This paper systematically explores the applications of image processing techniques in machined surface analysis, a critical area in industries like manufacturing, aerospace, automotive, and healthcare. It examines the integration of image processing in traditional Computer Numerical Control (CNC) machining and micromachining, focusing on its role in tool wear analysis, workpiece detection, automatic CNC programming, and defect inspection. With AI and machine learning advancements, these technologies enhance defect detection, surface texture analysis, predictive maintenance, and quality optimization. The paper also discusses future advancements in high resolutions, 3D imaging, augmented reality, and Industry 4.0, highlighting their impact on productivity, precision, and challenges such as data privacy. In conclusion, image processing remains vital to improving manufacturing efficiency and quality control.

In this study, the skull bone thicknesses of 150 patients ranging in age from 0 to 72 years were calculated using a novel approach (thermal analysis), and thickness changes were analyzed. Unlike conventional thickness calculation approaches (Beam Propagation, Hildebrand), a novel heat transfer-based approach was developed. Firstly, solid 3D objects with different thicknesses were modeled, and thermal analyses were performed on these models. To better understand the heat transfer of 3D object models, finite element models (FEM) of the human head have been reported in the literature. The FEM can more accurately model the complex geometry of a 3D human head model. Then, thermal analysis was performed on human skulls using the same methods. Thus, the skull bone thicknesses at different ages and in different genders from region to region were determined. The skull model was transferred to ANSYS, and it was meshed using different mapping parameters. The heat transfer results were determined by applying different heat values to the inner and outer surfaces of the skull mesh structure. Thus, the average thicknesses of skull regions belonging to a certain age group were obtained. With this developed method, it was observed that the temperature value applied to the skull was proportional to the thickness value. The average thickness of skull bones for men (frontal: 7.8 mm; parietal: 9.6 mm; occipital: 10.1 mm; temporal: 6 mm) and women (frontal: 8.6 mm; parietal: 10.1 mm; occipital: 10 mm; temporal: 6 mm) are given. The difference (10%) between men and women appears to be statistically significant only for frontal bone thickness. Thanks to the developed method, bone thickness information at any desired point on the skull can be obtained numerically. Therefore, the proposed method can be used to help pre-operative planning of surgical procedures.

Fish is important for human health due to its high nutritional value. However, it is prone to spoilage due to its structural characteristics. Traditional freshness assessment methods, such as visual inspection, are subjective and prone to inconsistency. This study proposes a novel, cost-effective hybrid methodology for automated three-level fish freshness classification (Day 1, Day 2, Day 3) by integrating single-wavelength laser reflectance data with deep learning-based image features. A comprehensive dataset was created by collecting visual and laser data from 130 mackerel specimens over three consecutive days under controlled conditions. Image features were extracted using four pre-trained CNN architectures and fused with laser features to form a unified representation. The combined features were classified using SVM, MLP, and RF algorithms. The experimental results demonstrated that the proposed multimodal approach significantly outperformed single-modality methods, achieving average classification accuracy of 88.44%. This work presents an original contribution by demonstrating, for the first time, the effectiveness of combining low-cost laser sensing and deep visual features for freshness prediction, with potential for real-time mobile deployment.

In this study, the methods which can detect the basic physical movements of a person (downward, upward, sitting, stop, walking, running) from inertial sensor data are evaluated. The performances of classical and current approaches to map IMU data to activity classes were compared. A three-stage study was carried out for this aim: 1) data acquisition; 2) creating training/test sets; 3) construction and classification of network architectures. At the stage of data acquisition, to obtain 6 different physical movements from 10 different people, the accelerometer sensor is placed on the persons. Repetitive movements of persons were recorded. At the second stage, the recorded long-term accelerometer data is divided into packages in the form of short-term windows. The training set of classical approaches was constructed by features extracting from each packet data containing one-dimensional acceleration information. The transformation of one-dimensional signals to a two-dimensional image matrix for the training set of the deep learning-based approaches was performed. In the third stage, ANN, SVM, k-NN and CNN architectures were constructed, and classification process was carried out. As a result of the experimental studies, it was found that the accuracy of IMU-activity mapping was 99% with the ANN method and 95% with the CNN method.

Medical professionals need methods that provide reliable information in diagnosing and monitoring neurological diseases. Among such methods, studies based on medical image analysis are essential among the active research topics in this field. Tumor segmentation is a popular area, especially with magnetic resonance imaging (MRI). Early diagnosis of tumours plays an essential role in the treatment process. This situation also increases the survival rate of the patients. Manually segmenting a tumour from MR images is a difficult and time-consuming task within the anatomical knowledge of medical professionals. This has necessitated the need for automatic segmentation methods. Convolutional neural networks (CNN), one of the deep learning methods that provide the most advanced results in the field of tumour segmentation, play an important role. This study, tumor segmentation was performed from brain and heart MR images using CNN-based U-Net and ResNet50 deep network architectures. In the segmentation process, their performance was tested using Dice, Sensitivity, PPV and Jaccard metrics. High performance levels were sequentially achieved using the U-Net network architecture on brain images, with success rates of approximately 98.47%, 98.1%, 98.85%, and 96.07%

Günümüz veritabanları hızlı bir şekilde büyümektedir. Örneğin Youtube’a her dakikada ortalama 300 saatlik video yüklenmektedir. Veri boyutuyla orantılı bir şekilde, işleme, depolama ve transfer maliyetleri artmaktadır. Buna karşılık, özellikle video ve imge gibi yüksek boyutlu veri içeriklerinin büyük oranda benzer olduğu bilinmektedir. Bu tür yüksek boyutlu ham verilerin, düşük boyutlara indirgenmesi, imge sınıflandırma, algılama ve anlamlı bilgi çıkarım prosesleri için hayati öneme sahiptir. Veri boyutunu indirgeyen çok sayıda teknik mevcuttur. Klasik yapay öğrenme tekniklerinden; PCA (Temel Bileşenler Analizi) ve LDA (Doğrusal Ayıraç Analizi), probleme matematiksel bir çözüm zemini kazandırdıkları için ön plana çıkarken, doğrusal olmayan tekniklerden, derin öğrenme yaklaşımlarından olan Oto-Kodlayıcı (Auto-Encoding), büyük verilerin indirgenmesine izin vermesi bakımından araştırmacıların ilgisini çekmektedir. Bu çalışmada, gerçek ve sentetik veriler (doğrusal ve doğrusal olmayan) kullanılarak PCA, LDA ve Auto-Encoding (AE) yöntemlerinin boyut indirgeme performansları incelenmiştir. Belirli kıstaslarda (harcanan zaman, yeniden inşa etme doğruluğu vb.) alınan sonuçlar karşılaştırmalı bir şekilde sunulmuştur.