| 1. |
- Cheddad, Abbas, et al.
(författare)
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SHIBR-The Swedish Historical Birth Records : a semi-annotated dataset
- 2021
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Ingår i: Neural Computing & Applications. - : Springer London. - 0941-0643 .- 1433-3058. ; 33:22, s. 15863-15875
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms' performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child's first name, birth date, date of baptism, father's first/last name, mother's first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.
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| 2. |
- Demir, Muhammed Fatih, et al.
(författare)
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Real-Time Resistor Color Code Recognition using Image Processing in Mobile Devices
- 2018
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Ingår i: 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538670972 ; , s. 26-30
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a real-time video analysis algorithm to read the resistance value of a resistor using a color recognition technique. To achieve this, firstly, a nonlinear filtering is applied to input video frame to smooth intensity variations and remove impulse noises. After that, a photometric invariants technique is employed to transfer the video frame from RGB color space to Hue-Saturation-Value (HSV) color space, which decreases sensitivity of the proposed method to illumination changes. Next, a region of interest is defined to automatically detect resistor's colors and then an Euclidean distance based clustering strategy is employed to recognize the color bars. The proposed method provides a wide range of color classification which includes twelve colors. In addition, it utilizes relatively low computational time which makes it suitable for real-time mobile video applications. The experiments are performed on a variety of test videos and results show that the proposed method has low error rate compared to the other resistor color code recognition mobile applications. © 2018 IEEE.
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| 3. |
- Kusetogullari, Anna, 1987-, et al.
(författare)
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Genetic Algorithm-based Variable Selection Approach for High-Growth Firm Prediction
- 2022
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Ingår i: International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665470957
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a novel method for high-growth firm prediction by minimizing a cost function using a Genetic Algorithm (GA). To achieve it, the GA is used to search to find a set of important variables which provide the best fit for machine learning models so that accurate predictions can be made for high-growth firm prediction. The GA is employed to optimize the mean square error (MSE) between the accurate results and the predicted results of the machine learning methods by evolving the initially generated binary solutions through iterations. The proposed method obtains the best fitting set of variables for the machine learning methods for high-growth firm prediction. Four different machine learning methods which are Support Vector Machines (SVM), Logistic Regression, Random Forest (RF) and K-Nearest Neighbor (K-NN) have been employed with the GA and experimental results show that using RF with the GA achieves the best accuracy results with 94.93%. © 2022 IEEE.
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| 4. |
- Kusetogullari, Hüseyin, 1981-, et al.
(författare)
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ARDIS : A Swedish Historical Handwritten Digit Dataset
- 2020
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Ingår i: Neural Computing & Applications. - : Springer Nature Switzerland. - 0941-0643 .- 1433-3058. ; 32:21, s. 16505-16518
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces a new image-based handwrittenhistorical digit dataset named ARDIS (Arkiv DigitalSweden). The images in ARDIS dataset are extractedfrom 15,000 Swedish church records which were writtenby different priests with various handwriting styles in thenineteenth and twentieth centuries. The constructed datasetconsists of three single digit datasets and one digit stringsdataset. The digit strings dataset includes 10,000 samplesin Red-Green-Blue (RGB) color space, whereas, the otherdatasets contain 7,600 single digit images in different colorspaces. An extensive analysis of machine learning methodson several digit datasets is examined. Additionally, correlationbetween ARDIS and existing digit datasets ModifiedNational Institute of Standards and Technology (MNIST)and United States Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms,including deep learning methods, provide low recognitionaccuracy as they face difficulties when trained on existingdatasets and tested on ARDIS dataset. Accordingly, ConvolutionalNeural Network (CNN) trained on MNIST andUSPS and tested on ARDIS provide the highest accuracies 58.80% and 35.44%, respectively. Consequently, the resultsreveal that machine learning methods trained on existingdatasets can have difficulties to recognize digits effectivelyon our dataset which proves that ARDIS dataset hasunique characteristics. This dataset is publicly available forthe research community to further advance handwritten digitrecognition algorithms.
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| 5. |
- Kusetogullari, Hüseyin, 1981-, et al.
(författare)
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Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm
- 2017
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE. - 1545-598X .- 1558-0571. ; 14:3, s. 414-418
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we propose a novel method for unsupervised change detection in multitemporal multispectral Landsat images using multiobjective evolutionary algorithm (MOEA). The proposed method minimizes two different objective functions using MOEA to provide tradeoff between each other. The objective functions are used for evaluating changed and unchanged regions of the difference image separately. The difference image is obtained by using the structural similarity index measure method, which provides combination of the comparisons of luminance, contrast, and structure between two images. By evolving a population of solutions in the MOEA, a set of Pareto optimal solution is estimated in a single run. To find the best solution, a Markov random field fusion approach is used. Experiments on semisynthetic and real-world data sets show the efficiency and effectiveness of the proposed method.
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| 6. |
- Kusetogullari, Huseyin, et al.
(författare)
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DIGITNET : A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset
- 2021
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Ingår i: Big Data Research. - : Elsevier. - 2214-5796 .- 2214-580X. ; 23
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces a novel deep learning architecture, named DIGITNET, and a large-scale digit dataset, named DIDA, to detect and recognize handwritten digits in historical document images written in the nineteen century. To generate the DIDA dataset, digit images are collected from 100,000 Swedish handwritten historical document images, which were written by different priests with different handwriting styles. This dataset contains three sub-datasets including single digit, large-scale bounding box annotated multi-digit, and digit string with 250,000, 25,000, and 200,000 samples in Red-Green-Blue (RGB) color spaces, respectively. Moreover, DIDA is used to train the DIGITNET network, which consists of two deep learning architectures, called DIGITNET-dect and DIGITNET-rec, respectively, to isolate digits and recognize digit strings in historical handwritten documents. In DIGITNET-dect architecture, to extract features from digits, three residual units where each residual unit has three convolution neural network structures are used and then a detection strategy based on You Look Only Once (YOLO) algorithm is employed to detect handwritten digits at two different scales. In DIGITNET-rec, the detected isolated digits are passed through 3 different designed Convolutional Neural Network (CNN) architectures and then the classification results of three different CNNs are combined using a voting scheme to recognize digit strings. The proposed model is also trained with various existing handwritten digit datasets and then validated over historical handwritten digit strings. The experimental results show that the proposed architecture trained with DIDA (publicly available from: https://didadataset.github.io/DIDA/) outperforms the state-of-the-art methods.
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| 7. |
- Kusetogullari, Hüseyin, 1981-, et al.
(författare)
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Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images
- 2018
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Ingår i: Applied Soft Computing. - : Elsevier Ltd. - 1568-4946 .- 1872-9681. ; 73, s. 1039-1052
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a novel method for generation of multiple description (MD) wavelet based image coding is proposed by using Multi-Objective Evolutionary Algorithms (MOEAs). Complexity of the multimedia transmission problem has been increased for MD coders if an input image is affected by any type of noise. In this case, it is necessary to solve two different problems which are designing the optimal side quantizers and estimating optimal parameters of the denoising filter. Existing MD coding (MDC) generation methods are capable of solving only one problem which is to design side quantizers from the given noise-free image but they can fail reducing any type of noise on the descriptions if they applied to the given noisy image and this will cause bad quality of multimedia transmission in networks. Proposed method is used to overcome these difficulties to provide effective multimedia transmission in lossy networks. To achieve it, Dual Tree-Complex Wavelet Transform (DT-CWT) is first applied to the noisy image to obtain the subbands or set of coefficients which are used as a search space in the optimization problem. After that, two different objective functions are simultaneously employed in the MOEA to find pareto optimal solutions with the minimum costs by evolving the initial individuals through generations. Thus, optimal quantizers are created for MDCs generation and obtained optimum parameters are used in the image filter to remove the mixed Gaussian impulse noise on the descriptions effectively. The results demonstrate that proposed method is robust to the mixed Gaussian impulse noise, and offers a significant improvement of optimal side quantizers for balanced MDCs generation at different bitrates. © 2018 Elsevier B.V.
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| 8. |
- Kusetogullari, Huseyin, et al.
(författare)
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Self-Adaptive Hybrid PSO-GA Method for Change Detection Under Varying Contrast Conditions in Satellite Images
- 2016
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Ingår i: Proceedings of the 2016 SAI Computing Conference (SAI). - : IEEE. - 9781467384605 ; , s. 361-368
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a new unsupervised satellite change detection method, which is robust to illumination changes. To achieve this, firstly, a preprocessing strategy is used to remove illumination artifacts and results in less false detection than traditional threshold-based algorithms. Then, we use the corrected input data to define a new fitness function based on the difference image. The purpose of using Self-Adaptive Hybrid Particle Swarm Optimization-Genetic Algorithm (SAPSOGA) is to combine two meta-heuristic optimization algorithms to search and find the feasible solution in the NP-hard change detection problem rapidly and efficiently. The hybrid algorithm is employed by letting the GA and PSO run simultaneously and similarities of GA and PSO have been considered to implement the algorithm, i.e. the population. In the SAPSOGA employed, in each iteration/generation the two population based algorithms share different amount of information or individual(s) between themselves. Thus, each algorithm informs each other about their best optimum results (fitness values and solution representations) which are obtained in their own population. The fitness function is minimized by using binary based SAPSOGA approach to produce binary change detection masks in each iteration to obtain the optimal change detection mask between two multi temporal multi spectral landsat images. The proposed approach effectively optimizes the change detection problem and finds the final change detection mask.
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| 9. |
- Kusetogullari, Huseyin, 1981-, et al.
(författare)
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Unsupervised Change Detection in Landsat Images with Atmospheric Artifacts : A Fuzzy Multiobjective Approach
- 2018
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Ingår i: Mathematical problems in engineering (Print). - : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2018, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- A new unsupervised approach based on a hybrid wavelet transform and Fuzzy Clustering Method (FCM) with Multiobjective Particle Swarm Optimization (MO-PSO) is proposed to obtain a binary change mask in Landsat images acquired with different atmospheric conditions. The proposed method uses the following steps: preprocessing, classification of preprocessed image, and binary masks fusion. Firstly, a photometric invariant technique is used to transform the Landsat images from RGB to HSV colour space. A hybrid wavelet transform based on Stationary (SWT) and Discrete Wavelet (DWT) Transforms is applied to the hue channel of two Landsat satellite images to create subbands. After that, mean shift clustering method is applied to the subband difference images, computed using the absolute-valued difference technique, to smooth the difference images. Then, the proposed method optimizes iteratively two different fuzzy based objective functions using MO-PSO to evaluate changed and unchanged regions of the smoothed difference images separately. Finally, a fusion approach based on connected component with union technique is proposed to fuse two binary masks to estimate the final solution. Experimental results show the robustness of the proposed method to existence of haze and thin clouds as well as Gaussian noise in Landsat images.
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| 10. |
- Tekin, Eren, et al.
(författare)
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Tubule-U-Net : a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer
- 2023
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Ingår i: Scientific Reports. - : Nature Portfolio. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and proposed to segment tubules in Whole Slide Images (WSI) of breast cancer. Moreover, this paper presents a new tubule segmentation dataset consisting of 30820 polygonal annotated tubules in 8225 patches. The Tubule-U-Net framework first uses a patch enhancement technique such as reflection or mirror padding and then employs an asymmetric encoder-decoder semantic segmentation model. The encoder is developed in the model by various deep learning architectures such as EfficientNetB3, ResNet34, and DenseNet161, whereas the decoder is similar to U-Net. Thus, three different models are obtained, which are EfficientNetB3-U-Net, ResNet34-U-Net, and DenseNet161-U-Net. The proposed framework with three different models, U-Net, U-Net++, and Trans-U-Net segmentation methods are trained on the created dataset and tested on five different WSIs. The experimental results demonstrate that the proposed framework with the EfficientNetB3 model trained on patches obtained using the reflection padding and tested on patches with overlapping provides the best segmentation results on the test data and achieves 95.33%, 93.74%, and 90.02%, dice, recall, and specificity scores, respectively. © 2023, The Author(s).
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