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In this article, we present a deconvolution software based on convex sets constructed from the phase of the Fourier Transform, bounded ℓ2energy and ℓ1energy of a given image. The iterative deconvolution algorithm is based on the method of projections onto convex sets. Another feature of the method is that it can incorporate an approximate total variation bound called filtered variation bound on the iterative deconvolution algorithm. The main purpose of this article is to introduce the open source software called projDeconv v2. Keywords: Deconvolution, Fourier Transform phase, ℓ1ball, Projection onto convex sets

Contrast enhancement is an important preprocessing step for the analysis of microscopy images. The main aim of contrast enhancement techniques is to increase the visibility of the cell structures and organelles by modifying the spatial characteristics of the image. In this paper, phase information-based contrast enhancement framework is proposed to overcome the limitations of existing image enhancement techniques. Inspired by the groundbreaking design of the phase contrast microscopy (PCM), the proposed image enhancement framework transforms the changes in image phase into the variations of magnitude to enhance the structural details of the image and to improve visibility. In addition, the concept of selective variation (SV) technique is introduced and enhancement parameters are optimized using SV. The experimental studies that were carried out on microscopy images show that the proposed scheme outperforms the baseline enhancement frameworks. The contrast enhanced images produced by the proposed method have comparable cellular texture structure as PCM images.

Abstract Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators. The dataset includes images from both Q3 (lower jaw left side) and Q4 (lower right side) regions extracted from panoramic images, resulting in a total of 6624 images for analysis. Following data collection, the methodology employs region of interest extraction, pre-filtering, and extensive data augmentation techniques to enhance classification accuracy. The deep neural network model, including architectures such as EfficientNet, EfficientNetV2, MobileNet Large, MobileNet Small, ResNet18, and ShuffleNet, is optimized for this task. Our findings indicate that EfficientNet achieved the highest classification accuracy at 83.7%. Other architectures achieved accuracies ranging from 71.57 to 82.03%. The variation in performance across architectures highlights the influence of model complexity and task-specific features on classification accuracy. This research introduces a novel machine learning model designed to accurately estimate the development stages of lower wisdom teeth in OPG images, contributing to the fields of dental diagnostics and treatment planning.

We introduce the wireless sensor network (WSN) data harvesting application we developed for wildfire detection and the experiments we have performed. The sensor nodes are equipped with temperature and relative humidity sensors. They are organized into clusters and they communicate with the cluster heads using 802.15.4/ZigBee wireless links. The cluster heads report the harvested data to the control center using 802.11/Wi-Fi links. We introduce the hardware and the software architecture of our deployment near Rhodiapolis, an ancient city raising on the outskirts of Kumluca county of Antalya, Turkey. We detail our technical insights into the deployment based on the real-world data collected from the site. We also propose a temperature-based fire detection algorithm and we evaluate its performance by performing experiments in our deployment site and also in our university. We observed that our WSN application can reliably report temperature data to the center quickly and our algorithms can detect fire events in an acceptable time frame with no or very few false positives.

Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision.

In this article, we present a computationally efficient algorithm to detect Volatile Organic Compounds (VOC) leaking out of components used in chemical processes in petrochemical refineries and chemical plants. A leaking VOC plume from a damaged component appears as a dynamic dark cloud in infrared videos. We describe a two-stage deep neural network structure, taking advantage of both spatial and temporal structure of the dynamic texture regions created by the leaking VOC plume. We first detect moving pixels which are darker then their neighboring pixels. We extract one-dimensional (1-D) signals representing the temporal history of such pixels from video and feed the 1-D signals to a 1-D convolutional neural network. If those pixels are near the edge of a VOC plume, their 1-D temporal signals exhibit high-frequency behavior. The neural network generates high probability estimates for such pixels. If 1-D neural network generates high confidence values, final decision is reached using a deep convolutional neural network (CNN) which processes image frames. The overall structure is computationally efficient because the spatio-temporal CNN does not process all of the image frames of the captured video. Experimental results are presented. [ABSTRACT FROM AUTHOR]

Adaptive filter banks for image compression are proposed.

In polyacrylamide gel electrophoresis (PAGE) image analysis, it is important to determine the percentage of the protein of interest of a protein mixture. This study presents reliable computer software to determine this percentage. The region of interest containing the protein band is detected using the snake algorithm. The iterative snake algorithm is implemented in a multi-resolutional framework. The snake is initialized on a low-resolution image. Then, the final position of the snake at the low resolution is used as the initial position in the higher-resolution image. Finally, the area of the protein is estimated as the area enclosed by the final position of the snake.

The introduction of lifting implementations for image wavelet decomposition generated possibilities of several applications and several adaptive decomposition variations. The prediction step of a lifting stage constitutes the interesting part of the decomposition since it aims to reduce the energy of one of the decomposition bands by making predictions using the other decomposition band. In that aspect, more successful predictions yield better efficiency in terms of reduced energy in the lower band. In this work, we present a prediction filter whose prediction domain pixels are selected adaptively according to the local edge characteristics of the image. By judicuously selecting the prediction domain from pixels that are expected to have closer relation to the estimated pixel, the prediction error signal energy is reduced. In order to keep the adaptation rule symmetric for the encoder and the decoder sides, lossless compression applications are examined. Experimental results show that the proposed algorithm provides good compression results. Furthermore, the edge calculation is computationally inexpensive and comparable to the famous Daubechies 5/3 lifting implementation.

An algorithm for human-eye localization in images is presented for faces with frontal pose and upright orientation. A given face region is filtered by a highpass wavelet-transform filter. In this way, edges of the region are highlighted, and a caricature-like representation is obtained. Candidate points for each eye are detected after analyzing horizontal projections and profiles of edge regions in the highpass-filtered image. All the candidate points are then classified using a support vector machine. Locations of each eye are estimated according to the most probable ones among the candidate points. It is experimentally observed that our eye localization method provides promising results for image-processing applications. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve performance in classifying the disorders and abnormalities. A new approach is examined here for enhancing EEG classification performance using a novel model of data representation that leverages knowledge of spatial layout of EEG sensors. An investigation of the performance of the proposed data representation model provides evidence of consistently higher classification accuracy of the proposed model compared with a model that ignores the sensor layout. The performance is assessed for models that represent the information content of the EEG signals in two different ways: a one-dimensional concatenation of the channels of the frequency bands and a proposed image-like two-dimensional representation of the EEG channel locations. The models are used in conjunction with different machine learning techniques. Performance of these models is examined on two tasks: social anxiety disorder classification, and emotion recognition using a dataset, DEAP, for emotion analysis using physiological signals. We hypothesize that the proposed two-dimensional model will significantly outperform the one-dimensional model and this is validated in our results as this model consistently yields 5-8% higher accuracy in all machine learning algorithms investigated. Among the algorithms investigated, Convolutional Neural Networks provide the best performance, far exceeding that of Support Vector Machine and k-Nearest Neighbors algorithms.

IntroductionWe aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images.MethodsA total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were cropped to extract the cervical vertebrae using an Aggregate Channel Features (ACF) object detector. The resulting images were used to train four different Deep Learning (DL) models: our proposed CNN, MobileNetV2, ResNet101, and Xception, together with a set of tunable directional edge enhancers. When using MobileNetV2, ResNet101 and Xception, data augmentation is adopted to allow adequate network complexity while avoiding overfitting. The performance of our CNN model was compared with that of MobileNetV2, ResNet101 and Xception with and without the use of directional filters. For validation and performance assessment, k-fold cross-validation, ROC curves, and p-values were used.ResultsThe proposed innovative model that uses a CNN preceded with a layer of tunable directional filters achieved a validation accuracy of 84.63%84.63% in CVM stage classification into five classes, exceeding the accuracy achieved with the other DL models investigated. MobileNetV2, ResNet101 and Xception used with directional filters attained accuracies of 78.54%, 74.10%, and 80.86%, respectively. The custom-designed CNN method also achieves 75.11% in six-class CVM stage classification. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If the custom-designed CNN is used without the directional filters, the test accuracy decreases to 80.75%. In the Xception model without the directional filters, the testing accuracy drops slightly to 79.42% in the five-class CVM stage classification.ConclusionThe proposed model of a custom-designed CNN together with the tunable Directional Filters (CNNDF) is observed to provide higher accuracy than the commonly used pre-trained network models that we investigated in the fully automated determination of the CVM stages.

Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors. In this paper, we develop novel machine learning methods for detecting and identifying VOC and Ammonia vapor from time-series data obtained by uncalibrated chemical and infrared sensors. We process time-series sensor signals using deep neural networks (DNN). Three neural network algorithms are utilized for this purpose. Additive neural networks (termed AddNet) are based on a multiplication-devoid operator and consequently exhibit energy efficiency compared to regular neural networks. The second algorithm uses generative adversarial neural networks so as to expose the classifying neural network to more realistic data points in order to help the classifier network to deliver improved generalization. Finally, we use conventional convolutional neural networks as a baseline method. Our findings indicate that using raw time-series data obtained from uncalibrated sensors and processing them using deep-learning-based methods yield better results than using hand-crafted feature parameters.

In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fourier domain and discard similar filters using the cosine similarity measure in the frequency domain. We test the performance of the neural network with a variety of wildfire video clips and the pruned system performs as good as the regular network in daytime wild fire detection, and it also works well on some night wild fire video clips.

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, a cepstrum-based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made.