DOI: 10.3897/jucs.130186

Authors: Yang Zhang, Ziwen Wei, Zhihua Liu, Xiaolong Wu, Junchao Qian

Abstract: Purpose: Incorrect patient positioning during radiotherapy can significantly impact treatment efficacy and pose potential risks. This study aims to develop a model that can rapidly and effectively monitor the patient’s postures during radiotherapy sessions using real-time video. Methods: The neural network utilized in this research employed a two-stream architecture, consisting of spatial and temporal streams. For the spatial stream, RGB frames from the videos were directly used as input. In the temporal stream, representative frames were extracted from the video to construct stacked grayscale 3-channel images (SG3I) frames. This approach enabled capturing motion information through a large-scale dataset pre-trained 2D convolutional neural network (CNN), eliminating the need for computationally expensive optical flow calculations. Additionally, an improved lightweight network architecture was employed. The model was trained and tested using volunteer videos collected from a radiotherapy center in a hospital. Results: The results demonstrated that the proposed model outperforms existing methods in terms of detection accuracy while achieving higher efficiency in frame generation. Conclusion: In this study, we introduced a cost-effective and highly accurate method for recognizing patient’s postures during radiotherapy. This approach could be readily deployed in any radiotherapy facility, ensuring treatment precision and patient safety.

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DOI: 10.3897/jucs.104490

Authors: Petra Grd, Igor Tomičić, Ena Barčić

Abstract: The classification of human face shapes, a pivotal aspect of one’s appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation for accurate feature extraction. Our methodology utilizes a publicly available dataset of female celebrities, comprising five face shape classes: heart, oblong, oval, round, and square. By augmenting this dataset during training, we magnify its diversity, enabling better generalization and enhancing the model’s robustness. With the EfficientNetV2S neural network, we employ transfer learning, leveraging pretrained weights to optimize accuracy, training speed, and parameter size. The result is a highly efficient and effective model, which outperforms state-of-the-art approaches on the same dataset, boasting an outstanding overall accuracy of 96.32%. Our findings demonstrate the efficiency of our approach, proving its potential in the field of face shape classification. The success of our methodology holds promise for various applications, offering valuable insights into beauty analysis, cosmetic recommendations, and personalized healthcare.

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DOI: 10.3897/jucs.94183

Authors: Burak Gülmez

Abstract: Plants are a big part of the ecosystem. Plants are also used by humans for various purposes. Cotton is one of these important plants and is very critical for humans. Cotton production is one of the most important sources of income for many countries and farmers in the world. Cotton can get diseases like other plants and living things. Detecting these diseases is critical. In this study, a model is developed for disease detection from leaves of cotton. This model determines whether the cotton is healthy or diseased through the photograph. It is a deep convolutional neural network model. While establishing the model, care is taken to ensure that it is a problem-specific model. The grey wolf optimization algorithm is used to ensure that the model architecture is optimal. So, this algorithm will find the most efficient architecture. The proposed model has been compared with the ResNet50, VGG19, and InceptionV3 models that are frequently used in the literature. According to the results obtained, the proposed model has an accuracy value of 1.0. Other models had accuracy values of 0.726, 0.934, and 0.943, respectively. The proposed model is more successful than other models.

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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-025-06-0611

Authors: Dominik Pieczyński, Marek Kraft, Michał Fularz

Abstract: In this paper, a method for improving the quality of person re-identification results is presented. The method is based on the assumption, that including segmentation information into re-identi_cation pipeline discards the automated detections that are of poor quality due to occlusions, misplaced regions of interest (ROI), multiple persons found within a single ROI, etc. using a simple segment number, bounding box fill rate and aspect ratio check. Assuming that a joint detector-segmented approach is used, the additional cost associated with the use of the proposed approach is very low.

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DOI: 10.3217/jucs-024-04-0357

Authors: Marcin Gabryel

Abstract: Algorithms from the field of computer vision are widely applied in various fields including security, monitoring, automation elements, but also in multimodal human-computer interactions where they are used for face detection, body tracking and object recognition. Designing algorithms to reliably perform these tasks with limited computing resources and the ability to detect the presence of nearby people and objects in the background, changes in illumination and camera pose is a huge challenge for the field. Many of these problems use different classification methods. One of many image classification algorithms is Bag-of-Words (BoW). Originally, the classic BoW algorithm was used mainly for the natural language, so its direct application to computer vision issues may not be effective enough. The algorithm presented in this article contains a number of modifications that facilitate application of many types of characteristic features extracted from an image, image representation analysis and an adaptive clustering algorithm to create a dictionary of image features. These modifications affect classification result, which was confirmed in the experimental research.

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DOI: 10.3217/jucs-018-20-2832

Authors: Daewon Kim, Doosung Hwang

Abstract: As the augmented reality technology has become more pervasive and applicable, it is easily seen in our daily lives regardless of fields and scopes. Existing camera vision based augmented reality techniques depend on marker based approaches rather than real world information. The augmented reality technology using marker recognition has limitations in its applicability and provision of proper environment to guarantee user's immersiveness to relevant service application programs. This study aims to implement a smart mobile terminal based augmented reality technology by using a camera built in a terminal device and image and video processing technology without any markers so that users can recognize multimedia objects from real world images and build an augmented reality service, where 3D content connected to objects and relevant information are added to the real world image. Object recognition from a real world image is involved in a process of comparison against preregistered reference information, where operation to measure similarity is reduced for faster running of the application, considering the characteristics of smart mobile devices. Furthermore, the design allows users to interact through touch events on the smart device after 3D content is output onto the terminal screen. Afterward, users can browse object related information on the web. The augmented reality technology appropriate for the smart mobile environment is proposed and tested through several experiments and showed reliable performances in the results.

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DOI: 10.3217/jucs-018-09-1177

Authors: Xingang Liu, Jiantan Liu, Kook-Yeol Yoo, Haengrae Cho

Abstract: For the Wireless Multimedia Sensor Network (WMSN), the intraframe video coding is widely used for the robust transmission and computation complexity. Though the intraframe algorithm requires much smaller computational complexity than the interframe coding, the amount of computation of the intraframe should be reduced to use WMSN application. In this paper, we propose an intra mode decision algorithm to reduce the computation complexity of intraframe H.264/AVC encoders. The proposed algorithm determines the candidate modes and skips the remaining modes based on the smoothness and directional similarity of MB. The simulation results show that the proposed algorithm achieves 18% to 70% reduction in the computational complexity, compared with the various conventional methods.

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DOI: 10.3217/jucs-017-01-0081

Authors: Rakesh Rampalli, Angarai Ramakrishnan

Abstract: This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

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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-014-02-0284

Authors: Wafa Boussellaa, Abderrazak Zahour, Adel Alimi

Abstract: This paper presents a new color document image segmentation system suitable for historical Arabic manuscripts. Our system is composed of a hybrid method which couple together background light intensity normalization algorithm and k-means clustering with maximum likelihood (ML) estimation, for foreground/ background separation. Firstly, the background normalization algorithm performs separation between foreground and background. This foreground is used in later steps. Secondly, our algorithm proceeds on luminance and distort the contrast. These distortions are corrected with a gamma correction and contrast adjustment. Finally, the new enhanced foreground image is segmented to foreground/background on the basis of ML estimation. The initial parameters for the ML method are estimated by k-means clustering algorithm. The segmented image is used to produce a final restored document image. The techniques are tested on a set of Arabic historical manuscripts documents from the National Tunisian Library. The performance of the algorithm is demonstrated on by real color manuscripts distorted with show-through effects, uneven background color and localized spot

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DOI: 10.3217/jucs-013-03-0440

Authors: José Fernández-Pérez, Francisco Sánchez-Fernández, Ricardo Carmona-Galán

Abstract: This paper presents a survey of the characteristics of a vision system implemented in a reconfigurable/programmable chip (FPGA). System limitations and performance have been evaluated in order to derive specifications and constraints for further vision system synthesis. The system hereby reported has a conventional architecture. It consists in a central microprocessor (CPU) and the necessary peripheral elements for data acquisition, data storage and communications. It has been designed to stand alone, but a link to the programming and debugging tools running in a digital host (PC) is provided. In order to alleviate the computational load of the central microprocessor, we have designed a visual co-processor in charge of the low-level image processing tasks. It operates autonomously, commanded by the CPU, as another system peripheral. The complete system, without the sensor, has been implemented in a single reconfigurable chip as a SOPC. The incorporation of a dedicated visual co-processor, with specific circuitry for low-level image processing acceleration, enhances the system throughput outperforming conventional processing schemes. However, time-multiplexing of the dedicated hardware remains a limiting factor for the achievable peak computing power. We have quantified this effect and sketched possible solutions, like replication of the specific image processing hardware.

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DOI: 10.3217/jucs-012-10-1426

Authors: Limin Liu, Tian-Shyr Dai

Abstract: Fingerprint image enhancement is a common and critical step in fingerprint recognition systems. To enhance the images, most of the existing enhancement algorithms use filtering techniques that can be categorized into isotropic and anisotropic according to the filter kernel. Isotropic filtering can properly preserve features on the input images but can hardly improve the quality of the images. On the other hand, anisotropic filtering can effectively remove noise from the image but only when a reliable orientation is provided. In this paper, we propose a ridge orientation estimation and verification algorithm which can not only generate an orientation of ridge flows, but also verify its reliability. Experimental results show that, on average, over 51 percent of an image in the NIST-4 database has reliable orientations. Based on this algorithm, a hybrid fingerprint enhancement algorithm is developed which applies isotropic filtering on regions without reliable orientations and anisotropic filtering on regions with reliable orientations. Experimental results show the proposed algorithm can combine advantages of both isotropic and anisotropic filtering techniques and generally improve the quality of fingerprint images.

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