Model synthesis

Three-dimensional models are extensively used in nearly all types of computer graphics applications. The demand for 3D models is large and growing. However, despite extensive work in modeling for over four decades, model generation remains a labor-intensive and difficult process even with the best available tools. We present a new procedural modeling technique called model synthesis that is designed to generate many classes of objects. Model synthesis is inspired by developments in texture synthesis. Model synthesis is designed to automatically generate a large model that resembles a small example model provided by the user. Every small part of the generated model is identical to a small part of the example model. By altering the example model, a wide variety of objects can be produced. We present several different model synthesis algorithms and analyze their strengths and weaknesses. Discrete model synthesis generates models built out of small building blocks or model pieces. Continuous model synthesis generates models on set of parallel planes. We also show how to incorporate several additional user-defined constraints to control the large-scale structure of the model, to control how the objects are distributed, and to generate symmetric models. The generality of the approach will be demonstrated by showing many models produced using each approach including cities, landscapes, spaceships, and castles. The models contain hundreds of thousands of model pieces and are generated in only a few minutes

An Approach to Pattern Recognition by Evolutionary Computation

Evolutionary Computation has been inspired by the natural phenomena of evolution. It provides a quite general heuristic, exploiting few basic concepts: reproduction of individuals, variation phenomena that affect the likelihood of survival of individuals, inheritance of parents features by offspring. EC has been widely used in the last years to effectively solve hard, non linear and very complex problems. Among the others, EC–based algorithms have also been used to tackle classification problems. Classification is a process according to which an object is attributed to one of a finite set of classes or, in other words, it is recognized as belonging to a set of equal or similar entities, identified by a label. Most likely, the main aspect of classification concerns the generation of prototypes to be used to recognize unknown patterns. The role of prototypes is that of representing patterns belonging to the different classes defined within a given problem. For most of the problems of practical interest, the generation of such prototypes is a very hard problem, since a prototype must be able to represent patterns belonging to the same class, which may be significantly dissimilar each other. They must also be able to discriminate patterns belonging to classes different from the one that they represent. Moreover, a prototype should contain the minimum amount of information required to satisfy the requirements just mentioned. The research presented in this thesis, has led to the definition of an EC–based framework to be used for prototype generation. The defined framework does not provide for the use of any particular kind of prototypes. In fact, it can generate any kind of prototype once an encoding scheme for the used prototypes has been defined. The generality of the framework can be exploited to develop many applications. The framework has been employed to implement two specific applications for prototype generation. The developed applications have been tested on several data sets and the results compared with those obtained by other approaches previously presented in the literature

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presentedSiirretty Doriast

Scene Based Text Recognition From Natural Images and Classification Based on Hybrid CNN Models with Performance Evaluation

Similar to the recognition of captions, pictures, or overlapped text that typically appears horizontally, multi-oriented text recognition in video frames is challenging since it has high contrast related to its background. Multi-oriented form of text normally denotes scene text which makes text recognition further stimulating and remarkable owing to the disparaging features of scene text. Hence, predictable text detection approaches might not give virtuous outcomes for multi-oriented scene text detection. Text detection from any such natural image has been challenging since earlier times, and significant enhancement has been made recently to execute this task. While coming to blurred, low-resolution, and small-sized images, most of the previous research conducted doesn’t work well; hence, there is a research gap in that area. Scene-based text detection is a key area due to its adverse applications. One such primary reason for the failure of earlier methods is that the existing methods could not generate precise alignments across feature areas and targets for those images. This research focuses on scene-based text detection with the aid of YOLO based object detector and a CNN-based classification approach. The experiments were conducted in MATLAB 2019A, and the packages used were RESNET50, INCEPTIONRESNETV2, and DENSENET201. The efficiency of the proposed methodology - Hybrid resnet -YOLO procured maximum accuracy of 91%, Hybrid inceptionresnetv2 -YOLO of 81.2%, and Hybrid densenet201 -YOLO of 83.1% and was verified by comparing it with the existing research works Resnet50 of 76.9%, ResNet-101 of 79.5%, and ResNet-152 of 82%

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field