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Öğe Automatic System for Sheepskin Quality Control with Convolutional Neural Network(IEEE, 2019) Celik, Agit; Ugurlu, Yagiz; Gozukucuk, Emre; Sonmez, Elena BattiniThis paper tackles the non-conventional problem of industrial inspection for sheepskin quality checking, which is a sub-field of the leather garment industry. Image processing methods are used in the production lines of several industrial fields for quality checking and production. However, the number of automatic systems is still very limited due to the complexity of the problem, which requires to be addressed. This paper proposes a new neural network-based algorithm to perform the automatic detection of defective parts on sheepskins. The presented method is tested on a newly created database of sheepskins.Öğe Classification with Emotional Faces via a Robust Sparse Classifier(IEEE, 2012) Sonmez, Elena Battini; Sankur, Bulent; Albayrak, SongulWe consider the problem of emotion recognition in faces as well as subject identification in the presence of emotional facial expressions. We propose alternative solutions for this identification and recognition problems using the idea of sparsity, in terms of Sparse Representation based Classifier (SRC) paradigm. In both cases, the problem is formulated as finding the most parsimonious set of representatives from a training set, which will best reconstruct the test image. For emotion classification, we considered the six fundamental states and the SRC performance was compared with that of the Active Appearance Model (AAM) algorithm [1]. For face recognition displaying various emotions, in order to test the robustness of SRC, we considered gallery faces of subjects having one or more expression variety while the probe faces had a different expression. We experimented with both the whole faces or faces observed with multiple blocks. The SRC algorithm, while not demanding any training, performed surprisingly well in both emotion identification across subjects and subject identification across emotions.Öğe Convolutional neural networks with balanced batches for facial expressions recognition(Spie-Int Soc Optical Engineering, 2017) Sonmez, Elena Battini; Cangelosi, AngeloThis paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.Öğe Critical parameters of the sparse representation-based classifier(Inst Engineering Technology-Iet, 2013) Sonmez, Elena Battini; Albayrak, SongulIn recent years, the growing attention in the study of the compressive sensing (CS) theory suggested a novel classification algorithm called sparse representation-based classifier (SRC), which obtained promising results by casting classification as a sparse representation problem. Whereas SRC has been applied to different fields of applications and several variations of it have been proposed, less attention has been given to its critical parameters, that is, measurements correlated to its performance. This work underlines the differences between CS and SRC, it gives a mathematical definition of five measurements possible correlated to the performance of SRC and identifies three of them as critical parameters. The knowledge of the critical parameters is necessary to fuse multiple scores of SRC classifiers allowing for classification. The authors addressed the problem of two-dimensional face classification: using the Extended Yale B dataset to monitor the critical parameters and the Extended Cohn-Kanade database to test the robustness of SRC with emotional faces. Finally, the authors increased the initial performance of the holistic SRC with a block-based SRC, which uses one critical parameter for automatic selection of the most successful blocks.Öğe EMRES: A New EMotional RESpondent Robot(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Sonmez, Elena Battini; Han, Hasan; Karadeniz, Oguzcan; Dalyan, Tugba; Sarioglu, BaykalThe aim of this work is to design an artificial empathetic system and to implement it into an EMotional RESpondent (EMRES) robot, called EMRES. Rather than mimic the expression detected in the human partner, the proposed system achieves a coherent and consistent emotional trajectory resulting in a more credible human-agent interaction. Inspired by developmental robotics theory, EMRES has an internal state and a mood, which contribute in the evolution of the flow of emotions; at every episode, the next emotional state of the agent is affected by its internal state, mood, current emotion, and the expression read in the human partner. As a result, EMRES does not imitate, but it synchronizes to the emotion expressed by the human companion. The agent has been trained to recognize expressive faces of the FER2013 database and it is capable of achieving 78.3% performance with wild images. Our first prototype has been implemented into a robot, which has been created for this purpose. An empirical study run with university students judged in a positive way the newly proposed artificial empathetic system.Öğe Face Classification via Sparse Approximation(Springer-Verlag Berlin, 2011) Sonmez, Elena Battini; Sankur, Bulent; Albayrak, SongulWe address the problem of 2D face classification under adverse conditions. Faces are difficult to recognize since they are highly variable due to such factors as illumination, expression, pose, occlusion and resolution. We investigate the potential of a method where the face recognition problem is cast as a sparse approximation. The sparse approximation provides a significant amount of robustness beneficial in mitigating various adverse effects. The study is conducted experimentally using the Extended Yale Face B database and the results are compared against the Fisher classifier benchmark.Öğe Fake Image Detection Using DCT and Local Binary Pattern(IEEE, 2019) Kunbaz, Ayah; Saghir, Souzi; Arar, Mira; Sonmez, Elena BattiniIn the technological era, digital images occupy an important position in different life's fields and image tampering has become affordable effortlessly, which results in a widespread of tampered and fake images through the internet and social media specifically. There are many techniques for image manipulation, some of the well-known methods are splicing and copy-move. Splicing can be defined as cutting part from an image and pasting it into another picture, while copy-move is about copying part of an image and pasting it into the same picture. This paper challenges splicing and copy -move forgery detection methods on CASIA TIDE databases. The proposed method is based on Local Binary Pattern (LBP), and 2D Discrete Cosine Transform (DCT), which are used for feature extraction. Afterward, a Support Vector Machine (SVM) classifier distinguishes real and manipulated images. Initial performance was increased by applying Local Binary Pattern (LBP) to the whole image rather than in a block-based fashion. The proposed model reaches the-state of- the-art in the CASIA TIDE v1.0 database with remarkable results in terms of accuracy.Öğe Feeling Analysis for Sadness and Happiness using Google n-gram Database(IEEE, 2018) Donmez, Ilknur; Sonmez, Elena BattiniThe current era has been defined as Digital Age and Information Age since it is characterized by an exponential grow of data, generated by both human, i.e. social environments, and machine, i.e. Internet of things. The challenge is to convert data into information, by analyzing the data and discovering patterns hidden inside it. In this paper the two basic human feelings of Happiness and Sadness are extracted from a subset of Google n-grams corpus and analyzed. Google n-grams corpus is generated from millions of scanned books published between year 1500 and 2008; it can be considered as an indicator for human specific feature and behavior. Under the hypothesis that user's emotion can he extrapolated by the frequency of the corresponding emotional words, this study applies regression to predict the importance of the Happiness and Sadness emotional states in future years.Öğe Image Captioning in Turkish Language(IEEE, 2019) Yilmaz, Berk Dursun; Demir, Ali Emre; Sonmez, Elena Battini; Yildiz, TugbaImage captioning is one of the everlasting challenging tasks in the field of artificial intelligence that requires computer vision and natural language processing. Plenty of salient works have been proposed throughout the time for English language however, the number of studies in Turkish language is still too limited. This paper couples an encoder CNN-the component that is responsible for extracting the features of the given images-, with a decoder RNN -the component that is responsible for generating captions using the given inputs-to generate Turkish captions within human gold-standards. We conducted the experiments using the most common evaluation metrics such as BLEU, METEOR, ROUGE and CIDEr. Results show that the performance of the proposed model is satisfactory in both qualitative and quantitatively evaluations. A Web App is already deployed to allow volunteers to contribute to the improvements of the Turkish captioned dataset.Öğe A Model for an Emotional Respondent Robot(Springer International Publishing Ag, 2018) Sancar, Ayse E.; Sonmez, Elena BattiniThe aim of this study is to design an emotional regulation model based on facial expressions. It is argued that emotions serve a critical function in intelligent behavior and some researchers posed the questions of whether a robot could be intelligent without emotions. As a result, emotion recognition and adequate reaction are essential requirements for enhancing the quality of human robot interaction. This study proposes a computational model of emotion capable of clustering the perceived facial expression, and using cognitive reappraisal to switch its internal state so as to give a human-like reaction over the time. That is, the agent learns the person's facial expression by using Self Organizing Map, and gives it a meaning by mapping the perceived expression into its internal state diagram. As a result, the presented model implements empathy with the aim to enhance human-robot communication.Öğe Object Detection in Shelf Images with YOLO(IEEE, 2019) Melek, Ceren Gulra; Sonmez, Elena Battini; Albayrak, SongulObject detection in shelf images can solve many problems in retails sales such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously. This study aims to detect object in shelf images with deep learning algorithms. Firstly, object detection algorithms and datasets are examined in the literature. Then, experimental study is performed using Coca Cola images obtained from Imagenet and Grocery dataset with YOLO (You Only Look Once) algorithm. Results of the study are discussed from different sides such as number of classes, threshold values and numder of iteration.Öğe The segmented UEC Food-100 dataset with benchmark experiment on food detection(Springer, 2023) Sonmez, Elena Battini; Memis, Sefer; Arslan, Berker; Batur, Okan ZaferAutomatic food classification systems have several interesting applications ranging from detecting eating habits, to waste food management and advertisement. When a food image has multiple food items, the food detection step is necessary before classification. This work challenges the food detection issue and it introduces to the research community the Segmented UEC Food-100 dataset, which expands the original UEC Food-100 database with segmentation masks. In the semantic segmentation experiment, the performance of YOLAC and DeeplabV3+ has been compared and YOLAC reached the best accuracy of 64.63% mIoU. In the instance segmentation experiment, YOLACT has been used due to its speed and high accuracy. The benchmark performance on the newly released Segmented UEC Food-100 dataset is 68.83% mAP. For comparison purpose, experiments have been run also on the UEC FoodPix Complete dataset of Okamoto et al. The database and the code will be available after publication.Öğe A Survey of Product Recognition in Shelf Images(IEEE, 2017) Melek, Ceren Gulra; Sonmez, Elena Battini; Albayrak, SongulNowadays, merchandising is one of the significant method which allows to increase the sales. Therefore, activities such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously have become important. An autonomous system is needed to automate operations such as product or brand recognition, stock tracking and planogram matching. In the literature, it is seen that many studies have been carried out in order to address this issue. This survey classifies and compares all existing works with the aim to guide researchers working on merchandising.Öğe Towards a New Computational Affective System for Personal Assistive Robots(IEEE, 2020) Sonmez, Elena Battini; Kose, Hatice; Barkana, Duygun ErolThe need of social interaction between human and robot is extensively highlighted in recent studies involving social robots. Language, emotions, postures, and gestures are commonly used to increase the quality of human-computer interaction. In this study, we focus on the design of a cognitive architecture to model the emotions and the dynamics of them to implement artificial empathy during human-computer interaction. Human-like empathy is considered as an emergent behavior based on social interaction with humans, gut feelings, mirroring system, and association between external stimuli and emotions in the developmental robotics theory. Our study uses developmental robotics theory and it presents a simulation of the internal emotional states of an agent/robot. Furthermore, our study demonstrates a model of the changes of the affective state of the robot from one emotion to another, in synchronization with the emotions expressed by its human partner. The robot can adjust its inner state and mood in harmony to the emotional state of the human partner after training. The simulations are performed and the proposed computational affective system is evaluated by the human participants subjectively.
