, photos and logos. Each of these is handled during printing with a class-specific pipeline of image transformation algorithms, and misclassification results in pejorative imaging effects. This paper reports on replacing an existing classifier with a Weka-based classifier that simultaneously improves accuracy (from 85.3% to 90.8%) and performance (from 1458 msec to 418 msec/image). Generic subsampling of the images further improved the performance (to 199 msec/image) with only a modest impact on accuracy (to 90.4%). A staggered subsampling approach, finally, improved both accuracy (to 96.4%) and performance (to 147 msec/image) for the Weka-base classifier. This approach did not appreciable benefit the HP classifier (85.4% accuracy, 497 msec/image). These data indicate staggered subsampling using the optimized Weka classifier substantially improves the classification accuracy and performance without resulting in additional “egregious” misclassifications (assigning photos or logos to the “document” class).

Converting paper books into electronic form provides benefits for archiving, distribution and content reuse. However, directly scanned images are usually undesirable for electronic books, and automated content re-mastering is required. In this paper, we describe a comprehensive image processing suite consisting of three major components: 1) image enhancement with deskew, cropping, color correction, contrast enhancement and text sharpening, 2) compound document image compression, and 3) extraction of TOC (table of content) and linking. We built a processing pipeline that automatically converts a set of scanned page images into a high quality and highly compressed e-book in the popular PDF format.

We present a novel color gradient operator based on a weighted max distance (a weighted Minkowski distance of L/sub /spl infin// norm) with the weights determined by the coherency of local gradient orientations. We demonstrate its performance advantages by quantitatively comparing it with several existing gradient operators using synthetic images.

We introduce a novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2-D envelope detection. We describe and compare two algorithms for envelope detection based on (1) the Hilbert transform and (2) zero crossings. We present criteria for filter selection and discuss quantitatively their effect on feature extraction. The performance of our method is demonstrated experimentally on samples of both natural and synthetic textures.

Robust and reliable edge detection is an important step for accomplishing automatic detection of heart wall boundaries in echocardiograms. We present an edge detection algorithm that makes use of both spatial and temporal information. Our algorithm is comprised of (1) a 3-D discrete dyadic wavelet transform carried out on a sequence of images, (2) edge detection that is carried out by maxima detection and a search strategy for maxima curves within the transform space. Using detected edges and "a priori" knowledge of cardiographic features we demonstrate the performance of fully automatic detection of epicardial and endocardial boundaries along the posterior and anterior walls in 2-D short-axis echocardiographic image sequences.>

Introduces a novel approach for accomplishing mammographic feature analysis by overcomplete multiresolution representations. The authors show that efficient representations may be identified within a continuum of scale-space and used to enhance features of importance to mammography. Methods of contrast enhancement are described based on three overcomplete multiscale representations: 1) the dyadic wavelet transform (separable), 2) the phi-transform (nonseparable, nonorthogonal), and 3) the hexagonal wavelet transform (nonseparable). Multiscale edges identified within distinct levels of transform space provide local support for image enhancement. Mammograms are reconstructed from wavelet coefficients modified at one or more levels by local and global nonlinear operators. In each case, edges and gain parameters are identified adaptively by a measure of energy within each level of scale-space. The authors show quantitatively that transform coefficients, modified by adaptive nonlinear operators, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. The authors' results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. They demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology, one can improve chances of early detection while requiring less time to evaluate mammograms for most patients.

This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (D/sub 20/) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D/sub 6/) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.>