Computer Engineering Commons^™

Uncovering And Mitigating Spurious Features In Domain Generalization, Saeed Karimi, Hamdi̇ Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Domain generalization (DG) techniques strive to attain the ability to generalize to an unfamiliar target domain solely based on training data originating from the source domains. Despite the increasing attention given to learning from multiple training domains through the application of various forms of invariance across those domains, the enhancements observed in comparison to ERM are nearly insignificant under specified evaluation rules. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a challenging task in conventional training since ERM simply minimizes the loss and does not exploit invariance among domains. To address this issue, we …

A Two-Stage Hair Region Localization Method For Guided Laser Hair Removal, Murat Avşar, İmam Şami̇l Yeti̇k

Turkish Journal of Electrical Engineering and Computer Sciences

Removal of hair using laser is a widely used method, where our goal is to permanently remove hair by using laser to cause heat in order to thermally damage the hair follicle. However, currently available laser hair removal systems affect the outer skin layers besides hair follicles. This is a disadvantage of classical methods with major health risks. We propose a method to overcome these health risks by guiding the laser beam only to automatically localized hair regions. This study aims to develop an automated feature-based hair region localization method as an integral part of the proposed hair removal system …

A Novel Hybrid Decision-Based Filter And Universal Edge-Based Logical Smoothingadd-On To Remove Impulsive Noise, Rajanbir Singh Ghumaan, Prateek Jeet Singh Sohi, Nikhil Sharma, Bharat Garg

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a novel hybrid filter along with a universal extension to remove salt and pepper noise even at a very high noise density. The proposed filter initially specifies a threshold and then denoises the image using a combination of linear, nonlinear, and probabilistic techniques. Furthermore, to improve the quality, a universal add-on is presented which uses edge detection and smoothening techniques to brush out fine details from the restored image. To evaluate the efficacy, the proposed and existing filtering techniques are implemented in MATLAB and simulated with benchmark images. The simulation results show that the proposed filter is …

Bibliometric Review On Image Based Plant Phenotyping, Shrikrishna Ulhas Kolhar, Jayant Jagtap

Library Philosophy and Practice (e-journal)

Plant phenotyping is a quantitative description of structural, physiological and temporal traits of plants resulting from interaction of plant genotypes with the environment. A rapid development is in progress in the field of image-based plant phenotyping. Plant phenotyping has wide range of applications in plant breeding research, plant growth prediction, biotic and abiotic stress analysis, crop management and early disease detection. The main motive is to provide detailed bibliometric review in order to know the available literature and current research trends in the area of plant phenotyping using plant images. The bibliometric analysis is primarily based on Scopus, web of …

Image Denoising Using Deep Convolutional Autoencoder With Feature Pyramids, Ekrem Çeti̇nkaya, Mustafa Furkan Kiraç

Turkish Journal of Electrical Engineering and Computer Sciences

Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted …

Multi-Pig Part Detection And Association With A Fully-Convolutional Network, Eric T. Psota, Mateusz Mittek, Lance C. Pérez, Ty Schmidt, Benny Mote

Department of Electrical and Computer Engineering: Faculty Publications

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new …

A Novel Method Of Near-Miss Event Detection With Software Defined Radar In Improving Railyard Safety, Subharthi Banerjee, Jose Santos, Michael Hempel, Pejman Ghasemzadeh, Hamid Sharif

Department of Electrical and Computer Engineering: Faculty Publications

Railyards are one of the most challenging and complex workplace environments in any industry. Railyard workers are constantly surrounded by dangerous moving objects, in a noisy environment where distractions can easily result in accidents or casualties. Throughout the years, yards have been contributing 20–30% of the total accidents that happen in railroads. Monitoring the railyard workspace to keep personnel safe from falls, slips, being struck by large object, etc. and preventing fatal accidents can be particularly challenging due to the sheer number of factors involved, such as the need to protect a large geographical space, the inherent dynamicity of the …

Accurate And Compact Stochastic Computations By Exploiting Correlation, Hamdan Abdellatef, Mohamed Khalil Hani, Nasir Shaikh-Husin

Turkish Journal of Electrical Engineering and Computer Sciences

Recent studies have shown, contrary to what was previously believed, that by exploiting correlation in stochastic computing (SC) designs, more accurate SC circuits with low area cost can be realized. However, if these basic SC circuits or blocks are cascaded in series to form a large complex system, correlation between stochastic numbers (SNs) from one block to the next would be lost; thus, inaccuracies are introduced. In this study, we propose correlating circuits to be used in building complex correlated SC systems. One of the circuits is the correlator that restores lost correlations between two SNs due to previous processing. …

A Multiseed-Based Svm Classification Technique For Training Sample Reduction, Imran Sharif, Debasis Chaudhuri

Turkish Journal of Electrical Engineering and Computer Sciences

A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets …

Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar

Turkish Journal of Electrical Engineering and Computer Sciences

Camera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals. To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast …

Image Processing And Understanding Based On Graph Similarity Testing: Algorithm Design And Software Development, Jieqi Kang

Doctoral Dissertations

Image processing and understanding is a key task in the human visual system. Among all related topics, content based image retrieval and classification is the most typical and important problem. Successful image retrieval/classification models require an effective fundamental step of image representation and feature extraction. While traditional methods are not capable of capturing all structural information on the image, using graph to represent the image is not only biologically plausible but also has certain advantages. Graphs have been widely used in image related applications. Traditional graph-based image analysis models include pixel-based graph-cut techniques for image segmentation, low-level and high-level image …

An Emg-Based Patient Monitoring System Using Zynq Soc Device, Farhad Fallahlalehzari

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis describes the design, development, and testing of an EMG-based patient monitoring system using the Zynq device. Zynq is a system on chip device designed by Xilinx which consists of an ARM dual cortex-A9 processor as well as an FPGA integrated into one chip. This work also analyzes the performance of image-processing algorithms on this system and compares that performance to more traditional PC-based systems. Image processing algorithms, such as Sobel edge detection, dilation and erosion, could be used in conjunction with a camera for the patient monitoring purposes. These algorithms often perform sub-optimally on processors because of their …

Hardware Implementation Of A Scale And Rotation Invariant Object Detection Algorithm On Fpga For Real-Time Applications, Murat Peker, Hali̇s Altun, Fuat Karakaya

Turkish Journal of Electrical Engineering and Computer Sciences

A hardware implementation of a computationally light, scale, and rotation invariant method for shape detection on FPGA is devised. The method is based on histogram of oriented gradients (HOG) and average magnitude difference function (AMDF). AMDF is used as a decision module that measures the similarity/dissimilarity between HOG vectors of an image in order to classify the object. In addition, a simulation environment implemented on MATLAB is developed in order to overcome the time-consuming and tedious process of hardware verification on the FPGA platform. The simulation environment provides specific tools to quickly implement the proposed methods. It is shown that …

Some Properties Of Digital H-Spaces, Özgür Ege, İsmet Karaca

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we study certain properties of digital H-spaces. We prove that a digital image that has the same digital homotopy type with any digital H-space is also a digital H-space. We show that the digital fundamental group of a digital H-space is abelian. We give examples that are related to a digital homotopy associative H-space and a $\kappa $-contractible digital H-space. Several important applications of digital H-spaces are given in computer vision and image processing. Finally, we deal with the importance of digital H-space in digital topology and image processing. We conclude that any $\kappa $-contractible digital image …

Brain Tumor Detection Using Monomodal Intensity Based Medical Image Registration And Matlab, Emrah Irmak, Ergun Erçelebi̇, Ahmet Hani̇fi̇ Ertaş

Turkish Journal of Electrical Engineering and Computer Sciences

digital image processing. Using suitable computer programming techniques and transformation between two images, a new much more informative image can be found. In this paper, three important and basic medical image registration (MIR) methods, namely MIR by maximization of mutual information, MIR using cross correlation (Fourier transform approach), and MIR by minimization of similarity metric, were proposed and accordingly two comprehensive applications were performed using MIR by minimization of the similarity metric, which uses the sum of the squared differences metric as a metric and the regular step gradient descent optimizer as an optimizer. What is more, MR images of …

Design And Implementation Of Digital Information Security For Physical Documents, Pengcheng Wang

Masters Theses

The objective of this thesis is to improve the security for physical paper documents. Providing information security has been difficult in environments that rely on physical paper documents to implement business processes. Our work presents the design of a digital information security system for paper documents, called "CryptoPaper", that uses 2-dimensional codes to represent data and its security properties on paper. A special scanner system is designed for "CryptoPaper" which uses image recognition techniques and cloud-based access control to display plaintext of encrypted and encoded data to authorized users.

Design And Verification Environment For High-Performance Video-Based Embedded Systems, Michael Mefenza Nentedem

Graduate Theses and Dissertations

In this dissertation, a method and a tool to enable design and verification of computation demanding embedded vision-based systems is presented. Starting with an executable specification in OpenCV, we provide subsequent refinements and verification down to a system-on-chip prototype into an FPGA-Based smart camera. At each level of abstraction, properties of image processing applications are used along with structure composition to provide a generic architecture that can be automatically verified and mapped to the lower abstraction level. The result is a framework that encapsulates the computer vision library OpenCV at the highest level, integrates Accelera's System-C/TLM with UVM and QEMU-OS …

Extraction Of Pictorial Energy Information From Campus Unmetered Buildings Using Image Processing Techniques, Yachen Tang

Dissertations, Master's Theses and Master's Reports - Open

In recent years, advanced metering infrastructure (AMI) has been the main research focus due to the traditional power grid has been restricted to meet development requirements. There has been an ongoing effort to increase the number of AMI devices that provide real-time data readings to improve system observability. Deployed AMI across distribution secondary networks provides load and consumption information for individual households which can improve grid management. Significant upgrade costs associated with retrofitting existing meters with network-capable sensing can be made more economical by using image processing methods to extract usage information from images of the existing meters. This thesis …

Detection Of Microcalcification In Digitized Mammograms With Multistable Cellular Neural Networks Using A New Image Enhancement Method: Automated Lesion Intensity Enhancer (Alie), Levent Ci̇vci̇k, Burak Yilmaz, Yüksel Özbay, Gani̇me Di̇lek Emli̇k

Turkish Journal of Electrical Engineering and Computer Sciences

Microcalcification detection is a very important issue in early diagnosis of breast cancer. Generally physicians use mammogram images for this task; however, sometimes analyzing these images become a hard task because of problems in images such as high brightness values, dense tissues, noise, and insufficient contrast level. In this paper, we present a novel technique for the task of microcalcification detection. This technique consists of three steps. The first step is focused on removing pectoral muscle and unnecessary parts from the mammogram images by using cellular neural networks (CNNs), which makes this a novel process. In the second step, we …

Automated Image Interpretation For Science Autonomy In Robotic Planetary Exploration, Raymond Francis

Electronic Thesis and Dissertation Repository

Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data.

This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces.

Two …

A Novel Fuzzy Filter For Speckle Noise Removal, Mehmet Ali̇ Soytürk, Alper Baştürk, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a novel fuzzy system-based method for speckle noise removal is proposed. The proposed method consists of a fuzzy inference system, an edge detection and dilation unit, and an image combiner. The fuzzy inference system includes 5 inputs and 1 output, and it is responsible for filtering the speckle noisy image. The inputs of the fuzzy system consist of the center pixel of the filtering window and its 2 horizontal and vertical neighbors. The edge detection and dilation unit is used for classifying the uniform areas and nonuniform image regions such as edges. The image combiner unites the …

Contrast Enhancement Using Linear Image Combinations Algorithm (Ceulica) For Enhancing Brain Magnetic Resonance Images, Burak Yilmaz, Yüksel Özbay

Turkish Journal of Electrical Engineering and Computer Sciences

Brain magnetic resonance imaging (MRI) images support important information about brain diseases for physicians. Morphological alterations in brain tissues indicate the probable existence of a disease in many cases. Proper estimation of these tissues, measuring their sizes, and analyzing their image patterns are parts of the diagnosis process. Therefore, the interpretability and perceptibility level of the MRI image is valuable for physicians. In this paper, a new image contrast enhancement algorithm based on linear combinations is presented. The proposed algorithm is focused on improving the interpretability and perceptibility of the image information. An MRI image is presented to the algorithm, …

Spatiotemporal Realization Of An Artificial Retina Model And Performance Evaluation Through Isi- And Spike Count-Based Image Reconstruction Methods, İrfan Karagöz, Mustafa Özden

Turkish Journal of Electrical Engineering and Computer Sciences

Development of an artificial retina model that can mimic the biologic retina is a highly challenging task and this task is an important step in the development of a visual prosthesis. The receptive field structure of the retina layer is usually modeled as a 2D difference of Gaussian (DOG) filter profile. In the present study, as a different approach, a retina model including a 3D 2-stage DOG filter (3D-ADOG) that has an adaptively changing bandwidth with respect to the local image statistic is developed. Using this modeling, the adaptive image processing of the retina can be realized. The contribution of …

The Development Of Payload Software For A Small Spacecraft, Kyle Goehner, Christoffer Korvald, Jeremy Straub, Ronald Marsh

Jeremy Straub

The OpenOrbiter project is a multi-department effort to design and build a small spacecraft which will demonstrate the feasibility of the Open Prototype for Educational NanoSats (OPEN) framework. This framework will reduce cost of small spacecraft creation by providing design plans for free. The focus of the payload software group is to design and implement an onboard task processing and image processing service. Currently the project is in the development phase and most large design decisions have been made. This poster presents the major design decisions that have been made for the payload software and how they will affect the …

Enhanced Diagnostic Accuracy Of Mammograms On A Mobile Device, Sharanya Padmanabhan

Open Access Theses

With the death of a woman every 13 minutes in the US, and one every minute worldwide, due to breast cancer, the need for early detection cannot be overstated. Mammography is a boon for both early detection and screening of breast tumors. It is an imaging system that uses low dose (9mrem) x-rays for examining the breasts, by the electrons reflected from the tissues (thermoelectric effect). However, there are 20% false positives and 10% false negatives in current practice. Hence, there is a critical need for enhancing the accuracy of these mammograms. Towards this, this thesis was aimed at enhancing …

An Automated Prognosis System For Estrogen Hormone Status Assessment In Breast Cancer Tissue Samples, Fati̇h Sarikoç, Adem Kalinli, Hülya Akgün, Fi̇gen Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

Estrogen receptor (ER) status evaluation is a widely applied method in the prognosis of breast cancer. However, testing for the existence of the ER biomarker in a patient's tumor sample mainly depends on the subjective decisions of the doctors. The aim of this paper is to introduce the usage of a machine learning tool, functional trees (FTs), to attain an ER prognosis of the disease via an objective decision model. For this aim, 27 image files, each of which came from a biopsy sample of an invasive ductal carcinoma patient, were scanned and captured by a light microscope. From these …

Payload Software, Christoffer Korvald, Jeremy Straub, Atif Mohammad, Josh Berk

Jeremy Straub

No abstract provided.

Resolution Enhancement Of Video Sequences By Using Discrete Wavelet Transform And Illumination Compensation, Sara Izadpanahi, Çağri Özçinar, Gholamreza Anbarjafari, Hasan Demirel

Turkish Journal of Electrical Engineering and Computer Sciences

This research paper proposes a new technique for video resolution enhancement that employees an illumination compensation procedure before the registration process. After the illumination compensation process, the respective frames are registered using the Irani and Peleg technique. In parallel, the corresponding frame is decomposed into high-frequency (low-high, high-low, and high-high) and low-frequency (low-low) subbands using discrete wavelet transform (DWT). The high-frequency subbands are superresolved using bicubic interpolation. Afterwards, the interpolated high-frequency subbands and superresolved low-frequency subband obtained by registration are used to construct the high-resolution frame using inverse DWT. The superiority of the proposed resolution enhancement method over well-known video …

Change Detection Without Difference Image Computation Based On Multiobjective Cost Function Optimization, Turgay Çeli̇k, Zeki̇ Yetgi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images by using multiobjective cost function optimization via genetic algorithm (GA). The spatial image grid of the input multi-temporal satellite images is divided into two distinct regions, representing ``changed'' and ``unchanged'' regions between input images, via the intermediate change detection mask produced by the GA. The dissimilarity of pixels of ``changed'' regions and similarity of pixels of ``unchanged'' regions between input multi-temporal images are measured using image quality metrics which consider correlation, spectral distortion, radiometric distortion, and contrast distortion. The contextual information of each pixel …

Knowledge-Based Navigation For Autonomous Road Vehicles, Murat Eki̇nci̇, Franches W.J.Gibbs, Barry T. Thomas

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a computer vision system for an autonomous road vehicle (ARV) that is capable of negotiating complex road networks including road junctions in real time. The ultimate aim of the system is to enable the vehicle to drive automatically along a given complex road network whose geometric description is known. This computer vision system includes three main techniques which are necessary for an ARV: a) road following, b) road junction detection, c) manoeuvring at the road junction. The road following algorithm presents a method of executing a number of algorithms using different methods concurrently, fusing their outputs together …