ÖZYapılan çalışmada, ortamda bulunan nesnelerin gerçek zamanlı olarak tespit edilmesi, sınıflandırılması ve elde edilen sonuçlar sunulmaktadır. Önerilen yönteme ait deneysel çalışmaların gerçekleştirilmesinde fındık meyvesi kullanılmaktadır. Çalışma ortamında bulunan fındıklara ait görüntü, kamera ile alındıktan sonra görüntü işleme teknikleri kullanılarak işlenmektedir. Fındıkların görüntü düzlemi üzerinde kapladıkları boyut ve alan verileri hesaplanmaktadır. Elde edilen veriler değerlendirilerek, fındıklar gerçek zamanlı olarak küçük (K1), orta (K2) ve büyük (K3) olmak üzere üç sınıfa ayrılmaktadır. Bu işlem ortalama tabanlı sınıflandırma ve K-means kümeleme yöntemleri kullanılarak gerçekleştirilmektedir. Küme merkezlerinin belirlenmesi ve sınıflandırma işlemi fındık meyvesi verilerinden elde edilen bilgi veritabanı kullanılarak sağlanmaktadır. Çalışma ortamında bulunan fındık meyveleri, görüntü işleme teknikleri kullanılarak %100 başarımla tespit edilmektedir. Fındık meyvelerinin, ortalama tabanlı ve K-means kümeleme yöntemleri kullanılarak sınıflandırılması karşılaştırılmaktadır. Karşılaştırma sonucunda, gerçeklenen iki yöntemin %90 ile %100 oranında benzerlik gösterdiği bulunmaktadır.Anahtar Kelimeler: Görüntü İşleme, Nesne Tespiti, Morfoloji, Moment, Kümeleme Detection and classification of hazelnut fruit by using image processing techniques and clustering methods ABSTRACTIn this study, the objects found in the environment are detected and classified in real time, the results obtained are presented. Hazelnut fruit is used in the experimental studies of the proposed method. The image belongs to hazelnut that is in a work environment is taken with the camera, it is processed by using image processing techniques. The size and area data of hazelnut on the image plane is calculated. By evaluating the obtained data, the hazelnut is divided into three classes as small (K1), medium (K2) and big (K3) in real time application. This process is performed using mean-based classification and K-means clustering methods. Detection and classification of cluster centers is provided by using the information database obtained from the data of hazelnut fruit. Hazelnut fruits found in the experimental environment are determined with 100% accuracy using image processing techniques. The classification of hazelnut fruits using the mean-based and K-means clustering methods has been compared. As a result of the comparison, it is observed that the two methods realized are similar ratio of 90% to 100%.

Data hiding called steganography is a security technique to protect secret data throughout the transmission from malicious attackers. The purposes of steganography are to obtain good stego-image quality, high embedding-capacity, low computational complexity, visual imperceptibility, undetectability, and more security. In this paper, we offer a new hybrid image steganography technique based on least significant bit (LSB) substitution and enhanced modified signed digit (EMSD) algorithms. The proposed algorithm utilizes n adjacent cover image pixels to hide the secret data with EMSD algorithm, and least significant k-bit for LSB substitution algorithm. Hence, it has more embedding capacity than the EMSD algorithm and exploiting modification direction (EMD) based algorithms. We obtain that the stego-image quality is better than 43 dB when the payload is 2.404 bpp. The results of experiment represent that this algorithm ensures high embedding-capacity while preserving acceptable visual stego image quality that can be undetectable by human eyes. Also, the hybrid of the EMSD and LSB substitution algorithms is to difficult for malicious people to consolidate data by scrambling secret data bits. INDEX TERMS Data hiding, Data security, EMD (exploiting modification direction), EMSD (enhanced modified signed digit), GEMD (generalized exploiting modification direction), LSB (least significant bit), SMSD (sparse modified signed digit),

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In this study, real time industrial application of single board computer based color detection system is realized. In this system, BeagleBoard-xM as a single board computer, a USB camera, a conveyor belt and an LCD7 touch screen are used. OpenCV is used as an image processing library in this color detection system. The main goal of this study is to define the number of different colored packages passing on the conveyor belt according to their color. Then, real time results of the number of the packages and the total package number are displayed on the LCD7 touch screen. At the same time, the USB camera image of the related package on the conveyor belt is monitorized on the same touch screen. If no image of any packages is taken by the USB camera during 60 seconds, the system is turned off.

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