DOI: 10.3897/jucs.161202

Authors: Yelda Fırat, Yılmaz Kılıçaslan, Hüseyin Ali Sarıkaya, Murat Kaan Yılmaz

Abstract: Autism Spectrum Disorder is a neurodevelopmental disorder with onset in early childhood and its diagnosis often requires clinical processes based on long, subjective observations. Although early diagnosis and intervention can significantly improve developmental outcomes, existing methods are limited in terms of scalability and objectivity. The aim of this study is to develop a hybrid deep learning model that detects Autism Spectrum Disorder with high accuracy by analyzing motor behaviors from videos of children recorded in their natural home environment. In this study, joint coordinates were extracted using the MediaPipe Pose model and spatial, temporal, frequency and coordination-based features were calculated from these data. The features were processed with a hybrid architecture integrating CNN, BiLSTM and attention mechanism. CNN captured spatial patterns, BiLSTM learned the dynamics over time, and the attention mechanism focused on critical movement segments. The model achieves over 97% accuracy on closed datasets and over 83% on public videos such as YouTube and TikTok. These results show that the method performs robustly under both controlled and real-world conditions. The study provides a scalable, objective and clinically applicable screening tool that overcomes the problems of artificial environments and limited data.

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DOI: 10.3897/jucs.161636

Authors: Wicus J. van der Linden, Trienko L. Grobler, Lynette van Zijl

Abstract: The effect of imbalanced data on the optical character recognition of Braille text is investigated by applying two techniques to a set of convolutional neural network image classification models. A multilabel classification framework is applied to identify the combination of Braille dots present in a character sample. This approach is compared to the multiclass classification framework prevalent in the literature, which directly identifies each sample as one of 64 possible Braille characters. Furthermore, data resampling methods are applied to investigate the impact of class imbalance on the multilabel and multiclass modelling approaches, respectively. The multilabel models are shown to achieve statistically significantly better performance than multiclass models, across different data resampling strategies. This includes better generalisation to out of distribution testing data from different Braille language codes, as well as robust performance under experimental image augmentation conditions. Furthermore, while multiclass models achieve better performance when trained on resampled data compared to training without resampling, this performance increase fails to rival the performance of the multilabel classification models across metrics and resampling strategies.

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DOI: 10.3897/jucs.71645

Authors: Roberto Cavicchioli, Riccardo Martoglia, Micaela Verucchi

Abstract: Exposing city information to dynamic, distributed, powerful, scalable, and user-friendly big data systems is expected to enable the implementation of a wide range of new opportunities; however, the size, heterogeneity and geographical dispersion of data often makes it difficult to combine, analyze and consume them in a single system. In the context of the H2020 CLASS project, we describe an innovative framework aiming to facilitate the design of advanced big-data analytics workflows. The proposal covers the whole compute continuum, from edge to cloud, and relies on a well-organized distributed infrastructure exploiting: a) edge solutions with advanced computer vision technologies enabling the real-time generation of “rich” data from a vast array of sensor types; b) cloud data management techniques offering efficient storage, real-time querying and updating of the high-frequency incoming data at different granularity levels. We specifically focus on obstacle detection and tracking for edge processing, and consider a traffic density monitoring application, with hierarchical data aggregation features for cloud processing; the discussed techniques will constitute the groundwork enabling many further services. The tests are performed on the real use-case of the Modena Automotive Smart Area (MASA).

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DOI: 10.3897/jucs.2020.025

Authors: Kamil Maliński, Krzysztof Okarma

Abstract: Rapid growth of availability of modern electronic and robotic solutions, also for home and amateur use, related to the progress in home automation and popularity of the IoT systems, makes it possible to develop some unique hardware solutions, also by independent researchers and engineers, often with the help of the 3D printing technology. Although in many industrial applications high speed pick and place machines are used for assembling small surface-mount devices (SMD), especially in mass production of electronic parts, there are still some applications, where the traditional through-hole technology used in Printed Circuit Boards (PCB) is utilised, particularly considering some mechanical, thermal or power conditions, preventing the use of the SMD technology. One of the possibilities of supporting such types of production and prototyping, in some cases supported by relatively less sophisticated robotic solutions, may be the application of vision systems, making it possible to classify and recognize some electronics parts with the use of shape analysis of their packages as well as further optical recognition of markings. Another application of such methods may be related to the automatic vision based verification of the assembling quality and correctness of the placement of electronic parts after completing the production. In the paper some experimental results, obtained using various shape descriptors for the classification of electronic packages, are presented. The initial experiments, obtained for a prepared dedicated database of synthetic images, have been verified and confirmed also for some natural images, leading to promising results.

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DOI: 10.3897/jucs.2020.014

Authors: Damir Krstinić, Maja Braović, Dunja Božić-Štulic

Abstract: Natural landscape image classification is a difficult problem in computer vision. Many classes that can be found in such images are often ambiguous and can easily be confused with each other (e.g. smoke and fog), and not just by a computer algorithm, but by a human as well. Since natural landscape video surveillance became relatively pervasive in recent years, in this paper we focus on the classification of natural landscape images taken mostly from forest fire monitoring towers. Since these images usually suffer from the lack of the usual low and middle level features (e.g. sharp edges and corners), and since their quality is degraded by atmospheric conditions, this makes the already difficult problem of natural landscape classification even more challenging. In this paper we tackle the problem of automatic natural landscape classiffication by proposing and evaluating a classifier based on a pretrained deep convolutional neural network and transfer learning.

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DOI: 10.3217/jucs-025-06-0683

Authors: Michał Sypetkowski, Grzegorz Sarwas, Tomasz Trzciński

Abstract: In this paper, we present an approach for football players pose estimation on very low-resolution images. The camera recording the football match is far away from the pitch in order to register at least half of it. As a result, even using very high resolution cameras, the image area presenting every single player is very small. Additionally, variable weather conditions or shadows and reflections, make this aim very hard. Such images are very hard to annotate by human. In our research we assume lack of manually annotated training data from our target distribution. Instead of manual annotation of large dataset, we create simple python script for rendering synthetic images with perfect annotations. Then we train vanilla CycleGAN (Cycle-consistent Generative Adversarial Networks) for transformation of raw synthetic images into more realistic. We use transformed images to train CPN (Cascaded Pyramid Networks) model. Without bells and whistles, we achieve similar precision on our images as the same CPN model trained with COCO (Common Objects in Context) keypoints dataset.

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DOI: 10.3217/jucs-021-06-0856

Authors: Alejandro Zambrano, Carlos Toro, Marcos Nieto, Ricardo Sotaquira, Cesar Sanín, Edward Szczerbicki

Abstract: This paper proposes a service-oriented architecture for video analysis which separates object detection from event recognition. Our aim is to introduce new tools to be considered in the pathway towards Cognitive Vision as a support for classical Computer Vision techniques that have been broadly used by the scientific community. In the article, we particularly focus in solving some of the reported scalability issues found in current Computer Vision approaches by introducing an experience based approximation based on the Set of Experience Knowledge Structure (SOEKS). In our proposal, object detection takes place client-side, while event recognition takes place server-side. In order to implement our approach, we introduce a novel architecture that aims at recognizing events defined by a user using production rules (a part of the SOEKS model) and the detections made by the client using their own algorithms for visual recognition. In order to test our methodology, we present a case study, showing the scalability enhancements provided.

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DOI: 10.3217/jucs-017-06-0926

Authors: Marcin Piekarczyk, Marek Ogiela

Abstract: One important subject associated with personal authentication capabilities is the analysis of handwritten signatures. Among the many known techniques, algorithms based on linguistic formalisms are also possible. However, such techniques require a number of algorithms for intelligent image analysis to be applied, allowing the development of new solutions in the field of personal authentication and building modern security systems based on the advanced recognition of such patterns. The article presents the approach based on the usage of syntactic methods for the static analysis of handwritten signatures. The graph linguistic formalisms applied, such as the IE graph and ETPL(k) grammar, are characterised by considerable descriptive strength and a polynomial membership problem of the syntactic analysis. For the purposes of representing the analysed handwritten signatures, new hierarchical (two-layer) HIE graph structures based on IE graphs have been defined. The two-layer graph description makes it possible to take into consideration both local and global features of the signature. The usage of attributed graphs enables the storage of additional semantic information describing the properties of individual signature strokes. The verification and recognition of a signature consists in analysing the affiliation of its graph description to the language describing the specimen database. Initial assessments display a precision of the method at a average level of under 75%.

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DOI: 10.3217/jucs-016-10-1269

Authors: Karsten Scheibe, Fay Huang, Reinhard Klette

Abstract: Sensor-line cameras have been designed for space missions in the 1980s, and are used for various tasks, including panoramic imaging. Laser range-finders are able to generate dense depth maps (of isolated surface points). Panoramic sensor-line cameras and laser range-finders may both be implemented as rotating sensors, and we used them together this way to reconstruct accurately 3D environments (such as, for example, large buildings). This article reviews related developments, followed by a detailed description of designed calibration and pose estimation techniques which have been used for both rotating sensors. Related experiments evaluate the accuracy of calibrated sensor parameters and of estimated poses.

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DOI: 10.3217/jucs-016-06-0903

Authors: Lucas Terissi, Juan Gómez

Abstract: Algorithms for 3D head pose and facial expression tracking using a single camera (monocular image sequences) is presented in this paper. The proposed method is based on a combination of feature-based and model-based approaches for pose estimation. A generic 3D face model, which can be adapted to any person, is used for the tracking. In contrast to other methods in the literature, the proposed method does not require a training stage. It only requires an image of the person's face to be tracked facing the camera to which the model is fitted manually through a graphical user interface. The algorithms were evaluated perceptually and quantitatively with two video databases. Simulation results show that the proposed tracking algorithms correctly estimate the head pose and facial expression, even when occlusions, changes in the distance to the camera and presence of other persons in the scene, occur. Both perceptual and quantitative results are similar to the ones obtained with other methods proposed in the literature. Although the algorithms were not optimized for speed, they run near real time. Additionally, the proposed system delivers separate head pose and facial expression information. Since information related with facial expression, which is represented only by six parameters, is independent from head pose information, the tracking algorithms could also be used for facial expression analysis and video-driven facial animation.

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DOI: 10.3217/jucs-015-18-3325

Authors: Shyama Chowdhury, Soumyadeep Dhar, Karen Rafferty, Amit Das, Bhabatosh Chanda

Abstract: The importance and use of text extraction from camera based coloured scene images is rapidly increasing with time. Text within a camera grabbed image can contain a huge amount of meta data about that scene. Such meta data can be useful for identification, indexing and retrieval purposes. While the segmentation and recognition of text from document images is quite successful, detection of coloured scene text is a new challenge for all camera based images. Common problems for text extraction from camera based images are the lack of prior knowledge of any kind of text features such as colour, font, size and orientation as well as the location of the probable text regions. In this paper, we document the development of a fully automatic and extremely robust text segmentation technique that can be used for any type of camera grabbed frame be it single image or video. A new algorithm is proposed which can overcome the current problems of text segmentation. The algorithm exploits text appearance in terms of colour and spatial distribution. When the new text extraction technique was tested on a variety of camera based images it was found to out perform existing techniques (or something similar). The proposed technique also overcomes any problems that can arise due to an unconstraint complex background. The novelty in the works arises from the fact that this is the first time that colour and spatial information are used simultaneously for the purpose of text extraction.

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DOI: 10.3217/jucs-015-04-0786

Authors: Andrey Vavilin, Kang-Hyun Jo

Abstract: In this paper we present an approach to detect traffic guidance signs and recognise the structure of junction information on them. The detection algorithm is based on using differently exposed images. These images are combined into one using tone mapping technique in order to minimize effects of bad environment conditions and low dynamic range of CCD-cameras. This technique allows robust sign detection in various lighting conditions. To localize sign candidates color segmentation is used. To minimize number of false detection filtering operations based on geometrical and color properties is applied. Recognition process is based on graph theory. Each sign candidate is decomposed into principal components and the region which represents junction structure is mapped into a graph. This graph is checked for possible mapping mistakes. Finally, the graph is analyzed in order to extract all possible paths of junction crossing. These paths must represent the real structure of the junction and correspond to the road law. The proposed method allows more effective detection in different lighting and environmental conditions such as insufficient or excessive lighting, rain, fog etc compared with conventional approaches.

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DOI: 10.3217/jucs-013-03-0363

Authors: Javier Díaz, Eduardo Ros, Rafael Rodriguez-Gomez, Begoña Pino

Abstract: Motion estimation from image sequences is a complex problem which requires high computing resources and is highly affected by changes in the illumination conditions in most of the existing approaches. In this contribution we present a high performance system that deals with this limitation. Robustness to varying illumination conditions is achieved by a novel technique that combines a gradient-based optical flow method with a non-parametric image transformation based on the Rank transform. The paper describes this method and quantitatively evaluates its robustness to different illumination changing patterns. This technique has been successfully implemented in a real-time system using reconfigurable hardware. Our contribution presents the computing architecture, including the resources consumption and the obtained performance. The final system is a real-time device capable to computing motion sequences in real-time even in conditions with significant illumination changes. The robustness of the proposed system facilitates its use in multiple potential application fields.

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DOI: 10.3217/jucs-012-09-1154

Authors: József Berke, Zsolt Polgar, Zoltán Horváth, Tamás Nagy

Abstract: On the field of potato research and breeding, there are several possibilities for the application of modern digital image processing and data collection/analysing techniques. One of the most obvious methods is the multi/hyper spectral analysis. In our experiments research were done in the visible as well as in the infra, near infra and thermal wavelength. For more advanced analysis we developed a multi/hyper-spectral analysis method (spectral fractal dimension measurement and application). In the following we summarize its basic elements and the developed integrated information system of potato research and breeding.

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