Digital Signal and Image Processing

— In the last few years exploitation of underwater gas and oil fields have increased. The produced oil and gas is transported mainly using pipelines which need to be inspected regularly. Submarine communication cables are laid on the sea floor for data communication across stretches of ocean. Due to earthquakes and other environmental changes these underwater cables and pipelines can get wear or get damaged. These damages need to be found and repaired quickly. Also the state of the pipelines and cables need to be constantly monitored. Currently inspection of underwater pipelines are done using Remotely Operated Vehicles (ROVs) which require human intervention. But this method is very risky. A more practical solution is to develop an intelligent vision based navigation and guidance system which involves efficient method for vision based target detection and tracking methods in underwater environment. AUVs used for survey missions in an underwater environment requires advanced precision navigation systems. Navigation usually requires high speed and high accuracy computation. Navigation is done through different techniques of which vision based navigation is the cheapest as it requires only a single camera. This paper presents a review on various AUV navigation techniques which uses vision sensor or any combination of vision sensor with any other sensor for navigation over the last decade.

As the Internet grows at a phenomenal rate email systems has become a widely used electronic form of communication. Everyday, a large number of people exchange messages in this fast and inexpensive way. With the excitement on electronic commerce growing, the usage of email will increase more exponential. In this paper we present our research in developing general method for intrusion detection in email system Internet-based. The main ideas are to use data mining techniques to discover consistent and useful patterns of email system that can recognize anomalies and known intrusions.

Multimodal Image Fusion techniques combine information from different sensors together to produce a more accurate and efficient representation which is more useful for further analysis. A Multi resolution based Multimodal Image Fusion Algorithm is this paper, an automatic algorithm based on multi resolution technique for fusing multimodal images is proposed. The multimodal images are decomposed using the Dual Tree Complex Wavelet Transform and Contourlet Transform and they are fused using some efficient and robust fusion rules. Finally, they are reconstructed using the Inverse transform and a new fused image with more information content is obtained. The basic idea of all multiresolution fusion schemes is motivated by the human visual system being primarily sensitive to local contrast changes, e.g. the edges or corners. These techniques also provide better directional information. The DT-CWT and Contourlet Transform methods are good at faithfully retaining the salient structural information present in both the multimodal images.

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we investigate the discriminative power of colour-based invariants in the presence of large illumination changes between training and test data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) we employ a more sophisticated photometric model of the camera and show how its parameters can be estimated, (ii) we derive several novel colour-based face invariants, and (iii) on a large database of video sequences we examine and evaluate the largest number of colour-based representations in the literature. Our results suggest that colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images.

In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF). OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc. The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels.

Dynamically changing background ("dynamic background") still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: that of swaying tree branches and their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new dataset suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected dataset, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

8th International Conference on Computer Science and Engineering (CSEN 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computing. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on Computer science & Computer Engineering advancements and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Computer Science & Computer Engineering.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science & Engineering

INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and
many algorithms and filters have been planned to improve the image information. There are various noise removal
procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the
overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising
algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF).
OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF
methods which are effective, efficient for denoising various kinds of images.
To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of
various degrees of noise in the image.
To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its
degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc.
METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising
algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels
in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt &
pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF,
UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained
for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved
from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for
lower to higher image noise densities levels.

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to using low-level, spatio-temporally local motion features only. This particular challenge arises in many semi-automatic surveillance systems which alert a human operator to potential abnormalities in the scene. Low-level, spatio-temporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions: (i) we describe a dense algorithm for perspective estimation which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate, (ii) we also present an alternative coarse algorithm which subdivides the image frame into blocks, and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme, and (iii) we report the results of an evaluation using nine large sets acquired using existing close-circuit television (CCTV) cameras, not installed specifically for the purposes of this work. Our findings demonstrate that both of the proposed methods are successful, their accuracy matching that of human labelling using complete visual data (by the constraints of the setup unavailable to our algorithms).

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

Electrocardiogram (ECG) is commonly used in medical field in order to diagnose different cardiovascular diseases and patient's general health. ECG Signal are often affected by artifacts from the environment including baseline wander, power line interference and electromyographic (EMG) noise. In this project, ECG Signal to be processed is collected from MIT/BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrythmia Database [1]. This method is being tested on one dataset only and serves as a beginner guide to digital signal processing with MATLAB for an affordable, precise, and portable Electrocardiogram device that may be used at underserved hospitals. With MATLAB, to remove the baseline wander, Detrend function can be used to remove low frequency below 0.5 Hz. Powerline interference (50 Hz or 60 Hz) can be filtered out by using bandstop IIR filter. EMG and high frequency noise can be reduced by using IIR low-pass filter to cut off high frequency signal. The resulting signal is filtered out successfully with less noise and P wave, QRS complex, T wave can be inferred properly.

Due to big data progress in biomedical and healthcare communities, accurate study of medical data benefits early disease recognition, patient care and community services. When the quality of medical data is incomplete the exactness of study is reduced. Moreover, different regions exhibit unique appearances of certain regional diseases, which may results in weakening the prediction of disease outbreaks. In the proposed system, it provides machine learning algorithms for effective prediction of various disease occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital data collected. To overcome the difficulty of incomplete data, it use a latent factor model to rebuild the missing data. It experiment on a regional chronic illness of cerebral infarction. Using structured and unstructured data from hospital it use Machine Learning Decision Tree algorithm and Map Reduce algorithm. To the best of our knowledge in the area of medical big data analytics none of the existing work focused on both data types. Compared to several typical estimate algorithms, the calculation exactness of our proposed algorithm reaches 94.8% with a convergence speed which is faster than that of the CNN-based unimodal disease risk prediction (CNN-UDRP) algorithm.

In this paper, we present a new complex electronic system for facilitating communication with severely disabled patients and telemonitoring their physiological parameters. The proposed assistive system includes three subsystems (Patient, Server, and Caretaker) connected to each other via the Internet. The two-way communication function is based on keywords technology using a WEB application implemented at the server level, and the application is accessed remotely from the patient’s laptop/tablet PC. The patient’s needs can be detected by using different switch-type sensors that are adapted to the patient’s physical condition or by using eye-tracking interfaces. The telemonitoring function is based on a wearable wireless sensor network, organized around the Internet of Things concept, and the sensors acquire different physiological parameters of the patients according to their needs. The mobile Caretaker device is represented by a Smartphone, which uses an Android application for communicating with patients and performing real-time monitoring of their physiological parameters.

The thesis concentrates on computational methods pertaining to ancient ostraca - ink on clay inscriptions, written in Hebrew. These texts originate from the biblical kingdoms of Israel and Judah, and dated to the late First Temple period (8th – early 6th centuries BCE). The ostraca are almost the sole remaining epigraphic evidence from the First Temple period and are therefore important for archaeological, historical, linguistic, and religious studies of this era. This “noisy” material offers a fertile ground for the development of various “robust” image analysis, image processing, computer vision and machine learning methods, dealing with the challenging domain of ancient documents’ analysis. The common procedures of modern epigraphers involve manual and labor-intensive steps, facing the risk of unintentionally mixing documentation with interpretation. Therefore, the main goal of this study is establishing a computerized paleographic framework for handling First Temple period epigraphic material. The major research questions, addressed in this thesis are: quality evaluation of manual facsimiles; quality evaluation of ostraca images; automatic binarization of the documents and its subsequent refinement; quality evaluation of binarizations on global and local levels; identification of different writers between inscriptions (two distinct methods are proposed); image segmentation (with improvements over the classical Chan-Vese algorithm); and letters’ shape prior estimation. The developed methods were tested on real-world archaeological and modern data and their results are found to be favorable.

Digital image processing is an area characterized by the need for extensive experimental work to establish the viability of
proposed solutions to a given problem. An important characteristic underlying the design of image processing systems is the
significant level of testing & experimentation that normally is required before arriving at an acceptable solution. This
characteristic implies that the ability to formulate approaches & quickly prototype candidate solutions generally plays a major
role in reducing the cost & time required to arrive at a viable system implementation.
An image may be defined as a two-dimensional function f (x, y), where x & y are spatial coordinates, & the amplitude off at any
pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y & the amplitude values of f
are all finite discrete quantities, we call the image a digital image. The field of DIP refers to processing digital image by means
of digital computer. The digital image is composed of a finite number of elements, each of which has a particular location &
value. The elements are called pixels.
Vision is the most advanced of our sensor, so it is not surprising that image plays the single most important role in human
perception. However, unlike humans, who are limited to the visual band of the EM spectrum imaging machines cover almost the
entire EM spectrum, ranging from gamma to radio waves. They can operate also on images generated by sources that humans
are not accustomed to associating with image.
There is no general agreement among authors regarding where image processing stops & other related areas such as image
analysis& computer vision start. Sometimes a distinction is made by defining image processing as a discipline in which both the
input & output of a process are images. This is limiting & somewhat artificial boundary. The area of image analysis (image
understanding) is in between image processing & computer vision.
There are no clear-cut boundaries in the continuum from image processing at one end to complete vision at the other. However,
one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, & high-level processes.
Low-level process involves primitive operations such as image processing to reduce noise, contrast enhancement & image
sharpening. A low- level process is characterized by the fact that both its inputs & outputs are images. Mid-level process on
images involves tasks such as segmentation, a description of that object to reduce them to a form suitable for computer
processing & classification of individual objects. A mid-level process is characterized by the fact that its inputs generally are
images but its outputs are attributes extracted from those images. Finally higher- level processing involves “Making sense” of
an ensemble of recognized objects, as in image analysis & at the far end of the continuum performing the cognitive functions
normally associated with human vision.
Digital image processing, as already defined is used successfully in a broad range of areas of exceptional social & economic value.

5th International  Conference  on  Electrical  Engineering  (ELEC  2022) aims  to  bring
together  researchers  and  practitioners  from  academia  and  industry  to  focus  on  recent
systems and techniques in the broad field of Electrical Engineering.

Many security systems employing different methods have been
proposed to protect buried oil pipelines transporting petroleum
products from the well-head via the refinery to: depots and other
receiving stations. Currently there is a security gap in monitoring
these buried pipelines in real time and in keeping them protected
from third party interference. This paper addresses the problem
of monitoring these systems by developing an automated image
analysis system using an Unmanned Aerial Vehicle (UAV)
equipped with sensors for monitoring buried pipeline-right-ofway
(ROW).Video frame sequences captured by the UAV
platform were ortho-rectified and mosaicked to form a seamless
Digital Surface Model (DSM), which was then compared with an
existing map for geo-referencing. This was achieved using the
Direct Linear Transform (DLT) model and the Speed-up Robust
Feature (SURF) algorithm. An OT-MACH correlation filter was
applied to the geo-registered mosaicked video frames to detect
third party interference with the ROW.
Keywords: Correlation filters, Video mosaicking, Rightof-
Way, Geo-reference, Pipeline, Unmanned aerial vehicle

Electrocardiogram (ECG) analysis plays vital role in diagnosis of heart diseases. In order to avoid erroneous conclusions, it is of utmost importance that only sinus rhythms are present in the ECG Signal. Therefore, preprocessing of the ECG signal is necessary before utilizing it for diagnostic purpose [9]. In an ECG signal all kind of noises may occur simultaneously, two major noises that can be contaminated in ECG signal are baseline wander and power line interference, removal of those two noises from an ECG signal is a classical problem. In this paper digital filtering based simple approach is proposed for the removal of baseline wander and power line interference. Performance evaluation of this algorithm is done on recorded ECG signals by holter recorder and MIT-BIH database. Validation of proposed algorithm indicates that it effectively removes baseline wander and power line interference and preserves useful clinical information.

One of the mathematical disciplines with the highest number of followers is now called fuzzy logic (Fuzzy Logic, in English), which is the logic that uses expressions that are not completely true nor completely false, that is, it is the logic applied to concepts that They can take any value of truth within the set of values ranging between two extremes, the absolute truth and total falsehood.

Fuzzy logic is a methodology that provides a simple and elegant way to draw a conclusion from information vague, ambiguous, inaccurate, garbled or incomplete entry. Overall fuzzy logic mimics a person makes decisions based on information such features.
The need to emulate human behavior in all organizations has increased rapidly, so this thesis focuses on decision making, emulating the expert judgment of the logistics supervisor in the supply chain, exactly the stage and pick up materials / or equipment.
The proposal of this thesis is to optimize the pickup stage of materials and / or replacing equipment logistics supervisor routine tasks through control points (sensors) through a "controller" governed by Fuzzy Logic (design thesis) that send a series of alerts that will help improve the work done at this stage. By streamlining the tasks of this phase it is expected to significantly reduce delivery times and reduce operating costs.
 

The International Journal of Chaos, Control, Modeling and Simulation is a Quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Chaos Theory, Control Systems, Scientific Modeling and Computer Simulation. In the last few decades, chaos theory has found important applications in secure communication devices and secure data encryption. Control methods are very useful in several applied areas like Chaos, Automation, Communication, Robotics, Power Systems, and Biomedical Instrumentation. The journal focuses on all technical and practical aspects of Chaos, Control, Modeling and Simulation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on chaotic systems and applications, control theory, process control, automation, modeling concepts, computational methods, computer simulation and establishing new collaborations in these areas.

CC BY-NC-ND El autor sólo permite que se pueda descargar esta obra y compartirla con otras personas, siempre que se reconozca su autoría, pero no se puede cambiar de ninguna manera ni se puede utilizar comercialmente. 2016 ii Dedicatoria A nuestros padres quienes siempre nos impulsaron a ser los mejores y a superarnos cada día. A mis maestros quienes nunca desistieron al enseñarnos, y continuaron depositando su confianza en nosotros. iii Agradecimiento Expresamos nuestro agradecimiento a la Universidad "San Martin de Porres", por ser nuestra casa de estudios que nos permitieron llegar a ser profesionales. A nuestros padres por apoyarnos en todo momento, por los valores que nos han inculcado y por habernos dado la oportunidad de tener una excelente educación en el transcurso de nuestras vidas. iv ÍNDICE Página RESUMEN x ABSTRACT xi

7th International Conference on Image Processing and Pattern Recognition (IPPR 2021) is a forum for presenting new advances and research results in the fields of Digital Image Processing. The Workshop will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the journal covers all theoretical and practical aspects of the Digital Image Processing & Pattern Recognition, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences.

9
th International Conference on Computer Science and Engineering (CSEN 2022) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Computing. The goal of this conference is to bring together
researchers and practitioners from academia and industry to focus on Computer science &
Computer Engineering advancements and establishing new collaborations in these areas.
Original research papers, state-of-the-art reviews are invited for publication in all areas of
Computer Science & Computer Engineering.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the areas of Computer Science & Engineering.

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.

Due to the ease of access to platforms that can be used by forgers to tamper digital documents, providing automatic tools for identifying forged images is now a hot research field in image processing. This paper presents a novel forgery detection algorithm based on variants of Benford's law. In the proposed method, Mean Absolute Deviation (MAD) feature is extracted using traditional Benford's law. Also, generalized Benford's law is used for mantissa distribution feature vector. In addition to Benford's law-based features, other statistical features are used to construct the final feature vector. Finally, support vector machine (SVM) with three different kernel functions is used to classify original and forged images. The method has been tested on two common image datasets (CASIA V1.0 and V2.0). The experimental results show that 0.27% and 0.21% improvements on CASIA V1.0 and CASIA V2.0 datasets were achieved, respectively in detection accuracy by the proposed method in comparison to best state-of-the-art methods. The proposed efficient algorithm has a simple implementation. Moreover, on the basis of Benford's law rich features are extracted from images so that classification process is efficiently performed by a simple SVM classifier in a short time.

Менаџер за дигитални записи или на англиски Digital Imaging Technologist,
попознато како Data Manager е филмска професија која со процесот на
дигитализација претставува поврзување на on-set активности со пост-продукција.
Оваа позиција спаѓа во одделот за камера која е еден вид техничка поддршка на
дигиталната камера. Основно за DIT e менаџирање со дигиталниот т.е. ,,суровиот,,
raw материјал. Дата менаџерот е тесно поврзан со асистентот на монтажа,
директорот на фотографија, скриптерот, како и режисерот, а работи според
организациони насоки на продуцентот.
Работната обврска на DIT е да ги подигне мемориските картички од асистентот на
камерa или clapper loader и да направи копија од истите со помош на специјален
програм за копирање на специјален тврд диск односно истовремено да направи
копија на најмалку две места. Меѓу другото на филмскиот сет се јавува потреба на
прва колор корекција, која може да биде прва фаза на колор корекцијата, тој треба
да направи корекција на live излезот од дигиталната камера на видео мониторот од
режисерот и продуцентите, бидејќи е одговорен за raw материјалот или Log
сигналот. Ова практично значи дека DIT менаџерот заедно со режисерот и
директорот на фотографија го прават првиот color look кој подоцна се пренесува во
пост-продукција и е основа за колор корекцијата.
Во суштина, DIT помага на DOP (директор на фотографија), тој во одредени
ситуации мониторира и реагира доколку забележи дека од некое светло доминира
зелена боја или се јавува некој „флеер“. Од технички аспект позицијата DIT бара
посебна техничка опрема, моќен компјутер, брзи дискови со голем капацитет,
читачи на мемориски картички и секако не помалку важно LTO снимач и LTO
касети кои се еден вид линиски backup. Секако во сето ова мора да споменеме дека
се користи соодветен специјален софтвер за копирање, транскодирање и колор
корекција. Постојат неколку видови на програми за успешно работење како што се
Silverstack зa offload и Davinci Resolve за колор корекција. Последното нешто за
што сака да се справува еден дата менаџер на сет е да загуби или избрише
материјал, а тоа е многу веројано ако работите се прават во лет или не се внимава
на процедурата.
Со прогресија на дигитализацијата се појавуваат повеќе задачи што DIT
треба да ги обавува, a со тоа од голема важност е да има прецизен WORKFLOW кој низ процесот на креирање на филмот, задачите уредно и максимално
професионално ќе бидат извршени.
DIT позицијата е креирана со самото преминување од долги години користената
филмска трака за филмска камера во дигитална камера.

Object tracking is the process of locating moving objects in the consecutive video frames. Real time object tracking is a challenging problem in the field of computer vision, motion-based recognition,
automated surveillance, traffic monitoring, augmented reality, object based video compression etc. To review the state-of-the-art tracking methods, classify them into different categories, and identify new trends.
Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, non rigid object structures, object to- object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of
higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. The tracking
methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. The important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects. Object tracking for video sensor-based applications, an online discriminative algorithm based
on incremental discriminative structured dictionary learning. A discriminative dictionary combining both positive , negative and trivial patches is designed to Sparsely represent the overlapped target patches. The models are trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable
performance. In various target tracking related estimation and data association applications, we extend or modify particle ltering algorithms. The passive ranging application when only angle information is
available is discussed for several problems. While numerous algorithms have been proposed for object tracking with demonstrated success, it remains a challenging problem for a tracker to handle large change in scale, motion, shape deformation with occlusion.

—This paper deals with a video content classification based on spatial and temporal information. The main goal of the paper is to find out whether the non-expert test subjects are able to correctly recognize the spatial and temporal information and how precise is the corresponding parameterization of video content based on these two attributes. Moreover, how well do the objective values provided by the tool deployed in this experiment correlate with the subjective ones. The subjective measurement has shown that the non-expert test subjects are able to correctly recognize the spatial and temporal information only for the extreme scenes in terms of the spatial and temporal information but have experienced some problems when it comes to a more granular classification. When it comes to the objective assessment, the SI and TI values provided by the tool used in this experiment correlate quite well with the subjective data obtained from the basic test for the spatial information.

Craters, ridges and rocks are the features to be extracted in the lunar images. Proposed methodology deals with detection of linear feature such as (i) ridges, (ii) lineaments and (iii) nonlinear features like craters. All edges are identified as either crater or lineament. Therefore, all circular edges are recognized as craters edges, and those remained edges, which are not crater edges and low possibility of the noise, are recognized as lineaments in the remained edges. The Steps for lineament recognition includes the steps such as Crater Rim Elimination, Small Area Elimination and Lineament Classification. Ridges detection includes edge extraction using haar wavelet transform followed by morphological operations such as dilation and erosion. For detection of non linear features such as craters, the most predominant feature on the lunar terrain, circular hough transform is used. But it fails to detect craters without proper connected edges because of presence of shadows in an image. This paper includes algorithms for shadow removal in the images which accounts for changes in illumination, visual appearance, and size. Results indicate that the detection rate is improved with shadow removal techniques.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

9 th International Conference on Computer Science and Engineering (CSEN 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computing. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on Computer science & Computer Engineering advancements and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Computer Science & Computer Engineering. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science & Engineering.

Существует широкий круг задач, где требуется анализ, аудио-визуальных моделей реальности. В частности, для многих военных и гражданских приложений, необходимо наличие поиска нечетких дубликатов видео. Для мирного применения, — это группировка подсказок в выдаче поисковиков, поиск неправомерных публикаций видеофайлов, сокращение объема хранимой и передаваемой информации. Для военного применения можно тоже перечислить несколько вариантов.

Un vehículo aéreo no tripulado, conocido también como drone, es una aeronave que se caracteriza por la ausencia de un piloto humano a bordo. Los drones han tenido importancia desde ya más de una década. En la actualidad tienen aplicaciones tanto académicas como industriales. Para la presente tesis se basa en un drone tipo cuadrocóptero, que se caracteriza por sustentar gracias a sus cuatro motores. Este, desde un punto de vista ingenieril, se trata de un sistema altamente no lineal que presenta perturbaciones y ruido que no permiten tener un estabilidad en pleno vuelo. Esta aeronave tiene un costo amigable; por eso, ha sido tratado como mesa de prueba para la elaboración de controladores avanzados, por lo que se piensa diseñar un regulador cuadrático gaussiano LQG con control neuronal para un prototipo cuadrocóptero. Para ello, se requiere un estudio previo de las redes neuronales aplicadas a la teoría de control; el modelamiento directo, el cual se basa en las ecuaciones diferenciales que gobiernan la dinámica de la aeronave; el modelamiento inverso, el cual pasa por un proceso de identificación de sistema basado en el modelo paramétrico autorregresivo y que gracias a la red neuronal se podrá identificar usando el algoritmo de aprendizaje de retropropagación o backpropagation. El controlador LQG es un controlador avanzado, por lo que se piensa importar a manera de código para que la red pueda emularlo y, de esa manera obtener el control neuronal. Se quiere obtener el control de los ángulos de inclinación del cuadrocóptero con el fin de mantenerlo estable en un punto determinado; por eso, se tiene un sistema regulador. Como etapa de prueba se realiza la simulación. Se hacen cálculos teóricos para poder comparar y calibrar los resultados obtenidos por el modelamiento inverso.

This work aims to provide a solution for the maintenance and control of equipment within different work environment as in industries, education, storage, among others. In our country, we are used to perform maintenance on equipment and keep a log of it in printed documents, which is tend to get lost or accumulating office space, which at some point should be discarded. The motivation for this thesis is to avoid the accumulation of forms for each of the maintenance work carried out in institutions, giving as a mobile application solution that any company personnel can install on his mobile device and perform the maintenance reports of such equipment in an easier way and allowing interaction with the supervisor in charge of the service.

Our thesis combines algorithms for digital processing signal  with proprietary algorithms to recognize barcodes using a mobile device with the Android® operating system, which is one of the most popular operating systems on the market comparable with iOS® from Apple® and Microsoft Mobile®  from Microsoft®.