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  • Sinem Aslan
Sinem Aslan

Sinem Aslan
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Sinem verified their affiliation via an institutional email.
University of Milan | UNIMI · Department of Sciences of History and Historical Documentation (Medieval, Modern, Contemporary)

Ph.D. in Computer Science

About

47
Publications
14,786
Reads
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1,169
Citations
Additional affiliations
July 2004 - April 2022
Ege University
Position
  • Research Assistant
May 2022 - September 2024
Ca' Foscari University of Venice
Position
  • Assistant Professor
January 2014 - February 2015
Boğaziçi University
Position
  • Visiting Researcher

Publications

Publications (47)
Article
Good representative dictionaries is the most critical part of the BoVW: Bag of Visual Words scheme, used for such tasks as category identification. The paradigm of learning dictionaries from datasets is by far the most widely used approach and there exists a plethora of methods to this effect. Dictionary learning methods demand abundant data, and w...
Conference Paper
Full-text available
Binary descriptors have been very popular in recent years. One reason is that the algorithms that use them become computationally and memory-wise efficient. Furthermore, they tend to have some inherent robustness against some geometrical variations and against various brightness changes. These changes might result from both internal factors and ext...
Conference Paper
Full-text available
We propose a new local image descriptor named SymPaD for image understanding. SymPaD is a probability vector associated with a given image pixel and represents the attachment of the pixel to a previously designed shape repertoire. As such the approach is model-driven. The SymPad descriptor is illumination and rotation invariant, and extremely flexi...
Conference Paper
Full-text available
In this study we propose a model-driven codebook generation method used to assign probability scores to pixels in order to represent underlying local shapes they reside in. In the first version of the symbol library we limited ourselves to photometric and similarity transformations applied on eight prototypical shapes of flat plateau, ramp, valley,...
Conference Paper
Full-text available
This paper examines the performance of Hidden Markov Tree model based weights in reconstruction quality for an existing task-aware compressive video coding system which aims object detection specifically. The existing system utilizes weights in reconstruction which are computed by tracking of the foreground object. The proposed system acquires simi...
Chapter
Reassembling 3D broken objects is a challenging task. A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We propose a method that tackles the pairwise assembly of 3D point clouds, that is agnostic on the type of object, and that relies solely on their geometrical information, w...
Preprint
Full-text available
This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for 2D and 3D puzzle solving. The fragments and fractures are realistic, caused by a collapse of a fres...
Preprint
Full-text available
Jigsaw puzzle solving is a challenging task for computer vision since it requires high-level spatial and semantic reasoning. To solve the problem, existing approaches invariably use color and/or shape information but in many real-world scenarios, such as in archaeological fresco reconstruction, this kind of clues is often unreliable due to severe p...
Article
Full-text available
Climate change presents a significant challenge to lagoon ecosystems, which are highly valued coastal environments known for their provision of unique ecosystem services. As important as fragile, lagoons are vulnerable to both natural processes and anthropogenic activities, and this vulnerability is exacerbated by the impacts of climate change, whi...
Conference Paper
Full-text available
Archaeological fragment processing is crucial to support the analysis of pictorial contents of broken artefacts. In this paper, we focus on the unexplored task of semantic segmentation of fresco fragments. This task enables the extraction of semantic information from a fragment, facilitating subsequent tasks like fragment classification or reassemb...
Preprint
Full-text available
Reassembling 3D broken objects is a challenging task. A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We propose a method that tackles the pairwise assembly of 3D point clouds, that is agnostic on the type of object, and that relies solely on their geometrical information, w...
Article
Full-text available
This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to c...
Chapter
Full-text available
Recognizing the emotion an image evokes in the observer has long attracted the interest of the community for its many potential applications. However, it is a challenging task mainly due to the inherent complexity and subjectivity of human feelings. Such a difficulty is exacerbated in the domain of visual arts, mainly because of their abstract natu...
Article
Eutrophication represents an important ecological and environmental issue in coastal lagoons. This paper presents an extensive study of recurrent cell and network architectures to model eutrophication processes in the Venice lagoon, a very complex and fragile ecosystem that has been strongly altered by anthropic activities over years. Experimental...
Article
Global warming is exacerbating weather, and climate extremes events and is projected to aggravate multi-sectorial risks. A multiplicity of climate hazards will be involved, triggering cumulative and interactive impacts on a variety of natural and human systems. An improved understanding of risk interactions and dynamics is required to support decis...
Article
Full-text available
Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publica...
Preprint
Full-text available
What is the creative process through which an artist goes from an original image to a painting? Can we examine this process using techniques from computer vision and pattern recognition? Here we set the first preliminary steps to algorithmically deconstruct some of the transformations that an artist applies to an original image in order to establis...
Article
Full-text available
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the perf...
Preprint
Full-text available
Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence. Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation (VVSD). Here, the sense of a verb is...
Article
Full-text available
The problem of food segmentation is quite challenging since food is characterized by intrinsic high intra-class variability. Also, segmentation of food images taken in-the-wild may be characterized by acquisition artifacts, and that could be problematic for the segmentation algorithms. A proper evaluating of segmentation algorithms is of paramount...
Preprint
Full-text available
Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publica...
Preprint
Full-text available
[https://arxiv.org/abs/2001.06535] Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL mod...
Article
In this work we tackle the problem of automatic recognition of ancient coin types using a semisupervised learning method, namely Graph Transduction Games. Such problem is complex, mainly due to the low inter-class and large intra-class variations and the task becomes even more complex due to lack of labeled large datasets from certain ancient ages....
Preprint
The availability of large-scale data sets is an essential pre-requisite for deep learning based semantic segmentation schemes. Since obtaining pixel-level labels is extremely expensive, supervising deep semantic segmentation networks using low-cost weak annotations has been an attractive research problem in recent years. In this work, we explore th...
Chapter
Full-text available
The availability of large-scale data sets is an essential prerequisite for deep learning based semantic segmentation schemes. Since obtaining pixel-level labels is extremely expensive, supervising deep semantic segmentation networks using low-cost weak annotations has been an attractive research problem in recent years. In this work, we explore the...
Conference Paper
Full-text available
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algor...
Preprint
Full-text available
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algor...
Chapter
Full-text available
In this chapter, cellular imaging is considered from Medical Biometrics of cells. In particular, the chapter brings together different aspects of the cellular imaging from microscopy to cell biology, and from image processing to genomics.
Preprint
Full-text available
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the si...
Preprint
Full-text available
Recognizing the type of an ancient coin requires theoretical expertise and years of experience in the field of numismatics. Our goal in this work is automatizing this time consuming and demanding task by a visual classification framework. Specifically, we propose to model ancient coin image classification using Graph Transduction Games (GTG). GTG c...
Preprint
Full-text available
Recognizing the type of an ancient coin requires theoretical expertise and years of experience in the field of numismatics. Our goal in this work is automatizing this time consuming and demanding task by a visual classification framework. Specifically, we propose to model ancient coin image classification using Graph Transduction Games (GTG). GTG c...
Conference Paper
Full-text available
Food image analysis has been one of the most important tasks accomplished for automatic dietary monitoring. In this work, we address semantic segmentation of food images with Deep Learning. Additionally, we explore food and non-food segmentation by getting advantage of supervised learning. Specifically, we have experimented SegNet model on these tw...
Conference Paper
Full-text available
We designed SymPaD framework, a model-driven visual dictionary construction and description method, with new shape models and quantized shape library. We demonstrate that, with this new design, the most current model-driven dictionary construction method is outperformed with even smaller dictionary in object recognition and image retrieval tasks.
Conference Paper
In this study we show empirically on a Turkish corpus that Zipf's power laws relating frequencies of words to rank, vocabulary and number of meanings can also be derived from term x document analysis. This supports the idea put forward by Ding that Zipfian distributions and SVD carry similar hidden (latent) semantics.
Conference Paper
Full-text available
In this study, compressed sensing concepts are applied to multi-view video coding. Existing work from single view video is utilized to develop efficient GOP patterns and reference framing for stereo coding. It has been observed that the most typical choice of pattern improved the characteristics 0.4 dB with respect to the model that do not benefit...
Conference Paper
A new face region extraction method is proposed in this study. The leading property of this method is its application simplicity. Performance success of 65% is achieved with this method and in order to improve this method for images which include multi-faces, we aim to incorporate Zernike moments as a future work.
Thesis
Full-text available
Due to widespread use of the Internet, efficient management of multimedia databases has attracted many researchers. Variety of techniques including database indexing, classification and feature extraction are developed. In this study, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods that have been widely used for fa...

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