Dynamical System Parameter Identification using Deep Recurrent Cell Networks

In this paper, we investigate the parameter identification problem in dynamical systems through a deep learning approach. Focusing mainly on second-order, linear time-invariant dynamical systems, the topic of damping factor identification is studied. By utilizing a six-layer deep neural network with different recurrent cells, namely GRUs, LSTMs or BiLSTMs; and by feeding input-output sequence pairs captured from a dynamical system simulator, we search for an effective deep recurrent architecture in order to resolve damping factor identification problem. Our study results show that, although previously not utilized for this task in the literature, bidirectional gated recurrent cells (BiLSTMs) provide better parameter identification results when compared to unidirectional gated recurrent memory cells such as GRUs and LSTM. Thus, indicating that an input-output sequence pair of finite length, collected from a dynamical system and when observed anachronistically, may carry information in both time directions for prediction of a dynamical systems parameter.Comment: Final version published in Journal of Neural Computing and Application

Comparison of Infrared and Visible Imagery for Object Tracking: Toward Trackers with Superior IR Performance

The subject of this paper is the visual object tracking in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble based tracking method that is tuned to IR data. The proposed algorithm sequentially constructs and maintains a dynamical ensemble of simple correlators and produces tracking decisions by switching among the ensemble correlators depending on the target appearance in a computationally highly efficient manner We empirically show that our algorithm significantly outperforms the state-of-the-art trackers in our extensive set of experiments with IR imagery

Türkçe dudak hareketlerinin ve yüz ifadelerinin 3 boyutlu ortamda benzetimi ve bir Türkçe ses makinasıyla eş zamanlı hale getirilmesi.

In this thesis, 3D animation of human facial expressions and lip motion and their synchronization with a Turkish Speech engine using JAVA programming language, JAVA3D API and Java Speech API, is analyzed. A three-dimensional animation model for simulating Turkish lip motion and facial expressions is developed. In addition to lip motion, synchronization with a Turkish speech engine is achieved. The output of the study is facial expressions and Turkish lip motion synchronized with Turkish speech, where the input is Turkish text in Java Speech Markup Language (JSML) format, also indicating expressions. Unlike many other languages, in Turkish, words are easily broken up into syllables. This property of Turkish Language lets us use a simple method to map letters to Turkish visual phonemes. In this method, totally 37 face models are used to represent the Turkish visual phonemes and these letters are mapped to 3D facial models considering the syllable structures. The animation is created using JAVA3D API. 3D facial models corresponding to different lip positions of the same person are morphed to each other to construct the animation. Moreover, simulations of human facial expressions of emotions are created within the animation. Expression weight parameter, which states the weight of the given expression, is introduced. The synchronization of lip motion with Turkish speech is achieved via CloudGarden®̕s Java Speech API interface. As a final point a virtual Turkish speaker with facial expression of emotions is created for JAVA3D animation.M.S. - Master of Scienc

Scale Invariant Sillhouette Features

In this study, a feature extractor and a global descriptor for closed planar curves, i.e. silhouettes, are proposed. Initially, the closed curve is arc-length sampled and the Gaussian scale-space is constructed. Using the absolute curvature values and orientations of the curves within the higher scale levels, scale invariant features are obtained. These features are transformed into a global descriptor, namely the feature images, and shape recognition is performed. The proposed method is evaluated using a ship silhouette image set and the results show good success rates with low computation burden

Eğrilik çlçek uzayı kullanarak 3D nesne tanıma.

In this thesis, a generic, scale and resolution invariant method to extract 3D features from 3D surfaces, is proposed. Features are extracted with their scale (metric size and resolution) from range images using scale-space of 3D surface curvatures. Different from previous scale-space approaches; connected components within the classified curvature scale-space are extracted as features. Furthermore, scales of features are extracted invariant of the metric size or the sampling of the range images. Geometric hashing is used for object recognition where scaled, occluded and both scaled and occluded versions of range images from a 3D object database are tested. The experimental results under varying scale and occlusion are compared with SIFT in terms of recognition capabilities. In addition, to emphasize the importance of using scale space of curvatures, the comparative recognition results obtained with single scale features are also presented.Ph.D. - Doctoral Progra

Shape recognition using orientational and morphological scale-spaces of curvatures

In this study, a scale-invariant representation for closed planar curves (silhouettes) is proposed. The orientations of all points within the Gaussian scale-space of the curve are extracted. This orientation scale-space is used to create the silhouette orientation image in which the positions of each pixel indicate the curve's pixel positions and scales, whereas the colour represents orientation. The representation is extracted for multiple levels of the morphological scale-space of the silhouette. The proposed representation is invariant to scale and transformable under planar rotation. Using linear and non-linear distance learning methods, experiments on the MPEG7, ETH80 and Kimia shape datasets were conducted, with results indicating an advanced recognition capability

3D face detection using transform invariant features

A generic, transform invariant 3D facial feature detection method based on mean (H) and Gaussian (K) curvature analysis is proposed. A scale space of the HK values is constructed differently from the previous HK attempts. The 3D features are extracted from this scale space and used in a global topology, which is trained with a Gaussian model using only faces with neutral and frontal poses. The model is then tested against 1323 faces with various poses and expressions. The method is compared with four other representative algorithms from the previous literature for 3D facial feature localisation and face detection purposes

An Analysis on the Effect of Skip Connections in Fully Convolutional Networks for License Plate Localization

In this study, the effect of the skip connections, which are seen in fully convolutional networks, on object localization is analyzed. For this purpose, a local data set for plate detection is created. Experiments are carried out using this data set. Due to the small size of the image set, data augmentation method is used to overcome the danger of over-fitting. The learning rates of the first layers are frozen for analysis and fine-tuning is applied to only the last layer and deconvolution layers. The results obtained are compared with the results of other image sets. The results indicate the importance of the information provided by the skip connections on object localization

Comparison of HK and SC Curvature Descriptions in a Scale-Space for the Purpose of 3D Object Recognition

Most 3D object recognition methods use mean-Gaussian curvatures (HK) [2] or shape index-curvedness (SC) [3] values for classification. Although these two curvature descriptions classify objects into same categories, their mathematical definitions vary. In this study a comparion between the two curvature description is carried out for the purpose of 3D object recognition. Since unlike S; H, K and C values are not invariant of scale and resolution, a method to set them fully invariant to any transforation is proposed. The results show that scale and resolution invariant HK curvatire values gives better recognition results compared to SC curvature values

3D Object Representation Using Transform and Scale Invariant 3D Features

An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output. This surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure