Love it or hate it, the peer review process (whether open, blind, or even double-blind) has become the standard and accepted way of assessing the quality of papers before publication, be it for a conference, journal, or book. Indeed, forming the program committee is an essential part in any conference organisation and a good program committee may well be the differentiator from peer conferences. However, we have all been the recipients of a less than stellar/helpful review: from the snarky ones to the one-liners, these reviews can be demoralising and can give the peer-review process a bad reputation! The scope of this tutorial is then to encourage researchers to become more involved in the peer-review process by joining program committees and encourages good practices to collectively strengthen the quality of the peer-review process.

In recent years, Page Object Detection (POD) has become a popular document understanding task, proving to be a non-trivial task given the potential complexity of documents. The rise of neural networks facilitated a more general learning approach to this task. However, in the literature, the different objects such as formulae, or figures among others, are generally considered individually. In this paper, we describe the joint localisation of six object classes relevant to scientific papers, namely isolated formulae, embedded formulae, figures, tables, variables and references. Through a qualitative analysis of these object classes, we note a hierarchy among the classes and propose a new localisation approach, using two, cascaded You Only Look Once (YOLO) networks. We also present a new data set consisting of labelled bounding boxes for all six object classes. This data set combines two commonly used data sets in the literature for formulae localisation, adding to the document images in these data sets the labels for figures, tables, variables and references. Using this data set, we achieve an average F1-score of 0.755 across all classes, which is comparable to the state-of-the-art for the object classes when considered individually for localisation.

Controlling the distribution of sensitive content such as pornography has become paramount with the ever-growing accessibility to the internet. Manual filtering of such large volumes of data is practically impossible, thus, the automatic detection of said material is sought after by Law Enforcement Agencies (LEAs) and has been tackled in various manners. However, the sorting of flagged pornographic documents is still done manually using scales that describe hierarchical degrees of content severity. In this paper, we address pornography detection by creating a model capable of locating and labelling sexual organs in images and extend this model to perform image classification to provide the user with one of 19 semantically meaningful descriptors of the content. Generating these descriptors serves as a proof of concept before approaching LEAs to work with illegal CSA material and scales such as COPINE. After creating our own custom sexual organ object detection dataset for the task at hand, we achieved an object detection mean average precision score of 63.63% and a top-3 classification accuracy of 87.78%.

Hyperspectral imaging (HSI) is used in analysis of paintings to obtain features hidden to the human eye by selecting specific wavelengths. Superpixel segmentation can be applied to HSI for feature extraction. A superpixel algorithm processes an image in a way in which the result includes an unnecessary amount of over-segmentation. In this work, we use over-segmentation and propose Spectral Similarity Merging (SSM), a region growing algorithm based on homogeneous spectral properties with the aim to reduce over-segmentation without compromising under-segmentation. The algorithm focuses on the similarity of the spectral shapes rather than intensity. Results show an average of 45% reduction in over-segmentation and an average of 53% improvement on the F-score on existing superpixel segmentation algorithms.

Sketches and drawings have an integral role in the communication of ideas and form concepts. In this tutorial we focus on the use of sketches in the design and manufacturing process. Here, the drawing adapts from an initial, rough concept sketch to precise machine-drawings. While several computer-aided design tools exist which support the use of different drawings, the abmiguity that is typically associated with initial sketches often means that the initial concept sketch needs to be redrawn according to the specific interfaces used within these systems. This tutorial discusses the challenges that exist in the interpretation of sketches and presents drawing interpretation techniques that address some of these challenges.

Common Music Notation (CMN) is the well-established foundation for the written communication of musical information, such as rhythm or harmony. CMN suffers from the complexity of its visual encoding and the need for extensive training to acquire proficiency and legibility. While alternative notations using additional visual variables (e.g., color to improve pitch identification) have been proposed, the community does not readily accept notation systems that vary widely from the CMN. Therefore, to support student musicians in understanding harmonic relationships, instead of replacing the CMN, we present a visualization technique that augments digital sheet music with a harmonic fingerprint glyph. Our design exploits the circle of fifths, a fundamental concept in music theory, as visual metaphor. By attaching such glyphs to each bar of a composition we provide additional information about the salient harmonic features available in a musical piece. We conducted a user study to analyze the performance of experts and non-experts in an identification and comparison task of recurring patterns. The evaluation shows that the harmonic fingerprint supports these tasks without the need for close-reading, as when compared to a not-annotated music sheet.

Musical notation is a means of passing on performance instructions with fidelity to others. Composers, however, often introduced embellishments to the music they performed notating these embellishments with symbols next to the relevant notes. In time, these symbols, known as ornaments, and their interpretation became standardized such that there are acceptable ways of interpreting an ornament. Although music books may contain footnotes which express the ornament in full notation, these remain cumbersome to read. Ideally, a music student will have the possibility of selecting ornamented notes and express them as full notation. The student should also have the possibility to collapse the expressed ornament back to its symbolic representation, giving the student the possibility of also becoming familiar with playing from the ornamented score. In this paper, we propose a complete pipeline that achieves this goal. We compare the use of COSFIRE and template matching for optical music recognition to identify and extract musical content from the score. We then express the score using MusicXML and design a simple user interface which allows the user to select ornamented notes, view their expressed notation and decide whether they want to retain the expressed notation, modify it, or revert to the symbolic representation of the ornament. The performance results that we achieve indicate the effectiveness of our proposed approach.

Engineering design makes use of freehand sketches to communicate ideas, allowing designers to externalise form concepts quickly and naturally. Such sketches serve as working documents which demonstrate the evolution of the design process. For the product design to progress, however, these sketches are often redrawn using computer-aided design tools to obtain virtual, interactive prototypes of the design. Although there are commercial software packages which extract the required information from freehand sketches, such packages typically do not handle the complexity of the sketched drawings, particularly when considering the visual cues that are introduced to the sketch to aid the human observer to interpret the sketch. In this paper, we tackle one such complexity, namely the use of shading and shadows which help portray spatial and depth information in the sketch. For this reason, we propose a vectorisation algorithm, based on trainable COSFIRE filters for the detection of junction points and subsequent tracing of line paths to create a topology graph as a representation of the sketched object form. The vectorisation algorithm is evaluated on 17 sketches containing different shading patterns and drawn by different sketchers specifically for this work. Using these sketches, we show that the vectorisation algorithm can handle drawings with straight or curved contours containing shadow cues, reducing the salient point error in the junction point location by 91% of that obtained by the off-the-shelf Harris-Stephen's corner detector while the overall vectorial representations of the sketch achieved an average F-score of 0.92 in comparison to the ground truth. The results demonstrate the effectiveness of the proposed approach.

Digital copies of musical scores may be saved on tablet devices, compressing volumes of scores into a single portable device. Tablet screens are however typically smaller than printed sheet music such that the score needs to be resized for readability. This necessitates additional page turning which is made more complex when repeat instructions are used since these give rise to forward and backward page turns of the music. In this paper, we tackle this problem by first performing image analysis of the score in order to identify repeat instructions and hence flatten the score. Thus, the music player is presented the score as it should be played. We then propose the use of eye-gaze tracking to provide a hands-free page turning mechanism. Thus, the player remains in full control of when the page turn occurs. Through a preliminary study, we found that our proposed score flattening and eye-gaze page turning reduced the time spent navigating the page turns by 47% in comparison to available music score reading tools.

This paper focuses on the identification of hidden lines and junctions from natural sketches of drawings that exhibit an extended-trihedral geometry. Identification of hidden lines and junctions is essential in the creation of a complete 3D model of the sketched object, allowing the interpretation algorithms to infer what the unsketched back of the object should look like. This approach first labels the sketched visible edges of the object with a geometric edge label, obtaining a labelled junction at each of the visible junctions of the object. Using a dictionary of junctions with visible and hidden edges, these labelled visible junctions are then used to deduce the edge interpretation and orientation of some of the hidden edges. A genetic algorithm is used to combine these hidden edges into hidden junctions, evolving the representation of the hidden edges and junctions until a feasible hidden view representation of the object is obtained.