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Yayın Compression of the biomedical images using quadtree-based partitioned universally classified energy and pattern blocks(Springer London, 2019-03-15) Gezer, Murat; Gargari, Sepideh Nahavandi; Güz, Ümit; Gürkan, HakanIn this work, an efficient low bit rate image coding/compression method based on the quadtree-based partitioned universally classified energy and pattern building blocks (QB-UCEPB) is introduced. The proposed method combines low bit rate robustness and variable-sized quantization benefits of the well-known classified energy and pattern blocks (CEPB) method and quadtree-based (QB) partitioning technique, respectively. In the new method, first, the QB-UCEPB is constructed in the form of variable length block size thanks to the quadtree-based partitioning rather than fixed block size partitioning which was employed in the conventional CEPB method. The QB-UCEPB is then placed to the transmitter side as well as receiver side of the communication channel as a universal codebook manner. Every quadtree-based partitioned block of the input image is encoded using three quantities: image block scaling coefficient, the index number of the QB-UCEB and the index number of the QB-UCPB. These quantities are sent from the transmitter part to the receiver part through the communication channel. Then, the quadtree-based partitioned input image blocks are reconstructed in the receiver part using a decoding algorithm, which exploits the mathematical model that is proposed. Experimental results show that using the new method, the computational complexity of the classical CEPB is substantially reduced. Furthermore, higher compression ratios, PSNR and SSIM levels are achieved even at low bit rates compared to the classical CEPB and conventional methods such as SPIHT, EZW and JPEG2000Yayın Adaptive convolution kernel for artificial neural networks(Academic Press Inc., 2021-02) Tek, Faik Boray; Çam, İlker; Karlı, DenizMany deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 × 3) kernels. This paper describes a method for learning the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagation-trainable Gaussian envelope which can grow or shrink in a base grid. Our experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network, a deeper residual network, and a U-Net architecture. The results in the popular image classification datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and ‘‘Faces in the Wild’’ showed that the adaptive kernels can provide statistically significant improvements on ordinary convolution kernels. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing ordinary convolution layers in a U-shaped network with 7 × 7 adaptive layers can improve its learning performance and ability to generalize.Yayın Enhancing real estate listings through image classification and enhancement: a comparative study(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-22) Küp, Eyüp Tolunay; Sözdinler, Melih; Işık, Ali Hakan; Doksanbir, Yalçın; Akpınar, GökhanWe extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). Each model’s performance and benchmark results were measured to identify the most effective method. In addition, the classification pipeline was expanded using image enhancement with contrastive unsupervised representation learning (CURL). This method assessed the impact of improved image quality on classification accuracy and the overall attractiveness of property listings. For each classification model, the performance was evaluated in binary conditions, with and without the application of CURL. The results showed that applying image enhancement with CURL enhances image quality and improves classification performance, particularly in models such as CNN and ResNet. The study results enable a better visual representation of real estate properties, resulting in higher-quality and engaging user listings. They also underscore the importance of combining advanced image processing techniques with classification models to optimize image presentation and categorization in the real estate industry. The extended platform offers information on the role of machine learning models and image enhancement methods in technology for the real estate industry. Also, an alternative solution that can be integrated into intelligent listing systems is proposed in this study to improve user experience and information accuracy. The platform proves that artificial intelligence and machine learning can be integrated for cloud-distributed services, paving the way for future innovations in the real estate sector and intelligent marketplace platforms.
