KARMAŞIK DALGACIK DÖNÜŞÜMÜ TABANLI YAPAY SİNİR AĞLARI İLE YÜZ ALGILAMA

Bu çalışmada yüz algılama için Gabor dalgacık dönüşümleri ve Çift Ağaç dalgacık dönüşümleri kullanılarak öznitelik çıkarımı yapılmıştır. Sınıflandırma basamağında ileri beslemeli yapay sinir ağları kullanılmıştır. Önerilen algoritmaların ilkinde, sinir ağlarını eğitmek için Çift Ağaç öznitelik vektörleri kullanılırken, ikincisinde sinir ağlarının eğitiminde Gabor öznitelik vektörleri kullanılmaktadır. Önerilen üçüncü algoritma ise ilk iki algoritmanın algı sonuçlarının OR mantık işlemi ile birleştirilmesinden oluşmaktadır. Sistemin başarımı yanlış algı oranının da hesaba katıldığı üç metrik ile hesaplanmıştır. MIT+CMU, FRAV2D, BioID, BANCA veri tabanları üzerinde simülasyonlar gerçekleştirilmiştir. Gabor dalgacık vektörlerinin boyutları farklı oranlara indirgenerek işlem zamanı ve performans üzerindeki etkileri incelenmiştir

Automatic Segmentation of Anatomical Structures from CT Scans of Thorax for RTP

Modern radiotherapy techniques are vulnerable to delineation inaccuracies owing to the steep dose gradient around the target. In this aspect, accurate contouring comprises an indispensable part of optimal radiation treatment planning (RTP). We suggest a fully automated method to segment the lungs, trachea/main bronchi, and spinal canal accurately from computed tomography (CT) scans of patients with lung cancer to use for RTP. For this purpose, we developed a new algorithm for inclusion of excluded pathological areas into the segmented lungs and a modified version of the fuzzy segmentation by morphological reconstruction for spinal canal segmentation and implemented some image processing algorithms along with them. To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert. The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets. We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets. Our method provides favorable results in the segmentation of CT scans of patients with lung cancer and can avoid heavy computational load and might offer expedited segmentation that can be used in RTP

VIDEO SCENE DETECTION USING DOMINANT SETS

In this paper, we propose a weighted undirected graph-based video scene detection method. The method is based on the idea of using the complete information of the graph. For this aim, each shot is represented by a vertex on the graph. Edge weights among vertices are evaluated by using spatial and temporal similarities of shots. Only a single video scene boundary which has the highest probability to be the correct one is determined and this scene boundary information is also used as a clue in the next steps. A tree-based peeling strategy is proposed to determine the boundaries of the remaining scenes. In order to test our graph-based video scene detection method, we used DVD chapters' information and promising results were obtained when compared to the results of the similar work presented in literature

Graph-based multilevel temporal segmentation of scripted content videos

This paper concentrates on a graph-based multilevel temporal segmentation method for scripted content videos. In each level of the segmentation, a similarity matrix of frame strings, which are series of consecutive video frames, is constructed by using temporal and spatial contents of frame strings. A strength factor is estimated for each frame string by using a priori information of a scripted content. According to the similarity matrix reevaluated from a strength function derived by the strength factors, a weighted undirected graph structure is implemented. The graph is partitioned to clusters, which represent segments of a video. The resulting structure defines a hierarchically segmented video tree. Comparative performance results of different types of scripted content videos are demonstrated

Video Content Analysis Using Dominant Sets

In this paper, a graph-based method for video content analysis is proposed The characteristics of the detected shots are investigated for news, commercial, animated cartoon, basketball and documentary videos and experimental studies are realized on these videos. The maximum clique on the weighted and undirected graph, which is constructed according to visual content, is tried being detected. it is inferred that specially in news and commercials, the proposed method can be used for temporal video segmentation

Graph-based multilevel temporal video segmentation

This paper presents a graph-based multilevel temporal video segmentation method. In each level of the segmentation, a weighted undirected graph structure is implemented. The graph is partitioned into clusters which represent the segments of a video. Three low-level features are used in the calculation of temporal segments' similarities: visual content, motion content and shot duration. Our strength factor approach contributes to the results by improving the efficiency of the proposed method. Experiments show that the proposed video scene detection method gives promising results in order to organize videos without human intervention

Effect of Sampling Rate on Transient Based RF Fingerprinting

10th International Conference on Electrical and Electronics Engineering (ELECO) -- NOV 30-DEC 02, 2017 -- Bursa, TURKEYWOS: 000426978800203In this paper, effect of sampling rate on the performance of transmitter identification system using transient-based RF fingerprints is considered. Two different existing RF fingerprinting techniques have been employed to investigate the performance of a transmitter identification system by using experimental data collected at a high sampling rate. Decimation was carried out to analyze the effect of lower sampling rates. It has been shown that transient-based RF fingerprinting methods can be effectively used for identification of wireless transmitters at low sampling rates.Chamber Elect Engineers Bursa Branch, Uludag Univ, Fac Engn, Dept Elect & Elect Engn, Istanbul Tech Univ, Fac Elect & Elect Engn, Sci & Technolog Res Council Turkey, IEEE Turkey Sec

RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum

WOS: 000459611100001Radio frequency (RF) fingerprinting is considered as one of the promising techniques to enhance wireless security in the Internet of Things (IoT) applications. In this paper, a low-complexity RF fingerprinting method for classification of wireless IoT devices is proposed. The method is based on the energy spectrum of the transmitter turn-on transient signals from which unique characteristics of wireless devices are extracted. The number of spectral components to be used is determined through a proposed approach based on the estimated transient duration value. Transient duration estimation is achieved from the smoothed versions of the instantaneous amplitude characteristics of transmitter signals, which are obtained through a sliding window averaging method. Classification performance of the proposed spectral fingerprints is assessed using experimental data and described by a confusion matrix. The discrimination effectiveness of the spectral fingerprints is quantified by a class separability criterion and evaluated for different noise levels through Monte Carlo simulations. It is demonstrated that the proposed fingerprints outperform the classification performance of two existing fingerprints especially at low signal-to-noise ratio. Additionally, computational complexity analysis of the classifier using the proposed fingerprints is provided

Dominant sets based movie scene detection

Multimedia indexing and retrieval has become a challenging topic in organizing huge amount of multimedia data. This problem is not a trivial task for large visual databases; hence, segmentation into low- and high-level temporal video segments might improve the realization of this task. In this paper, we introduce a weighted undirected graph-based movie scene detection approach to detect semantically meaningful temporal video segments. The method is based on the idea of finding the dominant scene of the video according to the selected low-level feature. The proposed method starts from obtaining the most reliable solution first and exploit each solution in the subsequent steps recursively. The dominant movie scene boundary, which can be the highest probability to be the correct one, is determined and this scene boundary information is also exploited in the subsequent steps. We handle two partitioning strategies to determine the boundaries of the remaining scenes. One is a tree-based strategy and the other is an order-based strategy. The proposed dominant sets based movie scene detection method is compared with the graph-based video scene detection methods presented in literature