SURF ve MSER Kombinasyonu ile Kopya Taşı Sahteciliği Algılama

Abstract

Because digital images may contain a variety of data, they are regarded as an important source for information sharing. Also, images are widely used as evidence in a variety of real-life cases. The rapid rise in popularity of digital photographs is due to the improvement of technologies. Several software programs have been developed in recent years to modify digital images, such as Photoshop and Corel Photo, however these programs are now being used extensively for forgery. Because of technological advancements, it is difficult for people to recognize faked images with their naked eyes Therefore, in this study, the features used in forgery detection problems are combined to ensure accurate labeling of even forgery images that are difficult to detect. Stronger feature is obtained by combining Speeded-Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER). Considering the experimental results; it has been observed that the use of the proposed method, which is obtained as a result of combining the two methods in copy-move forgery detection problems, is more successful than using the SURF and MSER features separately.

Lightweight Non-Destructive Detection of Diseased Apples Based on Structural Re-Parameterization Technique

“…Traditional target detection methods, such as sliding windows and manual feature extraction, are exemplified by techniques like Haar [3], HOG [4], Hu moment [5], SIFT [6], SURF [7], and DPM [8]. The evolution of computer vision and deep learning has ushered target detection into agricultural production prominence, with algorithms bifurcated into single-stage (e.g., YOLO series [9][10][11], SSD series [12][13][14], RetinaNet series [15,16]) and two-stage detection algorithms (e.g., RCNN series [17], FasterRCNN series [18]).…”