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13th International Computer Engineering conference (ICENCO)

作者： Elrefaey, Amany Sarhan, Amany El-Shennawy, Nada M. Tanta Univ Fac Engn Comp Engn & Control Engn Dept Tanta Egypt

ISBN: (纸本)9781538642665

Data encryption is crucial for protecting confidential information on the internet. However, the rapid growth of multimedia based communication and real-time systems requires similar improvement in the performance of the encryption algorithms to keep pace with this growth. In this paper, we propose a new parallel implementation for a Chaotic-based image encryption method. The proposed method exploits the capabilities of the parallel processing environments in improving the performance of Chaotic-based encryption algorithms. The proposed parallel implementations are evaluated using different images of different sizes to ensure its validity. The performance is compared to the original serial implementation of the same algorithm showing that the performance is highly improved compared to the original sequential version of the same algorithm.

关键词： image encryption Chaotic maps Parallel processing Baker Map Chen diffusion

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NIR Camera Based Mobile Seat Belt Enforcement System Using Deep Learning Techniques

NIR Camera Based Mobile Seat Belt Enforcement System Using D...

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International IEEE conference on Signal-image Technologies and Internet-Based System

作者： Burak Balci Bensu Alkan Alperen Elihos Yusuf Artan Image & Video Processing Dept. Havelsan Inc. Ankara Turkey

ISBN: (纸本)9781538693865;9781538693858

Camera based traffic enforcement solutions have been ubiquitously employed in many countries around the world. Many companies offer camera based solutions towards toll violation, seat belt violation, red light violation, and speed violation enforcement. These systems are typically mounted on a fixed platform to hold the camera and the illuminator. However, installation of these fixed platforms is costly and their operation range is limited to the surrounding area of the installation. In this study, we propose a mobile seat belt enforcement system that can operate on mobile police vehicles. Proposed system utilizes deep learning algorithms towards the detection of the most common traffic violation; not wearing a seat belt while driving. Using a camera system placed on top of a vehicle, real world images are captured during day and night time. We conducted experiments using a test set containing 2600 real world images and achieved an overall accuracy of 83% on seat belt detection task.

关键词： Belts Cameras Deep learning image classification Detectors Training Vehicles

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Bio-Inspired Night image Enhancement Based on Contrast Enhancement and Denoising 1

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3rd International conference on Cognitive systems and Information processing (ICCSIP)

作者： Bai, Xinyi Priyanka, Steffi Agino Tung, Hsiao-Jung Wang, Yuankai Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Fu Jen Catholic Univ New Taipei Taiwan

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

Due to the low accuracy of object detection and recognition in many intelligent surveillance systems at nighttime, the quality of night images is crucial. Compared with the corresponding daytime image, nighttime image is characterized as low brightness, low contrast and high noise. In this paper, a bio-inspired image enhancement algorithm is proposed to convert a low illuminance image to a brighter and clear one. Different from existing bio-inspired algorithm, the proposed method doesn't use any training sequences, we depend on a novel chain of contrast enhancement and denoising algorithms without using any forms of recursive functions. Our method can largely improve the brightness and contrast of night images, besides, suppress noise. Then we implement on real experiment, and simulation experiment to test our algorithms. Both results show the advantages of proposed algorithm over contrast pair, Meylan and Retinex.

关键词： Bio-inspired image enhancement algorithm Nighttime image Contrast enhancement Denoising Intelligent surveillance system

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Intelligent Maze Solving Robot Based on image processing and Graph Theory algorithms

Intelligent Maze Solving Robot Based on Image Processing and...

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International conference on Promising Electronic Technologies (ICPET)

作者： Aqel, Mohammad O. A. Issa, Ahmed Khdair, Mohammed ElHabbash, Majde AbuBaker, Mohammed Massoud, Mohammed Al Azhar Univ Gaza Fac Engn & Informat Technol Dept Engn POB 1277 Gaza Palestine

ISBN: (纸本)9781538622698

The most important task for maze solving robots is the fast and reliable finding of its shortest path from its initial point to its final destination point. This paper proposes an intelligent maze solving robot that can determine its shortest path on a line maze based on image processing and artificial intelligence algorithms. The image of the line maze is captured by a camera and sent to the computer to be analyzed and processed by a program developed using Visual C++ and OpenCV libraries and based on graph theory algorithms. The developed program solves the captured maze by examining all possible paths exist in the maze that could convey the robot to the required destination point. After that, the best shortest path is determined and then the instructions that guide the car-like robot to reach its desired destination point are sent to the robot through Bluetooth. The robot follows the received guide path to reach its destination. The proposed approach works faster than the traditional methods which push the robot to move through the maze cell by cell in order to find its destination point. Moreover, the proposed method allows the maze solving robot to avoid trapping and falling in infinity loops. Applications of maze solving systems include intelligent traffic control that helps ambulances, fire fighters, or rescuing robots to find their shortest path to their destination.

关键词： maze solving artificial intelligence autonomous robotic graph theory algorithm image processing

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An Iterative Algorithm for image Inpainting Using Aggregation Functions 17

An Iterative Algorithm for Image Inpainting Using Aggregatio...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Gonzalez-Hidalgo, Manuel Massanet, Sebastia Mir, Amau Ruiz-Aguilera, Daniel Univ Balearic Isl SCOPIA Res Grp Dept Math & Comp Sci Cira ValldemossaKm 7-5 E-07122 Palma De Mallorca Balearic Island Spain

ISBN: (纸本)9781509049172

image inpainting is a prolific line of research due to its applications in restoration of missing or damaged areas of the image. In this paper, a novel iterative algorithm for image inpainting based on aggregation functions and penalty-based functions is presented. The algorithm combines diffusion-based and patch-based techniques. In each iteration of the algorithm, the level of consensus among the known pixels in a neighbourhood of each pixel is computed. If a minimum value of consensus is reached, the pixel is recovered by means of an aggregation of the known pixels of the neighbourhood through aggregation functions and penalty-based functions. Otherwise, a non-local search of similar patches is performed and then a similar aggregation but now of the centre pixels of those patches more similar to the region we must recover is carried out. Experiments on synthetic and natural images show the potential of this algorithm both from the qualitative and the quantitative points of view in comparison to other classical inpainting algorithms.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Methods of spatial and temporal processing of images in optoelectronic control systems

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IOP conference Series: Earth and Environmental Science 2019年第1期378卷

作者： V E Makhov S E Shaldaev A.F. Mozhaisky Military Space Academy 13 Zhdanovskaya Str. St. Petersburg 197082 Russia

The data about the effect of image quality on accuracy of information parameters are presented. Studies on accuracy of measuring characteristics of objects in their images depending on image quality are described. Studies showed that accuracy of measurement algorithms improves with an increase in sample sizes of frames in the optoelectronic system (OES). image quality is improved by processing the frame samples with algorithms for increasing resolution and reducing noise levels, both in single-point and multi-position OESs based on digital cameras with matrix photodetectors. The maximum effect of an increase in information content of the image and accuracy of measuring algorithms is determined by variations in the pixel structure of each image in the region of brightness gradient boundaries. In the single-point OESs which ensure high quality of optical images without noise factors and variations of other external parameters, the optimal number of frames in the OES is four.

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The Role of Optical Flow in Automated Quality Assessment of Full-Motion Video 40

The Role of Optical Flow in Automated Quality Assessment of ...

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conference on Applications of Digital image processing XL

作者： Harguess, Josh Shafer, Scott Marez, Diego Space & Naval Warfare Syst Ctr Pacific 53560 Hull St San Diego CA 92152 USA

ISBN: (数字)9781510612501

ISBN: (纸本)9781510612501;9781510612495

In real-world video data, such as full-motion-video (FMV) taken from unmanned vehicles, surveillance systems, and other sources, various corruptions to the raw data is inevitable. This can be due to the image acquisition process, noise, distortion, and compression artifacts, among other sources of error. However, we desire methods to analyze the quality of the video to determine whether the underlying content of the corrupted video can be analyzed by humans or machines and to what extent. Previous approaches have shown that motion estimation, or optical flow, can be an important cue in automating this video quality assessment. However, there are many different optical flow algorithms in the literature, each with their own advantages and disadvantages. We examine the effect of the choice of optical flow algorithm (including baseline and state-of-the-art), on motion based automated video quality assessment algorithms.

关键词： Video Optical flow image quality Video compression Cameras Motion estimation Video surveillance image compression image processing

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The Structure of Many-Valued Relations iii. Tied Relational systems

The Structure of Many-Valued Relations III. Tied Relational ...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Della Stella, Maria Emilia Guido, Cosimo Ruzittu, Laura Univ Salento Dept Math & Phys Lecce Italy

In this paper fuzzy (tied) relational systems are considered which are the objects of semicategories whose morphisms constitute a general variable-basis approach to fuzzy Galois connections and conjugated pairs. Usefu... 详细信息

ISBN: (纸本)9781509060344

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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image Vignetting Reduction via a Maximization of Fuzzy Entropy

Image Vignetting Reduction via a Maximization of Fuzzy Entro...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Lopez-Fuentes, Laura Massanet, Sebastia Gonzalez-Hidalgo, Manuel Univ Balear Islands UIB Dept Math & Comp Sci SCOPIA Res Grp E-07122 Palma De Mallorca Spain AnsuR Technol Martin Linges Vei 25 N-1364 Fornebu Norway Autonomous Univ Barcelona UAB CVC Campus UAB Barcelona 08193 Spain

ISBN: (纸本)9781509060344

In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Using Trainable Segmentation and Watershed Transform for Identifying Unilocular and Multilocular Cysts from Ultrasound images of Ovarian Tumour

Using Trainable Segmentation and Watershed Transform for Ide...

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conference on Mobile Multimedia/image processing, Security, and Applications

作者： Ibrahim, Dheyaa Ahmed Al-Assam, Hisham Du, Hongbo Jassim, Sabah Univ Buckingham Dept Appl Comp Buckingham England

ISBN: (数字)9781510609440

ISBN: (纸本)9781510609433;9781510609440

Ovarian masses are categorised into different types of malignant and benign. In order to optimize patient treatment, it is necessary to carry out pre-operational characterisation of the suspect ovarian mass to determine its category. Ultrasound imaging has been widely used in differentiating malignant from benign cases due to its safe and non-intrusive nature, and can be used for determining the number of cysts in the ovary. Presently, the gynaecologist is tasked with manually counting the number of cysts shown on the ultrasound image. This paper proposes, a new approach that automatically segments the ovarian masses and cysts from a static B-mode image. Initially, the method uses a trainable segmentation procedure and a trained neural network classifier to accurately identify the position of the masses and cysts. After that, the borders of the masses can be appraised using watershed transform. The effectiveness of the proposed method has been tested by comparing the number of cysts identified by the method against the manual examination by a gynaecologist. A total of 65 ultrasound images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual counting method for accurately determining the number of cysts in a US ovarian image.

关键词： image segmentation Ultrasonography image processing algorithms and systems Neural networks image processing Ovarian cancer Binary data Evolutionary algorithms Tumor growth modeling Cancer Computing systems Diagnostics image analysis Machine learning Medical imaging

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