Temu Kembali Citra Tenun Nusa Tenggara Timur menggunakan Esktraksi Fitur yang Robust terhadap Perubahan Skala, Rotasi, dan Pencahayaan

Ragam motif pada tenun Nusa Tenggara Timur (NTT) seperti flora, fauna dan geometris menjadi suatu keunikan yang dapat membedakan daerah asal dan jenis dari tenun tersebut. Pada penelitian ini, sistem temu kembali citra berbasis isi atau Content-Based Image Retrieval (CBIR) diimplementasikan pada citra tenun NTT sehingga user dapat mencari citra tenun pada database menggunakan citra query berdasarkan fitur visual yang terkandung dalam citra. Seringkali citra query yang diinputkan user memiliki skala, rotasi dan pencahayaan yang bervariasi, sehingga diperlukan suatu metode ektraksi fitur yang dapat mengakomodasi variasi tersebut. Sistem temu kembali citra tenun pada penelitian ini menggunakan model Bag of Visual Words (BoVW) dari keypoints pada citra yang diekstrak dengan metode Speeded Up Robust Feature (SURF). BoVW dibangun menggunakan K-Means untuk menghasilkan visual vocabulary dari keypoints pada seluruh citra training. Representasi BoVW diharapkan dapat menangani variasi skala dan rotasi pada citra. Sedangkan untuk mengatasi variasi pencahayaan pada citra, dilakukan perbaikan kualitas citra dengan menggunakan Contrast Limited Adaptive Histogram Equalization (CLAHE). Percobaan dilakukan dengan membandingkan kinerja dari representasi BoVW yang dibangun menggunakan fitur SURF dengan Maximally Stable Extremal Regions (MSER) pada temu kembali citra tenun. Hasil uji coba menunjukkan bahwa metode SURF menghasilkan rata-rata akurasi 89,86% dan waktu komputasi 9,94 detik, sedangkan MSER menghasilkan rata-rata akurasi 84,04% dan waktu komputasi 1,95 detik. AbstractThe variety of motifs in East Nusa Tenggara tenun such as flora, fauna and geometric is an unique thing that can distinguish the region of origin and type of the tenun. In this study, the Content-Based Image Retrieval (CBIR) system is implemented in the tenun image. With Content-based techniques Users can search tenun images on the image database by using query images based on visual features contained in the image. Often the query image that the user enters has a different scale, rotation and lighting, so a feature extraction method is needed that can accommodate these differences. The tenun image retrieval system in this study used the Bag of Visual Words (BoVW) model of the keypoints in the extracted image using the Speeded Up Robust Feature (SURF) method. BoVW was built using K-Means to produce visual vocabulary from keypoints on all training images. The representation of BoVW is expected to be able to handle scale variations and rotations in images. Whereas to overcome the lighting variations in the image, image quality improvement is done by using Contrast Limited Adaptive Histogram Equalization (CLAHE). The experiment was conducted by comparing the performance of the BoVW representation which was built using the SURF feature with Maximally Stable Extremal Regions (MSER) at the tenun image retrieval. The results of the trial showed that SURF obtained higher accuracy in all conditions of tenun image data with an average value of 89.86% whereas MSER obtained an average accuracy value of 84.04%. But MSER's computation time is 1.95 seconds faster than SURF which is 9.94 seconds

Um estudo comparativo das abordagens de detecção e reconhecimento de texto para cenários de computação restrita

Orientadores: Ricardo da Silva Torres, Allan da Silva PintoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Textos são elementos fundamentais para uma efetiva comunicação em nosso cotidiano. A mobilidade de pessoas e veículos em ambientes urbanos e a busca por um produto de interesse em uma prateleira de supermercado são exemplos de atividades em que o entendimento dos elementos textuais presentes no ambiente são essenciais para a execução da tarefa. Recentemente, diversos avanços na área de visão computacional têm sido reportados na literatura, com o desenvolvimento de algoritmos e métodos que objetivam reconhecer objetos e textos em cenas. Entretanto, a detecção e reconhecimento de textos são problemas considerados em aberto devido a diversos fatores que atuam como fontes de variabilidades durante a geração e captura de textos em cenas, o que podem impactar as taxas de detecção e reconhecimento de maneira significativa. Exemplo destes fatores incluem diferentes formas dos elementos textuais (e.g., circular ou em linha curva), estilos e tamanhos da fonte, textura, cor, variação de brilho e contraste, entre outros. Além disso, os recentes métodos considerados estado-da-arte, baseados em aprendizagem profunda, demandam altos custos de processamento computacional, o que dificulta a utilização de tais métodos em cenários de computação restritiva. Esta dissertação apresenta um estudo comparativo de técnicas de detecção e reconhecimento de texto, considerando tanto os métodos baseados em aprendizado profundo quanto os métodos que utilizam algoritmos clássicos de aprendizado de máquina. Esta dissertação também apresenta um método de fusão de caixas delimitadoras, baseado em programação genética (GP), desenvolvido para atuar tanto como uma etapa de pós-processamento, posterior a etapa de detecção, quanto para explorar a complementariedade dos algoritmos de detecção de texto investigados nesta dissertação. De acordo com o estudo comparativo apresentado neste trabalho, os métodos baseados em aprendizagem profunda são mais eficazes e menos eficientes, em comparação com os métodos clássicos da literatura e considerando as métricas adotadas. Além disso, o algoritmo de fusão proposto foi capaz de aprender informações complementares entre os métodos investigados nesta dissertação, o que resultou em uma melhora das taxas de precisão e revocação. Os experimentos foram conduzidos considerando os problemas de detecção de textos horizontais, verticais e de orientação arbitráriaAbstract: Texts are fundamental elements for effective communication in our daily lives. The mobility of people and vehicles in urban environments and the search for a product of interest on a supermarket shelf are examples of activities in which the understanding of the textual elements present in the environment is essential to succeed in such tasks. Recently, several advances in computer vision have been reported in the literature, with the development of algorithms and methods that aim to recognize objects and texts in scenes. However, text detection and recognition are still open problems due to several factors that act as sources of variability during scene text generation and capture, which can significantly impact detection and recognition rates of current algorithms. Examples of these factors include different shapes of textual elements (e.g., circular or curved), font styles and sizes, texture, color, brightness and contrast variation, among others. Besides, recent state-of-the-art methods based on deep learning demand high computational processing costs, which difficult their use in restricted computing scenarios. This dissertation presents a comparative study of text detection and recognition techniques, considering methods based on deep learning and methods that use classical machine learning algorithms. This dissertation also presents an algorithm for fusing bounding boxes, based on genetic programming (GP), developed to act as a post-processing step for a single text detector and to explore the complementarity of text detection algorithms investigated in this dissertation. According to the comparative study presented in this work, the methods based on deep learning are more effective and less efficient, in comparison to classic methods for text detection investigated in this work, considering the adopted metrics. Furthermore, the proposed GP-based fusion algorithm was able to learn complementary information from the methods investigated in this dissertation, which resulted in an improvement of precision and recall rates. The experiments were conducted considering text detection problems involving horizontal, vertical and arbitrary orientationsMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

Neural models of inter-cortical networks in the primate visual system for navigation, attention, path perception, and static and kinetic figure-ground perception

Vision provides the primary means by which many animals distinguish foreground objects from their background and coordinate locomotion through complex environments. The present thesis focuses on mechanisms within the visual system that afford figure-ground segregation and self-motion perception. These processes are modeled as emergent outcomes of dynamical interactions among neural populations in several brain areas. This dissertation specifies and simulates how border-ownership signals emerge in cortex, and how the medial superior temporal area (MSTd) represents path of travel and heading, in the presence of independently moving objects (IMOs). Neurons in visual cortex that signal border-ownership, the perception that a border belongs to a figure and not its background, have been identified but the underlying mechanisms have been unclear. A model is presented that demonstrates that inter-areal interactions across model visual areas V1-V2-V4 afford border-ownership signals similar to those reported in electrophysiology for visual displays containing figures defined by luminance contrast. Competition between model neurons with different receptive field sizes is crucial for reconciling the occlusion of one object by another. The model is extended to determine border-ownership when object borders are kinetically-defined, and to detect the location and size of shapes, despite the curvature of their boundary contours. Navigation in the real world requires humans to travel along curved paths. Many perceptual models have been proposed that focus on heading, which specifies the direction of travel along straight paths, but not on path curvature. In primates, MSTd has been implicated in heading perception. A model of V1, medial temporal area (MT), and MSTd is developed herein that demonstrates how MSTd neurons can simultaneously encode path curvature and heading. Human judgments of heading are accurate in rigid environments, but are biased in the presence of IMOs. The model presented here explains the bias through recurrent connectivity in MSTd and avoids the use of differential motion detectors which, although used in existing models to discount the motion of an IMO relative to its background, is not biologically plausible. Reported modulation of the MSTd population due to attention is explained through competitive dynamics between subpopulations responding to bottom-up and top- down signals

Scene Image Classification and Retrieval

Scene image classification and retrieval not only have a great impact on scene image management, but also they can offer immeasurable assistance to other computer vision problems, such as image completion, human activity analysis, object recognition etc. Intuitively scene identification is correlated to recognition of objects or image regions, which prompts the notion to apply local features to scene categorization applications. Even though the adoption of local features in these tasks has yielded promising results, a global perception on scene images is also well-conditioned in cognitive science studies. Since the global description of a scene imposes less computational burden, it is favoured by some scholars despite its less discriminative capacity. Recent studies on global scene descriptors have even yielded classification performance that rivals results obtained by local approaches. The primary objective of this work is to tackle two of the limitations of existing global scene features: representation ineffectiveness and computational complexity. The thesis proposes two global scene features that seek to represent finer scene structures and reduce the dimensionality of feature vectors. Experimental results show that the proposed scene features exceed the performance of existing methods. The thesis is roughly divided into two parts. The first three chapters give an overview on the topic of scene image classification and retrieval methods, with a special attention to the most effective global scene features. In chapter 4, a novel scene descriptor, called ARP-GIST, is proposed and evaluated against the existing methods to show its ability to detect finer scene structures. In chapter 5, a low-dimensional scene feature, GIST-LBP, is proposed. In conjunction with a block ranking approach, the GIST-LBP feature is tested on a standard scene dataset to demonstrate its state-of-the-art performance

THE ROLE OF TEXTURE IN INDOOR SCENE RECOGNITION

Ph.DDOCTOR OF PHILOSOPH

Text Detection and Recognition in the Wild

Text detection and recognition (TDR) in highly structured environments with a clean background and consistent fonts (e.g., office documents, postal addresses and bank cheque) is a well understood problem (i.e., OCR), however this is not the case for unstructured environments. The main objective for scene text detection is to locate text within images captured in the wild. For scene text recognition, the techniques map each detected or cropped word image into string. Nowadays, convolutional neural networks (CNNs) and Recurrent Neural Networks (RNN) deep learning architectures dominate most of the recent state-of-the-art (SOTA) scene TDR methods. Most of the reported respective accuracies of current SOTA TDR methods are in the range of 80% to 90% on benchmark datasets with regular and clear text instances. However, those detecting and/or recognizing results drastically deteriorate 10% and 30% - in terms of F-measure detection and word recognition accuracy performances with irregular or occluded text images. Transformers and their variations are new deep learning architectures that mitigate the above-mentioned issues for CNN and RNN-based pipelines.Unlike Recurrent Neural Networks (RNNs), transformers are models that learn how to encode and decode data by looking not only backward but also forward in order to extract relevant information from a whole sequence. This thesis utilizes the transformer architecture to address the irregular (multi-oriented and arbitrarily shaped) and occluded text challenges in the wild images. Our main contributions are as follows: (1) We first targeted solving the irregular TDR in two separate architectures as follows: In Chapter 4, unlike the SOTA text detection frameworks that have complex pipelines and use many hand-designed components and post-processing stages, we design a conceptually more straightforward and trainable end-to-end architecture of transformer-based detector for multi-oriented scene text detection, which can directly predict the set of detections (i.e., text and box regions) of the input image. A central contribution to our work is introducing a loss function tailored to the rotated text detection problem that leverages a rotated version of a generalized intersection over union score to capture the rotated text instances adequately. In Chapter 5, we extend our previous architecture to arbitrary shaped scene text detection. We design a new text detection technique that aims to better infer n-vertices of a polygon or the degree of a Bezier curve to represent irregular-text instances. We also propose a loss function that combines a generalized-split-intersection-over union loss defined over the piece-wise polygons. In Chapter 6, we show that our transformer-based architecture without rectifying the input curved text instances is more suitable than SOTA RNN-based frameworks equipped with rectification modules for irregular text recognition in the wild images. Our main contribution to this chapter is leveraging a 2D Learnable Sinusoidal frequencies Positional Encoding (2LSPE) with a modified feed-forward neural network to better encode the 2D spatial dependencies of characters in the irregular text instances. (2) Since TDR tasks encounter the same challenging problems (e.g., irregular text, illumination variations, low-resolution text, etc.), we present a new transformer model that can detect and recognize individual characters of text instances in an end-to-end manner. Reading individual characters later makes a robust occlusion and arbitrarily shaped text spotting model without needing polygon annotation or multiple stages of detection and recognition modules used in SOTA text spotting architectures. In Chapter 7, unlike SOTA methods that combine two different pipelines of detection and recognition modules for a complete text reading, we utilize our text detection framework by leveraging a recent transformer-based technique, namely Deformable Patch-based Transformer (DPT), as a feature extracting backbone, to robustly read the class and box coordinates of irregular characters in the wild images. (3) Finally, we address the occlusion problem by using a multi-task end-to-end scene text spotting framework. In Chapter 8, we leverage a recent transformer-based framework in deep learning, namely Masked Auto Encoder (MAE), as a backbone for scene text recognition and end-to-end scene text spotting pipelines to overcome the partial occlusion limitation. We design a new multitask End-to-End transformer network that directly outputs characters, word instances, and their bounding box representations, saving the computational overhead as it eliminates multiple processing steps. The unified proposed framework can also detect and recognize arbitrarily shaped text instances without using polygon annotations

Towards music perception by redundancy reduction and unsupervised learning in probabilistic models

PhDThe study of music perception lies at the intersection of several disciplines: perceptual psychology and cognitive science, musicology, psychoacoustics, and acoustical signal processing amongst others. Developments in perceptual theory over the last fifty years have emphasised an approach based on Shannon’s information theory and its basis in probabilistic systems, and in particular, the idea that perceptual systems in animals develop through a process of unsupervised learning in response to natural sensory stimulation, whereby the emerging computational structures are well adapted to the statistical structure of natural scenes. In turn, these ideas are being applied to problems in music perception. This thesis is an investigation of the principle of redundancy reduction through unsupervised learning, as applied to representations of sound and music. In the first part, previous work is reviewed, drawing on literature from some of the fields mentioned above, and an argument presented in support of the idea that perception in general and music perception in particular can indeed be accommodated within a framework of unsupervised learning in probabilistic models. In the second part, two related methods are applied to two different low-level representations. Firstly, linear redundancy reduction (Independent Component Analysis) is applied to acoustic waveforms of speech and music. Secondly, the related method of sparse coding is applied to a spectral representation of polyphonic music, which proves to be enough both to recognise that the individual notes are the important structural elements, and to recover a rough transcription of the music. Finally, the concepts of distance and similarity are considered, drawing in ideas about noise, phase invariance, and topological maps. Some ecologically and information theoretically motivated distance measures are suggested, and put in to practice in a novel method, using multidimensional scaling (MDS), for visualising geometrically the dependency structure in a distributed representation.Engineering and Physical Science Research Counci

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

The Fifteenth Marcel Grossmann Meeting

The three volumes of the proceedings of MG15 give a broad view of all aspects of gravitational physics and astrophysics, from mathematical issues to recent observations and experiments. The scientific program of the meeting included 40 morning plenary talks over 6 days, 5 evening popular talks and nearly 100 parallel sessions on 71 topics spread over 4 afternoons. These proceedings are a representative sample of the very many oral and poster presentations made at the meeting.Part A contains plenary and review articles and the contributions from some parallel sessions, while Parts B and C consist of those from the remaining parallel sessions. The contents range from the mathematical foundations of classical and quantum gravitational theories including recent developments in string theory, to precision tests of general relativity including progress towards the detection of gravitational waves, and from supernova cosmology to relativistic astrophysics, including topics such as gamma ray bursts, black hole physics both in our galaxy and in active galactic nuclei in other galaxies, and neutron star, pulsar and white dwarf astrophysics. Parallel sessions touch on dark matter, neutrinos, X-ray sources, astrophysical black holes, neutron stars, white dwarfs, binary systems, radiative transfer, accretion disks, quasars, gamma ray bursts, supernovas, alternative gravitational theories, perturbations of collapsed objects, analog models, black hole thermodynamics, numerical relativity, gravitational lensing, large scale structure, observational cosmology, early universe models and cosmic microwave background anisotropies, inhomogeneous cosmology, inflation, global structure, singularities, chaos, Einstein-Maxwell systems, wormholes, exact solutions of Einstein's equations, gravitational waves, gravitational wave detectors and data analysis, precision gravitational measurements, quantum gravity and loop quantum gravity, quantum cosmology, strings and branes, self-gravitating systems, gamma ray astronomy, cosmic rays and the history of general relativity